diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb new file mode 100644 index 0000000..3cb2d2c --- /dev/null +++ b/feature_crosses.ipynb @@ -0,0 +1,1652 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_crosses.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ZTDHHM61NPTw", + "0i7vGo9PTaZl" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Feature Crosses" + ] + }, + { + "metadata": { + "id": "F7dke6skIK-k", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Improve a linear regression model with the addition of additional synthetic features (this is a continuation of the previous exercise)\n", + " * Use an input function to convert pandas `DataFrame` objects to `Tensors` and invoke the input function in `fit()` and `predict()` operations\n", + " * Use the FTRL optimization algorithm for model training\n", + " * Create new synthetic features through one-hot encoding, binning, and feature crosses" + ] + }, + { + "metadata": { + "id": "NS_fcQRd8B97", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "4IdzD8IdIK-l", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First, as we've done in previous exercises, let's define the input and create the data-loading code." + ] + }, + { + "metadata": { + "id": "CsfdiLiDIK-n", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "10rhoflKIK-s", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ufplEkjN8KUp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "2f21eb21-ed26-46de-a01d-4c74d314c859" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2667.0 542.9 \n", + "std 2.1 2.0 12.5 2239.1 429.4 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1465.0 297.0 \n", + "50% 34.2 -118.5 29.0 2137.0 435.0 \n", + "75% 37.7 -118.0 37.0 3161.2 651.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1440.8 504.5 3.9 2.0 \n", + "std 1187.3 392.0 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 792.0 282.0 2.6 1.5 \n", + "50% 1168.0 409.0 3.6 1.9 \n", + "75% 1730.0 609.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "oJlrB4rJ_2Ma", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "NBxoAfp2AcB6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hweDyy31LBsV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## FTRL Optimization Algorithm\n", + "\n", + "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)." + ] + }, + { + "metadata": { + "id": "S0SBf1X1IK_O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1Cdr02tLIK_Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + }, + "outputId": "5496fcc1-803f-4f82-b422-b253761fa693" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 298.52\n", + " period 01 : 197.95\n", + " period 02 : 149.66\n", + " period 03 : 196.02\n", + " period 04 : 190.10\n", + " period 05 : 166.59\n", + " period 06 : 174.26\n", + " period 07 : 167.08\n", + " period 08 : 160.77\n", + " period 09 : 131.58\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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jgwcPZtGiRUyfPh2Xy8V1111Hu3btPFXWKbP5BtAjNJ6Mwi0UVhYRZg3FYjbT\np2s4P23az25nGZ0ig7xdpoiIyBnBZLTCrgNPdrs11K337a4feDdzEdN7XMSITkMB+GH9Hl75ZBNT\nR8UzYUiMx+oSdbm2VGqXlktt03KpbRrHK0NIbdHhzR0PL6fuG+fAhHanFhERaU4KMCchzBpKZ3tH\nMguzqKitACDY5kfXDsFs3VVMWWWNlysUERE5MyjAnKSkiARchouN+ZsPH4t3UGcYbNhW4MXKRERE\nzhwKMCfJfVfeI4aRDu1OrbvyioiINA8FmJPUMag94dYwNuRn4KqrvwNvl3Z2ggP9SM/Op671zYkW\nERFpdRRgTtKhzR0raivZUpQNgNlkIinOQUl5DTv2ala5iIiIpynAnILDw0iHb2p3aBhp3VanV2oS\nERE5kyjAnIJuoV0J8AkgLW+j+w68CbHhWMwm0rM1D0ZERMTTFGBOgcVsoa+jF4VVRew6kAuAzepD\n904hbNtTSnFZtZcrFBERadsUYE5RUuTBYaS8w8NIiQeHkdarF0ZERMSjFGBOUUJ4D3xMll8sp44A\ndFdeERERT1OAOUVWHys9wrqx60Au+RWFAHRw2HAEW1m/rYBaV52XKxQREWm7FGBOQ1Lkob2R6oeR\nTCYTSd0cVFTVkrW72JuliYiItGkKMKch8TibOybF1c+DSdM8GBEREY9RgDkNof4hxNg7s7Uom/Ka\ncgB6xYTh62PWPBgREREPUoA5TUmRCdQZdazPzwDA39dCry5h7M4rI7+40svViYiItE0KMKepoc0d\nNYwkIiLiGQowp6l9YDsirOFszM+gpq4WOHw/GO1OLSIi4hkKMKfJZDKRFNmHKlc1mYVZAESFBtDe\nYWPjjgJqal1erlBERKTtUYBpAkkRRy+nBkiMc1BdU8fmnUXeKktERKTNUoBpAnEhsQT62EjP20id\nUX8Du36H5sFoGElERKTJKcA0AYvZQt+I3hRXl5BTuhuA7p1DsfpZSMvKd+9YLSIiIk1DAaaJuIeR\nDm7u6GMx0yc2nP1FFewrrPBmaSIiIm2OAkwT6RXeAx+zz1HLqRM1jCQiIuIRCjBNxOrjT6+wbuSW\n7cVZUR9YEg9tK5Dl9GZpIiIibY4CTBNy39Tu4DBSmN2fLu2C2LyziMrqWm+WJiIi0qYowDShvhEJ\nmDAdc1deV53Bxu2FXqxMRESkbVGAaUIh/nZigzuztWgbB2rKAEiKjwA0D0ZERKQpKcA0saSIPhgY\nbHDWb+4Y1z6YQKsP6dlaTi0iItJUFGCaWFLk0XflNZtNJMY5KCytImf/AW+WJiIi0mYowDSxdrYo\nogIi2FiQSY2rBji8O3W6dqdBOgThAAAgAElEQVQWERFpEgowTcxkMpEYmUC1q5rNhVsB6BvnwGSC\ndZoHIyIi0iQUYDzAvZz64DBSUIAv8R1CyNpdzIGKGm+WJiIi0iYowHhAXEgMQb6BpDs3uTd3TIx3\nYBiwYVuBl6sTERFp/RRgPMBsMpMYkUBJdSk7SnIASNJdeUVERJqMjydPPmfOHNasWUNtbS3XX389\nS5cuZcOGDYSGhgJwzTXXMGrUKD766CNef/11zGYz06ZN49JLL/VkWc0iKSKBH/f8TJpzI11DYujS\nLoiQID/SswuoqzMwm03eLlFERKTV8liAWblyJVu2bGHBggUUFhYyZcoUhgwZwp133sno0aPdrysv\nL+e5555j4cKF+Pr6MnXqVM477zx3yGmteoV3x9fsS1reBi6MPx+TyURSnIPlaXvYtqeE+I4h3i5R\nRESk1fLYEFJycjLPPPMMAMHBwVRUVOByuY553bp160hMTMRut2O1Whk4cCCpqameKqvZ+Fn86B3e\ng73l+9lfngforrwiIiJNxWMBxmKxYLPZAFi4cCEjRozAYrHw5ptvcuWVV3LHHXdQUFCA0+kkPDzc\n/b7w8HDy8vI8VVazSoo4dFO7+r2REmLDsJhNpOl+MCIiIqfFo3NgAJYsWcLChQt59dVXWb9+PaGh\nofTu3ZuXX36ZZ599lgEDBhz1+sbcbj8szIaPj8VTJRMZaW+S84yyJ/PW5oVsKsrgskGTAOgb72Dd\nFicWf1/Cg61Ncp0zSVO1jTQttUvLpbZpudQ2p8ejAWb58uW8+OKLvPLKK9jtds4++2z3c2PGjOGh\nhx5i/PjxOJ2HV+bs37+f/v37N3jewsJyj9UcGWknL6+0yc4XFxzDZmc22bv3YPcLolfnUNZtcbLs\n5x0MT+rQZNc5EzR120jTULu0XGqblktt0zgNhTyPDSGVlpYyZ84cXnrpJfeE3FtuuYWcnPplxatW\nraJ79+7069eP9PR0SkpKKCsrIzU1lcGDB3uqrGaXFFm/ueN656b6x/GHllNrGElERORUeawHZvHi\nxRQWFnL77be7j1188cXcfvvtBAQEYLPZeOyxx7BarcyePZtrrrkGk8nETTfdhN3edrrVkiIS+GDr\np6Q5N3J2h2Siw21EhlrZsK2AWlcdPhbdikdERORkeSzATJ8+nenTpx9zfMqUKcccS0lJISUlxVOl\neFWULZJoWxSbCjKpdlXjZ/EjKS6Cr1N3sXVXMb1iwrxdooiISKujP/+bQVJkH2rqasgo2FL/uJuG\nkURERE6HAkwz+OVy6p6dQ/HzMWs5tYiIyClSgGkGMcGdCfazk+7cSJ1Rh5+vhV4xYeQ6y3AWVXi7\nPBERkVZHAaYZ1G/u2JsDNWVsK94JQL9Dq5HUCyMiInLSFGCaSVJEHwDSnBsASNRyahERkVOmANNM\neoZ1w8/iR1reBgzDICIkgI4RgWTsKKS65tg9okREROTEFGCaia/Fl4TwHuyvcLKvfD9Q3wtTXVtH\nxs4iL1cnIiLSuijANCP3MFJe/WqkpLhDw0jOE75HREREjqUA04z6RPTCbDK758F06xRCgL+FtKz8\nRm1iKSIiIvUUYJpRkG8g8SGxbC/JobiqFB+LmT6x4TiLK9lb4LkNKkVERNoaBZhmlhSRcHBzx4PD\nSPERAKzbqtVIIiIijaUA08ySIn+xnDouHIB03Q9GRESk0RRgmllEgIMOgdFkFG6lsraKkCB/YqLt\nZOYUUVFV6+3yREREWgUFGC9Iikigtq6WjIJMoP6uvK46g43bC7xcmYiISOugAOMFh4eR6ufB6K68\nIiIiJ0cBxgs62zsS4hfMeucmXHUuurYPxm7zJS1by6lFREQaQwHGC8wmM4mRCZTVlpNdvB2zyUTf\nrg6KD1Szc98Bb5cnIiLS4inAeMnhzR0PLafWXXlFREQaSwHGS3qExWO1+Ls3d+wbF47JBGlaTi0i\nIvKrFGC8xNfsQ29HT5yVBewp20eg1ZduHUPI3l1CaXm1t8sTERFp0RRgvCgpIgE4fFO7pHgHBrB+\nm5ZTi4iINEQBxov6Og5u7ph39LYC6VpOLSIi0iAFGC+y+droHhrHjtIciqqK6RQZSJjdn/TsfOrq\ntJxaRETkRBRgvOzQaqR050ZMJhNJ8Q7KKmvJzi3xcmUiIiItlwKMlyUemgdzaBgp7uBy6mwtpxYR\nETkRBRgvcwSE0SmoA5mFW6msraR3bBg+FhNpWzUPRkRE5EQUYFqApIgEag0XGwsysfr50LNzKDv3\nH6CwtMrbpYmIiLRICjAtgHtzx7z65dSJh1Yj6aZ2IiIix6UA0wJ0CupAmH8o6/MzcNW56KfdqUVE\nRBqkANMCmEwmkiITqKitYGvRNtqF24gKC2DD9gJqXXXeLk9ERKTFUYBpIQ5v7nj4rrxV1S4yc4q8\nWZaIiEiLpADTQnQPjSPAx0qacyOGYRyxO7WGkURERH5JAaaFsJgt9HH0oqCykN0H9tCzcyh+vmYF\nGBERkeNQgGlBjtzc0dfHQkJMOHsLytlfWO7lykRERFoWBZgWJMHRC4vJQprz0OaO9cNI6dnanVpE\nRORIPp48+Zw5c1izZg21tbVcf/31JCYmct9991FbW4uPjw9PPvkkkZGR9OnTh4EDB7rfN2/ePCwW\niydLa5ECfKz0CItnU0EmhZVF7gCzLsvJ2EGdvFydiIhIy+GxALNy5Uq2bNnCggULKCwsZMqUKZx1\n1llMmzaNCRMm8NZbb/Haa69x9913ExQUxPz58z1VSquSFJHApoJM0pwbGdlpKJ0iA8nYUURVjQt/\n3zMv1ImIiByPx4aQkpOTeeaZZwAIDg6moqKCP/3pT4wfPx6AsLAwioq0RPiXDm/ueOiuvA5qXXVs\n2lHozbJERERaFI8FGIvFgs1mA2DhwoWMGDECm82GxWLB5XLx9ttvM3nyZACqq6uZPXs2M2bM4LXX\nXvNUSa1CmDWULvaObCnKpqK2gn6HthXQaiQRERE3j86BAViyZAkLFy7k1VdfBcDlcnH33XczZMgQ\nzj77bADuvvtuLrjgAkwmE1dccQWDBw8mMTHxhOcMC7Ph4+O54ZTISLvHzt0YQ2IG8u76j8mp2cGQ\nfoMIfD+dDdsLiIgIwmQyebU2b/N228jxqV1aLrVNy6W2OT0eDTDLly/nxRdf5JVXXsFur2+o++67\nj5iYGG6++Wb36y677DL310OGDCEzM7PBAFPowWXFkZF28vJKPXb+xuhm6wbAiqw19AjoRZ/YMH7a\ntJ91m/bSMTLIq7V5U0toGzmW2qXlUtu0XGqbxmko5HlsCKm0tJQ5c+bw0ksvERoaCsBHH32Er68v\nt956q/t12dnZzJ49G8MwqK2tJTU1le7du3uqrFahQ2A0DmsYG/I3U1tXS2LcwbvyandqERERwIM9\nMIsXL6awsJDbb7/dfSw3N5fg4GBmzZoFQHx8PA899BDR0dFMnToVs9nMmDFjSEpK8lRZrYLJZCIp\nog/f7FrBlsJsEuNiMQFpW/M5/6wYb5cnIiLidaccYLZv305sbOwJn58+fTrTp09v1LnuuuuuUy2j\nzUqKTOCbXStIc26gd88exLYPZsuuYsora7FZPT51SUREpEVrcAjp6quvPurx888/7/76wQcf9ExF\nAkB8SFdsPgFHbe5YZxhs3K678oqIiDQYYGpra496vHLlSvfXhmF4piIBDm3u2JuiqmJySncfdVde\nERGRM12DAeaXS3aPDC1n+nLe5pAUeXhzx5hoO8GBfqRnF1Cn8CgiIme4k1qFpNDSvBLCe+BzcHNH\ns8lEYlw4JWXV7NirpXciInJma3A2aHFxMT/++KP7cUlJCStXrsQwDEpKSjxe3JnO6mOlR3g3NuZv\nxllRQFJ8BN+n7yU9K5+u7YO9XZ6IiIjXNBhggoODj5q4a7fbee6559xfi+clRfRhY/5m0p0bOSv2\nLMwmE2nZ+VxwTldvlyYiIuI1DQYY7RDtfYkRvXlnc/3mjqM7n0P3TiFk5hRRUl5NsM3P2+WJiIh4\nRYNzYA4cOMC8efPcj9955x0uvPBCbr31VpxOrYZpDqH+IcQEd2Zr8TbKaspJindgAOt1V14RETmD\nNRhgHnzwQfLz6/+h3LZtG08//TT33HMPQ4cO5a9//WuzFCj1w0h1Rh0b8jPcy6nTtDu1iIicwRoM\nMDk5OcyePRuAL774gpSUFIYOHcqMGTPUA9OMkiIOLqfO20CHiEAcwf6szy7AVVfn5cpERES8o8EA\nY7PZ3F//9NNPDBkyxP1YS6qbT/vAdkQEONhYsJlaw0VifATlVbVk7dZKMBEROTM1GGBcLhf5+fns\n3LmTtWvXMmzYMADKysqoqKholgLl0OaOCVS5qsks3KphJBEROeM1GGCuvfZaJkyYwOTJk7nxxhsJ\nCQmhsrKSmTNnctFFFzVXjQL0i+wL1A8j9e4Sho/FrAAjIiJnrAaXUY8cOZIVK1ZQVVVFUFAQAFar\nlbvuuotzzjmnWQqUenEhMQT5BpLu3Mj0nlPoFRPK+uwCCkoqCQ+2ers8ERGRZtVgD0xubi55eXmU\nlJSQm5vr/i8uLo7c3NzmqlEAs8lMX0dviqtL2Vm6i6S4g8NIWk4tIiJnoAZ7YMaMGUPXrl2JjIwE\njt3M8Y033vBsdXKUpMgEVu5dTVreRobEj+DtJVtIz8pnVP+O3i5NRESkWTUYYJ544gk+/PBDysrK\nmDhxIpMmTSI8PLy5apNf6BXeA1+zD2nODVwQn0J0uI2N2wupqa3D1+ek9uUUERFp1Rr8V+/CCy/k\n1Vdf5R//+AcHDhzg8ssv5/e//z0ff/wxlZWVzVWjHORv8aNXeHf2lO0jrzyfpHgHVTUuMnOKvF2a\niIhIs2rUn+3t27fnxhtv5LPPPmP8+PH85S9/0SReL0mK6ANAmnODezn1uizdVFBERM4sDQ4hHVJS\nUsJHH33E+++/j8vl4vrrr2fSpEmerk2Oo29Eb0yYSHNuYETSOfj7WUjPyodzvV2ZiIhI82kwwKxY\nsYL//ve/rF+/nnHjxvH444/To0eP5qpNjiPYz07XkC5kFW2nqq6ChJgw1m5xsq+gnHbhtl8/gYiI\nSBvQYID5/e9/T2xsLAMHDqSgoIDXXnvtqOcfe+wxjxYnx5cU0Yfs4h2sz99Ev24dWLvFSVpWPucp\nwIiIyBmiwQBzaJl0YWEhYWFhRz23a9cuz1UlDUqKSGBR1mLSnBu5NC4RqL8fzHnJnb1cmYiISPNo\nMMCYzWbuuOMOqqqqCA8P56WXXiImJoY333yTl19+mYsvvri56pQjtAuMop0tkk35mwlMMNMlKojN\nOwuprK7F6teoaU0iIiKtWoP/2v39739n3rx5xMfH8/XXX/Pggw9SV1dHSEgI7733XnPVKMeRFNGH\nr3YuY3PhFhLjHezcf4BNOwoZ0D3S26WJiIh4XIPLqM1mM/Hx8QCMHTuW3bt3c+WVV/Lss8/Srl27\nZilQji8pMgGAtLyN7uXU6drcUUREzhANBhiTyXTU4/bt23Peeed5tCBpnNjgLth9g0jP30jX9nYC\nrT6sy8o/arsHERGRtuqk7j//y0Aj3mM2mUmM6E1p9QF2HthF3zgHhaVV7M4r83ZpIiIiHtfgHJi1\na9cyatQo9+P8/HxGjRqFYRiYTCaWLVvm4fKkIUmRffhhz8+k5W0gKX4gqzbuY12Wk05RQd4uTURE\nxKMaDDCff/55c9Uhp6BnWHf8zL6kOTdyR9K5mKifBzPx7FhvlyYiIuJRDQaYjh07Nlcdcgr8LL70\nDu/BOucGyikmrkMwW3eXUFZZQ6DV19vliYiIeMxJzYGRlicx8uDmjnn1mzvWGQYbthV4uSoRERHP\nUoBp5RIdhzZ33EhSfAQAaVpOLSIibZwCTCsX5BdIXEgs24p3EBYGIUF+pGfnU6fl1CIi0oYpwLQB\nSZEJGBisz99EYpyD0vIatu8p9XZZIiIiHuPRADNnzhymT5/OJZdcwpdffsmePXuYNWsWM2fO5Lbb\nbqO6uhqAjz76iEsuuYRLL71UWxScgqSIg/NgnBtIiqu/K29altObJYmIiHiUxwLMypUr2bJlCwsW\nLOCVV17h0UcfZe7cucycOZO3336bmJgYFi5cSHl5Oc899xzz5s1j/vz5vP766xQVFXmqrDYpyhZB\ndGA7Mgq20K1LEBazSfNgRESkTfNYgElOTuaZZ54BIDg4mIqKClatWsXYsWMBGD16ND/++CPr1q0j\nMTERu92O1Wpl4MCBpKameqqsNispIoGaulp2lGXTvVMI2/eWUlxW7e2yREREPKLB+8CcDovFgs1m\nA2DhwoWMGDGCFStW4OfnB4DD4SAvLw+n00l4eLj7feHh4eTl5TV47rAwGz4+Fk+VTmSk3WPn9pSR\n5mS+3PENmaVbGNpvKBk7i9i+v4xzf+PwdmlNqjW2zZlA7dJyqW1aLrXN6fFYgDlkyZIlLFy4kFdf\nfZVx48a5j59o08HGbEZYWFjeZPX9UmSknby81jcBNtgIJ8TPzurdadzS61wAvl+3m35dw7xcWdNp\nrW3T1qldWi61TcultmmchkKeRyfxLl++nBdffJF//etf2O12bDYblZWVAOzbt4+oqCiioqJwOg9P\nON2/fz9RUVGeLKtNMpvM9I1I4EBNGRU+eUSEWNmwrYBaV523SxMREWlyHgswpaWlzJkzh5deeonQ\n0FAAhg4dyhdffAHAl19+yfDhw+nXrx/p6emUlJRQVlZGamoqgwcP9lRZbVpSRAIA6c6NJMU7qKiq\nJTNHE6JFRKTt8dgQ0uLFiyksLOT22293H3v88cd54IEHWLBgAR06dOCiiy7C19eX2bNnc80112Ay\nmbjpppuw2zUueCp6hnXDz+JHmnMDM3sPY2nqbv77bTa9YsIwm0zeLk9ERKTJmIzGTDppYTw5btja\nxyX/lT6f/+Wl88BZs/lwSR4/bdrPlSk9GdW/9W/M2drbpq1Su7RcapuWS23TOF6bAyPN79AwUlre\nBqaP6Y7Vz8J/l2VRWq4l1S3Fnvwyfli/h137D+Cq0xwlEZFT4fFVSNK8+kb0xmwyk+bcyPjYMVx0\nTlfeWbqVhcuyuHpCb2+Xd8arqKrlb+/8j8LSKgD8fMx0aWcnNtpObHs7MdHBtA+3YTZryE9EpCEK\nMG1MoK+NbiFdySzKoriqhLGDO7EifQ/L0/Ywol8H4juGeLvEM9r732VTWFrFb3pH4edrYfueUrJz\nS9i6u9j9Gn9fCzHtgohtH0xstJ2YaDvtwm2axyQicgQFmDYoKbIPmUVZpDs3ck7HIVwxriePv5XK\n/C838+BVyfrr3kuyc0tYumYX0eE2rpmYgK9P/QhudY2LnP0H2L63lO17Sti+t5Qtu4vJ3HU41Fj9\nLPW9NNHBB3tq7ESFBmBSqBGRM5QCTBuUFJHAwi0fkXYwwPToHMqwvtF8v34v36zdzdhBnbxd4hmn\n1lXH659nYABXpfR0hxcAP18L8R1Djuodq6p2sXN/Kdv3lLJ9b32o2byziIydh5fF2/x9iDk49NQ1\nOpiYaDsRIVaFGhE5IyjAtEGOgHA6BrVnc+FWKmursPr4c+nobqzd4uT977IZ3CuKkEA/b5d5Rvnq\n5xxy9h9geFJ7enb59bsj+/tZ6N4plO6dQt3HKqpq2bmvtL6n5mBvzaYdhWzaUeh+TaDVxz30FBtd\n///wYH+FGhFpcxRg2qikiAQ+2/41a/b/j2EdziI40I+LR8bx5peZvLt0K9dOTvB2iWeM/UUVfLhi\nG8E2Xy4d3e2UzxPg70PPLmFHBaDyyhp27DtQ30tzsLdmw7YCNmwrcL/GbvN1h5nY9vXBJszuf1qf\nSUTE2xRg2qihHX7D1znLWbR1MX0dCYT42xnVvyPL1+3hxw17GdGvcT0BcnoMw2D+F5uprq3jtxN6\nERTg26Tnt1l96R0TRu+Yw215oKKGHfsOz6fZvqeU9Ox80rPz3a8JCfQ7GGiC3f9Xr5yItCYKMG1U\nuDWMC+PP573MD3k3cxHXJs7CbDZxxfgePPrGGt78MpM/XZ2Mj0W3AvKkVRv3sWFbAX27hnNW73bN\ncs2gAF/6xIbTJ/bwLu+l5dXs2FvKtiMmCq/Lymdd1uFQE2b3Pzj0VB9oYqLtBNsUakSkZVKAacNG\ndDyb1H3r+F9eOqn70xgYlUR8hxCG9+vAd+tyWbJ6FylndfF2mW3WgYoa/vP1Fvx8zMwa39M9D6Wi\nthJnRQEOaxg234BmqcVu86NvnIO+cQ73seKyana4h55K2ba3hLVbnKzdcnhzVUew9aihp5hoe5P3\nIomInAoFmDbMbDJzee9Leeynv/Pu5kX0CIsnyDeQqaPiSc3M48Pvt3FWQjvNh/CQd7/ZSml5DZeO\njicytD6o1NbV8kzqi+QcyAXA5hOAIyCcCGs4EQGO+q8DwomwOgi3hmIxWzxWX0igH0nxESTFR7iP\nFZZWsWPv4ZVP2/eUsCYzjzWZee7XRIZaiYkOpuvB3hpbkNVjNYqInIgCTBvXzhbJxK7jWJS1mP9u\n+ZirEmYQFODL1FHxzPssg3e+3sINF/X1dpltTsaOQlak7aFzVBDnDe7sPr542xJyDuQSFxKLzceK\ns6KAvWX7yCndfcw5TJgIs4YeDDfhOAIcRFjD6v8fEE6Qb2CTry4Ks/sTZvenf/f6UGMYBoWlVQdX\nPh3urVmdsZ/VGfsBsJjXccnIeMb/prNWO4lIs1GAOQOM6Tyc1P1p/LQ3lUFR/egb0ZtzktqzfF0u\nP2fsZ8T2gqPmS8jpqal18foXmzEBvz2/l3ueUXbxdr7c8Q0Oazg39fsdVp/6nos6o47S6gM4Kwpw\nVuTjrCwgv6IAZ0UB+ZUFZBZlkVmUdcx1/C1+9b027oBzRE+ONQxfy+kP9ZhMJsKDrYQHWxnYIxKo\nDzX5xZXu5dwrN+7l3W+2smNfKb89vxf+vp7rNRIROUS7Uf9CW90hdPeBPTzx81zsfkE8cNadBPgE\nsGNvKQ+//jNRYTYe/t1vjrq5WkvUWtpm0fJsPvp+O+cO6sTM83oAUFlbxWM//4P8igJuH/h/dAvt\n2ujz1bhqyK8sJL+ywB1y8isKcFbWf13lOv5GnSF+wfXDUYeGpqzh7iGqYD87ZlPTtLfF35eHX/mR\nrN0ldIkK4uZLEokIaZ65PdKw1vI7cyZS2zROQ7tRqwfmDNExqD3jY8eweNtXfLB1MTN7XUJMtJ0x\nAzrxdeouvvhpJ5OGxnq7zFYv11nGpz/uIMzuz5QRce7ji7IW46zI59wuI08qvAD4WnyJDowiOjDq\nmOcMw6CsphxnZf7BcFNAfkU+zspC8ivyyS7eQVbx9mPPafbBcUSgqQ839UNTDms4Vp/Gz4sKD7Zy\n92UDeeurTL5bl8vD81Zzw0V9j1raLSLS1BRgziDjY0bzv/3pfJ+7ikFR/egZ3o0pI7ry8+b9fPLD\ndob0aae/nE9DnWHw+ucZuOoMrhjXgwD/+l+vjfmbWb77RzoERjMpbnyTXtNkMhHkF0iQXyCxwceu\nKHPVuSioLMJZme8elqofoqoPPHvL9x/3vEG+gUQcDDRH9tw4rA7CrCHH9N74+pj57fm9iI2289ZX\nmTz1zv+YPrYb5w7qpHkxIuIRCjBnEB+zD1f0vpQnVz/LWxkLuf+sO7FZ/Zg2Op5XPtnEf5Zs4ZZL\nkrxdZqu1fF0uW3YVM6hHJAO6188XKasp581N72IxWbgyYQa+5ub9lbOYLUTaHETaHMd9vrym4qih\nqUPzb/IrCsgp3c32kp3HntNkIdwa6p57062wC4n2JKw+/owa0JEOEYE8v2g9/1myhZ17S7kypSe+\nPpoXIyJNSwHmDBMT3Jlzu4zkq53L+Djrc6b2uICz+0Tz3f9yWbvFSVqW86hltdI4xQeqePebLAL8\nLe55LwALNn9AcXUpF8Sl0NnewYsVHp/NNwCbb0c62zse81ydUUdRVfHBYanDc24O9eRkFG6BQliR\nu4rowO+4ru8s2gVG0aNzKA9eNZhn30/n+/V7yc0v46YpiYQHa7m1iDQdy0MPPfSQt4s4WeXlx5+0\n2BQCA/09ev6WIC4klrV5aWzM30yv8O6EW8OIjQ7m2//lsnV3MSP7dcDSAu/Q25LbZt5nGezYW8qM\nsd1JOLiia/W+/7F4+xK6Bsdwea+pTTZptrmYTCYCDt6nprO9Iz3DujEgKpGhHX7DuTEjGdt5BMnt\nBhBo8+d/ezewau8a2h2cqxPg78PQvtEUllSRll3Ayg17iesQgiNEIaY5teTfmTOd2qZxAgNPPB9P\nAeYXzoQfKovZQuegjqzcs5qs4u0Mbf8bwuwBlFfVkp5dgNlsolcLnIDZUtsmLcvJf7/NJr5jsPuO\nu0VVxbyw7jVMmLi5/++x+wV5u8wm52P2we4XxDndBhJoBJPm3MDP+9ZSW1dL99A4fCwWBnSPICjA\nl9RMJz+s34vdVr8Hk+bFNI+W+jsjapvGaijAtK4/CaXJxIfGMqLTUPaV57F4+xIALjynK6FBfixe\nuZP9heVerrB1qKp2Mf+LTCxmE1el9MJsMmEYBm9ueo/y2gou7j6JKFvbH5JLjh7AHwbfTGSAgy93\nfMNz6/5NafUBTCYT5w7uzB9m9CfA34f5X2zm9c83U1Nb5+2SRaSVU4A5g10Ql4LDGsaSnd+ys3QX\nAf4+zBjbnVpXHW99tYVWeIugZrdoRTb5JZWknNWFTpH1vSwrcleyqSCT3uE9OKfDEC9X2Hw6BrXn\n7sG3khjRm82FW3ni57nuScC9YsJ48LeD6dIuiO/W5TLnP6kUHajycsUi0popwJzBrD7+zOw1lTqj\njjc3vUdtXS3JvaJIiA0jPTuf1Eznr5/kDLZjbylf/pxDVGgAkw/eQ2d/uZP3t3yCzSeAK3pfesYN\nldh8A7gu8Somx42nqKqYv695gRW7V2IYBhEhAdx3xSDOSmhH1u4S/jzvZ7J2F3u7ZBFppRRgznC9\nwrsztP1v2H1gD1/tWPnbujEAACAASURBVIbJZOLy83pgMZv4z9eZVFW7vF1ii+Sqq2Pe5xkYBlyZ\n0hM/Xwt1Rh1vbFxAdV0N03tOIdQ/xNtleoXZZCYldiw39bsGf4s//9n8Pm9mvEe1qwZ/XwvXTU5g\n2uhulJRV88TbqSxfl+vtkkWkFVKAES7uPpEQv2A+2/41uQf20t4RSMpZXSgoqeLjH7Z7u7wW6es1\nu9mxt5ShfaPdq46+2rGMbSU7GBTVj8Ht+nu5Qu/r7ejBPcm30sVeP2H86dTnya8owGT6/+3dd3iU\ndbr/8fe0tEmbmTRI7w0ChCIgNoosoqC0KBDU3dVV13Ute1x119Vz3HLw/CzHcnbXjsGCoNgFG1jp\nPaQX0kPapNcpvz8SB6KAlExmJtyv6+LiYsoz9/CdJ/PJ97mf76PgFxdEcPey8bhrVLz8SR5rP83H\nZJa+GCHE6ZMAI/BUe3Jd0iLMVjNr89ZjsVq4cloUBl93Nu8sp6axw9ElOpXGlm42fl2Ct6eGjJlx\nAFS0VfNR6Wf4ufmQkXiNgyt0HgZPPXen38b0UZOpaKti9a6nyGnMByA1Ws+DN0wmLFDLl3ur+H9v\n7qe1Q87KEEKcHgkwAoCxASlMCh5PWWsFX1Z8g7ubiutmJ2C2WFn7aYE09A6wWq1kfZpPT5+ZjJlx\n+Hi50Wcx8WrOm5itZlYkL0Or8XJ0mU5Fo9KwInkpy5MW02Pu4f8OvMQnpV9gsVoI8vfkgcyJTEoM\npKCimf9as4sjta2OLlkI4QIkwAibpfEL8dZo+bBkM3Wd9UyIDyAt1kBumZFdeSe+Zs75Znd+PQeL\nG0mO1DF9TAgAH5V8SnVHLTNCp5JqSHRwhc7rwtEXcPfE2/B39+PD0s38++AaOvu68HBTc+vVY1h8\nSQzG1h7+sXYv32fXOLpcIYSTkwAjbLzdtCxLuJo+i4nX8jZgxcry2fGoVUre/KKQrh6To0t0qM7u\nPl7/rAC1SsmqgQXrippL+bz8KwI8DSyKu9LRJTq9SN9w7pv8e5J08WQ35rJ691NUtdegUCiYPy2K\n3y9NQ61S8sKHubz5RSFmi/TFCCFOTAKMGCQ9KI1xgWMoai7l26rtBOm8uGJqBM3tvbz3bamjy3Oo\nDV+V0NLRy1UXRhGs96Lb1M2rOesAuD4lA3eVm4MrdA3eblp+O/5XXB55GQ1djfzP7mfYWbsXgLTY\nAB68fhKjDF58uquCx9cdoE1WKxVCnIAEGDGIQqEgI+FqPNWevFv8MY1dRq6YGkmgvwef766ksq7d\n0SU6RGFlM1v3VREaoGXeBREAvFP0IY3dTcyJvJQYvyjHFuhilAolC2PncfPYVagUKtbkvMlbBe9i\nspgI0Xvx51WTmBAfQG6ZkUfW7Kb8aJujSxZCOBkJMOIn/Nx9WRJ/FT3mXt7IfxuNWsmKOQlYrFbW\nfpp/3jX0mswW1mzqP3Pm+l8koVYpyW7I5bvqnYR6j2J+9BwHV+i6xgWO4d7Jv2OUNpivKr/nf/f9\nm+aeFjzd1fx20VgWzoimoaWbv2ftYWfuUUeXK4RwIhJgxAldEDKRFH0iuU0FbK/dQ1psABPiAyio\nbOH77FpHlzesPtleRnVDB5dNCCUuzI/23g7W5q1HrVBxfcq1qJVqR5fo0oK9AvnDxNuZGDSOkpYy\n/nvX/1JoLEapULBwRjS/WzQWhVLBv947zPqtRVgs51eAFkKcmAQYcUIKhYLrkhbhrnLj7cIPaO5p\n4brZ8biplazfUkRnd5+jSxwWtU2dfPB9GX7ebiy+JBar1cqb+e/Q1tvOlTFzCfUe5egSRwQPtTs3\npi5nSfwCOvo6eWr/83xZ/jVWq5UJCYH8edUkgnWefLK9nCfXH6DjPPn8CSFOTgKMOCm9h45r4ubT\nZepiXf67GHw9uOrCKFo7+3jn6xJHl2d3VquVVzflYTJbWDE7AS8PNbuO7mNf/SFi/aKYFXGxo0sc\nURQKBZeFz+D3E36Dt0bL20Uf8tLh1+g29RAaoOXB6ycxNsZAdmkTj7yym6r687MfSwjRT/Xwww8/\nbK+NFxQUkJGRgVKpJC0tjTvuuIPXX3+djRs38vLLL5Obm0tCQgKXXHIJ3333HRs3bmTr1q3Mmzfv\nlNvttONZCVqtu12372rCfUIpai4ht6mAEG0QF8YnsCuvjuzSJsbHBeDv7T5stQz32Hx3qJbPdlcy\nPi6Aqy+KprmnhX8dfBmVQsnt43+NVqMdtlqc2VCPi95Dx+TgCZS2lpPTlM/BhhySdHHovHy5IDkY\ns8XK/qIGvs+uZZTBi9EBMg4nIz/PnJeMzenRak/+HWO3GZjOzk4eeeQRpk2bZrvtqaeeIisri6ys\nLMaMGcPSpUsBiI6Ott3+1FNP2askcRaUCiXLk5agUWp4q+A9usydrLw8AasVsj7NxzJCG3pbO3tZ\n92Uh7hoVK+YkYMXK2tz1dJm6WRx/FQGeBkeXOKL5ufty54TfcGnYhdR2HOXR3U+zvz4bpVLB4kti\nue3qMQA8uzGbjV+XjNjPoRDi5OwWYNzc3Hj++ecJCgr6yX0lJSW0tbWRlpZmr5cXQyjIK4CrYubS\n3tfBhsL3SYnSMyU5iJLq1hF7JeF1XxTR0W1i0cUxGPw8+LpqG3nGQsYYkpg+aoqjyzsvqJQqliYs\n5IaU67BYLTx/6FXeK/4Es8XMpKQg/pQ5kQA/Dz74/ghPbzhIZ/f5vdCiEOcbu50+oVarUatPvPlX\nX32VlStX2v7d0NDAHXfcQV1dHcuXL2fBggWn3LZO54VarRrSeo8XGOhjt227qmWGeRxqymb30f3M\njJ/KbUvHc2j1F7zzdQlzpkXjN0yHkoZjbPbl17HtcC1x4f5k/CKZo+1Hea/4Y3zctNxx4Q34e/ra\nvQZXY89xuSLwYsaEx/L/vvs3n5Ztoaa7mt9P/RUTAkfxVLSBR7N2s7+gnn+8tpc//3IKYUGy/wI0\ntXZT3dDOqABvFAqFo8sRJyDfNedGYbXzoh5PP/00Op3OFlh6e3tZvHgxH3zwAQDt7e1s3ryZBQsW\n0NbWxtKlS3njjTdOOHPzg/p6+y1qFRjoY9ftu7KajqP8984n0Wq8+PMFf+DrvfW8taWIi8eN5oZ5\nSXZ//eEYm54+M395cQeNLT08eP0kwoK8eGzv/1HWWsGvxqwkPUhmDX9suPaZLlMXa3LWcaghB393\nP24am0mUbwRmi4W3t5awaWc5nu4qbroqlfFxAXavx9l0dpvILzeSU2Yk50gTNY2dAPhp3UgI9ycx\nwp/EcH9GB2gl0DgB+a45PacKeXZt4gXYuXMnnp6etsNF27dvp7W1lVmzZgH9h5pSUlJQqVR4enqy\nd+9ewsLCCAsLO+k2pYnXMXzc+n+TO9iQQ3tfBwvGTmVvQT2HS5sYE6NH7+Nh19cfjrF595tSDhQ1\nMndKBBeOHcXmI1vYdXQvk4PTmRc9y66v7aqGa5/RKDWkB6WhVqo51JDDjpo9+Lh5E+kbxpgYA8E6\nT/YWNLA9uxalAuLD/Uf0F3WfyUxBeTNfH6jm7a9KeOOLAnbk1lFa00qvyUxypI6ESB11TZ2U1LRy\nsLiRLfuq+HJvFSXVrbS096JRK/HRuo3o/ydnJd81p+dUTbzDvgLXoUOHSEo69tv69u3b2bJlC/ff\nfz+dnZ3k5eURHR093GWJ0zQn4lL21R1iW80uJgaPY+XlCax+fR9rNxfw4PWTUCpd9wdhZV07m3eW\nY/D1YOGMaMrbKvn4yGf4u/uxLGGho8sT9DeV/yJqJpE+Ybyc8zpv5L9DaWs5GQnXMDU1hFEGLc+8\nc5CN35RSfrSdX85PxtN9ZCw0aLFYOVLbRm5ZEzlHjBRVtdBn6r/YpUqpIDbUj5RIHcmROmJD/VCr\nlAQG+lBX10qdsYv8imbyy43kVzSzp6CePQX1AHi5q0kI97fN0kQEe6NSygobwvnZbc/Ozs5m9erV\nVFVVoVar2bx5M08//TT19fVERETYHjdp0iTeffddMjIyMJvN3HzzzQQHB9urLHGOVEoVK5OX8uju\np3k9723+NOVupqWGsO1wLVv2VTFr4slnzpyZxWLllU15mC1WMucmolRZWJOzDovVwsrkpXhpPB1d\nojhOsiGBP076PS9kv8r2mt1Utddw05hMIkP0PHjDZP71bjZ7Cuqpberk9sVjCdZ5ObrkM2a1Wqlp\n7CR34JBQfnkzncddET4s0JuUqP7AkhDubwtqDV2NfF+znZymfCxKM1HaSBJ18Vw4NpyLx43GarXS\n0NJNfnkzBRXN5FcY2V/UwP6iBgA83FTEhfmRGO5PYoSOqBAf1CoJNML52L0Hxh6kB8bx3i/exOay\nL7kkbDpzQ6/ggee2A/D3m6fip7XPVZntOTZf7Knktc8KmJIcxC0Lx/B24Qd8WfENl4RNZ1nC1XZ5\nzZHCkftMn7mPtwre5fuaXWjVXtyQeh0phkRMZgtvbSni892VeLmruWVhKmNinP/U96bW7oHAYiS3\nrInm9mOHGAL8PEiJ0pESpScpQofvwH7Wa+6jsLmEnMY8cpryqetsOOG23VVuxPvHkKiPJ1EXx2ht\niO3QUVNr98AMTTP5Fc0cbeq0Pc9NoyQu1K9/hibcn5jRvmjseBLF+UK+a07PqXpgJMD8iHyoTk+f\nuY//3vW/1HbWcVf6rZQVa3jtswKmjwnh11em2OU17TU2xrYe/vT8dpQKBX+7eSpHeyt4at9zBHoZ\nuH/ynbip7BPIRgpn2Ge+q97BW/nvYrZamB99OXOjLkOpUPLtwRpe3ZyP2WJhySWx/OKCCKfq92jv\n6iOvzNgfWsqMg4KDj5eG5Mj+wJIcqSPQv38W0Gq1UtfVQE5jPjmN+RQ2F9Nn6Z+ZcVe5kaiLJ8WQ\nQIo+kbDgALYVHSDfWESesXBQuPHReJOojyNRF0eiLh6Dp852X0t7T3+gqWimoLyZqoYO231qlZLY\n0b62puCYUD/cNRJozpQz7DeuQALMGZAP1ekraSnj8T3/R6CngT9OupP/Xruf8qPt3LcinYRw/yF/\nPXuNzTPvHGJvQT03zEticqqev+98guaeFu6ZeBtRvhE/v4HznLPsM2WtFTx/KAtjTzNjDMlcn3It\nXhpPSqpbeXbjIYxtPUxJDuLGecm4uznmC7enz0xhZTO5R/oDS3ltGz/8AHZ3U5EY7t/fxxKlJzRQ\ni3IgbHWbeihsLianMZ/Djfk0djfZtjlaG0KKIZFUQyIxflGDLi7647ExdjeTZywiv6mIfGMhrb3H\n7gvwNJCoiyNJH0+CfyzebsdWOG7t7KXwuEBTUdduq1ulVBA96ligiQ31GzF9R/bkLPuNs5MAcwbk\nQ3VmfjjUMjviEtI8Z/C3rD2EBmp56IbJQ37c3B5js6+gnqffOURCmB/3rkjntbz1bK/ZzbyoWVwZ\nM3dIX2ukcqZ9pr23g5cPv06esZAATwM3j11FqPcoWtp7ePbdbIoqWwgP8uZ3i8YS4G//viazxUJp\nTRu5R5rILetvvDWZ+3/kHt94mxKlJ2rUsV4Tq9VKTcdRcpr6Z1mKm0sxWc0AeKg8SNLHk2pIJFmf\ngM7j5L8snGpsrFYrtZ115Df1z84UGkvoNnfb7g/3Hk2Cvn92Js4/GvfjZiI7uvsorGghv8JIfnkz\nZUfb+OGbRKlQEBniQ2K4PwkR/iSE+eHloTmn/8eRyJn2G2cmAeYMyIfqzPSae/nbjsdp7DbyH5Nu\nZ8t37Xx9oIaMmXHMnTK0sxdDPTZdPSb+/MIO2jp7efjGKdRbj/DcoTWE+4Tyh4m/HfSbrDg5Z9tn\nLFYLH5Z8yuayL9EoNSxPWsyUkHRMZguvf17I1n1VeHtquHVhKslR+iF9bavVSlVDB7lH+g8L5ZUb\n6e7tDx4KIDzYm5QoPSmROuLD/AfNBHWZusk3FvX3sjQWYOxptt0X7j2aFEMSKYZEon0jUClPbwbp\nTMbGbDFT3lZFvrGQ/KYiSlqO2EKTSqEi2i+CJF08ifo4In3CB9XQ1WOiqKploIfGyJGaNswW66D3\nnRiuIzGi/2wnb08JNM623zgrCTBnQD5UZ67AWMT/7nuO0doQbku9hb+8sBuTxcrfb5qKzmfoVugd\n6rF57bMCvthTyYILo5h1QRB/3fEY3eYe7pv8e0Zp5Uy40+Ws+8yB+sO8mrOObnM3l4RNZ1HclaiV\nar7aX8XaTwuwWmHZzDjmTAo7p76Yhpaugabb/j+tHccab4N1niQPBJakSN2gL26r1UpVe01/L0tT\nPsUtR7BY+0+L1qq9SNLHk2JIJFmfiJ/72a3Yei5j02vupbjliO1wU0VbNdaBA0ceKnfi/GNI1MeR\npItnlDZ40P9hT6+ZouoWCgaagkuqj808AYQGam1nOSWE+9ut8d+ZOet+42wkwJwB+VCdnTfy3ubb\n6h1cETUbbWsqr27Kt53RM1SGcmxKqlv526u7CdZ78fCNk3kl7zUO1GezOO5KZkZcPCSvcb5w5n3m\naGc9zx96lZqOo8T4RfKrMSvxd/ejqLKFZzceoqWjl2mpIVz/i0TcTrMRta2z1xZWco8YqWvust3n\np3UjeeDU5pRIPQa/wYs7dvZ1kttUSE5TPrmN+bQM9KAoUBDhG0aKvr+XJdI3HKXi3A/BDuXYdPR1\nUmAsJt9YRH5TIXVdxzUEu3nbmoETdXGDGoKhf9G9kupW21lOxVUt9A6sYQMwyuBlO8spMUI3pL/4\nOCtn3m+ciQSYMyAfqrPTZermrzseo7W3jXsn3cEr71RTWtPKH64dT8oQTdMP1diYzBb+65XdVNa3\n88flEzBqisnKfYt4/xjumHDzkHxxnE+cfZ/pMffyWu569tQdwMfNm1+lriBeF4uxrYdnNx6ipLqV\nyBAffrdoLHrfn64m3d1roqCihdyyJnKPGCmva7fd5+muIjFc178eS5Se0QavQTMRFquFirYqchoL\nyGnKo7Sl3DaL4a3RkjwQWJL08fi4eQ/5e7fn2BxrCC4k31g0qCE4cKAhOFEfT4IuFm+NdtBzTWYL\nR2rabD00hVUt9AwcagMI8vckYaApODHcf1j6lYabs+83zkICzBmQD9XZO9yYx/8deIkIn1CWhl3P\n39bsJVjvxX/9asqQNPQO1dh8sr2M9VuLuShtFAsuC+HvO58ArDww5S4MnkPbE3E+cIV9xmq1srXy\nO94p+hCAa2Kv4LLwizCZrWR9ms+3B2vw8dJw29VjiA31o6S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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i4lGvqajDWlw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## One-Hot Encoding for Discrete Features\n", + "\n", + "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n", + "\n", + "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n", + "\n", + "| # | feature_value |\n", + "|---|---------------|\n", + "| 0 | 2 |\n", + "| 1 | 0 |\n", + "| 2 | 1 |\n", + "\n", + "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n", + "\n", + "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n", + "|---|-----------------|-----------------|-----------------|\n", + "| 0 | 0.0 | 0.0 | 1.0 |\n", + "| 1 | 1.0 | 0.0 | 0.0 |\n", + "| 2 | 0.0 | 1.0 | 0.0 |" + ] + }, + { + "metadata": { + "id": "KnssXowblKm7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Bucketized (Binned) Features\n", + "\n", + "Bucketization is also known as binning.\n", + "\n", + "We can bucketize `population` into the following 3 buckets (for instance):\n", + "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n", + "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n", + "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n", + "\n", + "Given the preceding bucket definitions, the following `population` vector:\n", + "\n", + " [[10001], [42004], [2500], [18000]]\n", + "\n", + "becomes the following bucketized feature vector:\n", + "\n", + " [[1], [2], [0], [1]]\n", + "\n", + "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n", + "\n", + "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements." + ] + }, + { + "metadata": { + "id": "cc9qZrtRy-ED", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_boundaries(feature_values, num_buckets):\n", + " boundaries = np.arange(1.0, num_buckets) / num_buckets\n", + " quantiles = feature_values.quantile(boundaries)\n", + " return [quantiles[q] for q in quantiles.keys()]\n", + "\n", + "# Divide households into 7 buckets.\n", + "households = tf.feature_column.numeric_column(\"households\")\n", + "bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " california_housing_dataframe[\"households\"], 7))\n", + "\n", + "# Divide longitude into 10 buckets.\n", + "longitude = tf.feature_column.numeric_column(\"longitude\")\n", + "bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " california_housing_dataframe[\"longitude\"], 10))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U-pQDAa0MeN3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train the Model on Bucketized Feature Columns\n", + "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n", + "\n", + "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples." + ] + }, + { + "metadata": { + "id": "YFXV9lyMLedy", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + "\n", + " #\n", + " # YOUR CODE HERE: bucketize the following columns, following the example above:\n", + " #\n", + " \n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " \n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " \n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0FfUytOTNJhL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "297b0ad1-2e63-44a4-940c-e8a7eda0f9cf" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=0.95,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 174.12\n", + " period 01 : 148.40\n", + " period 02 : 131.85\n", + " period 03 : 120.31\n", + " period 04 : 111.96\n", + " period 05 : 105.64\n", + " period 06 : 100.77\n", + " period 07 : 97.04\n", + " period 08 : 93.86\n", + " period 09 : 91.24\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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REVErc+LEUZ3arVr1KdLSUus8/o9/vNpUITU5FjBEREStSHp6Go4cOahT26VLl8HNrX2d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\nziY2FK2joqx4WbMZn5ydvzuTuEszOHLtbniu2cC6ysQTTo3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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZTDHHM61NPTw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "JQHnUhL_NRwA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model." + ] + }, + { + "metadata": { + "id": "Ro5civQ3Ngh_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RNgfYk6OO8Sy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "46c61e82-81c8-40c3-b099-c0f1eca68bc7" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 170.96\n", + " period 01 : 144.52\n", + " period 02 : 127.92\n", + " period 03 : 116.53\n", + " period 04 : 108.44\n", + " period 05 : 102.46\n", + " period 06 : 97.93\n", + " period 07 : 94.30\n", + " period 08 : 91.38\n", + " period 09 : 89.03\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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xA33s0M/LFldTSnAttUTHFRIRERk+BhgtGOE6FB4WbjifdwnpFZnNlguCgMgQ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\nQJWxAmW5JYlJwfMVjkRwwTmWmEPT3OPB+MR0obEY1FhWakq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "AFJ1qoZPlQcs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Feature Crosses\n", + "\n", + "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n", + "\n", + "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n", + "\n", + "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model." + ] + }, + { + "metadata": { + "id": "-Rk0c1oTYaVH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train the Model Using Feature Crosses\n", + "\n", + "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n", + "\n", + "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`." + ] + }, + { + "metadata": { + "id": "-eYiVEGeYhUi", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xZuZMp3EShkM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "5292c043-bd0b-4573-d9aa-081de0add052" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 164.63\n", + " period 01 : 136.14\n", + " period 02 : 118.86\n", + " period 03 : 107.43\n", + " period 04 : 99.39\n", + " period 05 : 93.51\n", + " period 06 : 89.00\n", + " period 07 : 85.46\n", + " period 08 : 82.59\n", + " period 09 : 80.19\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RoH6rVn2BtLRUje1vvfVqU4XU5FjAEBERtSHp6Wk4duxIg/ouXrwELi4dNLZ/8smKpgqr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\n62iuQO6b5EgBkwLpVMol2SiT5JJeczfROzVwhp5xd/w9h7G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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "0i7vGo9PTaZl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "3tAWu8qSTe2v", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-_vvNYIyTtPC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "bf203533-8259-444f-ee51-59b0380aa187" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 164.64\n", + " period 01 : 136.27\n", + " period 02 : 119.00\n", + " period 03 : 107.55\n", + " period 04 : 99.45\n", + " period 05 : 93.52\n", + " period 06 : 89.01\n", + " period 07 : 85.39\n", + " period 08 : 82.65\n", + " period 09 : 80.27\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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OjEZ7v87OcLAxwckrKUjPLdFBhERERE0DC5inSBAEjG89CgCwI0Zzo0eZVILx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\noz3oHu1Fz9h5hKKzz4QxqQtQa6hCrbEKtYZqlGgtV/38mbk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ymlHJ-vrhLZw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Try Out More Synthetic Features\n", + "\n", + "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?" + ] + } + ] +} \ No newline at end of file diff --git a/feature_sets.ipynb b/feature_sets.ipynb new file mode 100644 index 0000000..b6871d7 --- /dev/null +++ b/feature_sets.ipynb @@ -0,0 +1,1580 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_sets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "IGINhMIJ5Wyt", + "pZa8miwu6_tQ" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zbIgBK-oXHO7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Feature Sets" + ] + }, + { + "metadata": { + "id": "bL04rAQwH3pH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set" + ] + }, + { + "metadata": { + "id": "F8Hci6tAH3pH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set." + ] + }, + { + "metadata": { + "id": "F5ZjVwK_qOyR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "As before, let's load and prepare the California housing data." + ] + }, + { + "metadata": { + "id": "SrOYRILAH3pJ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "dGnXo7flH3pM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jLXC8y4AqsIy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "63343266-945e-42bc-a2cc-90721d4d0f4d" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2648.5 539.8 \n", + "std 2.1 2.0 12.6 2202.9 425.2 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1459.8 297.0 \n", + "50% 34.2 -118.5 29.0 2124.0 433.0 \n", + "75% 37.7 -118.0 37.0 3157.0 646.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1432.1 502.3 3.9 2.0 \n", + "std 1183.7 390.2 1.9 1.1 \n", + "min 6.0 1.0 0.5 0.0 \n", + "25% 789.0 282.0 2.6 1.5 \n", + "50% 1169.0 408.5 3.6 1.9 \n", + "75% 1720.0 603.2 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 52.0 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "hLvmkugKLany", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Develop a Good Feature Set\n", + "\n", + "**What's the best performance you can get with just 2 or 3 features?**\n", + "\n", + "A **correlation matrix** shows pairwise correlations, both for each feature compared to the target and for each feature compared to other features.\n", + "\n", + "Here, correlation is defined as the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient). You don't have to understand the mathematical details for this exercise.\n", + "\n", + "Correlation values have the following meanings:\n", + "\n", + " * `-1.0`: perfect negative correlation\n", + " * `0.0`: no correlation\n", + " * `1.0`: perfect positive correlation" + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 379 + }, + "outputId": "abd1a117-74ff-4e78-c3cc-a0d51b48d6d8" + }, + "cell_type": "code", + "source": [ + "correlation_dataframe = training_examples.copy()\n", + "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n", + "\n", + "correlation_dataframe.corr()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms \\\n", + "latitude 1.0 -0.9 0.0 -0.0 \n", + "longitude -0.9 1.0 -0.1 0.0 \n", + "housing_median_age 0.0 -0.1 1.0 -0.4 \n", + "total_rooms -0.0 0.0 -0.4 1.0 \n", + "total_bedrooms -0.1 0.1 -0.3 0.9 \n", + "population -0.1 0.1 -0.3 0.9 \n", + "households -0.1 0.1 -0.3 0.9 \n", + "median_income -0.1 -0.0 -0.1 0.2 \n", + "rooms_per_person 0.1 -0.1 -0.1 0.1 \n", + "target -0.1 -0.0 0.1 0.1 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "latitude -0.1 -0.1 -0.1 -0.1 \n", + "longitude 0.1 0.1 0.1 -0.0 \n", + "housing_median_age -0.3 -0.3 -0.3 -0.1 \n", + "total_rooms 0.9 0.9 0.9 0.2 \n", + "total_bedrooms 1.0 0.9 1.0 -0.0 \n", + "population 0.9 1.0 0.9 -0.0 \n", + "households 1.0 0.9 1.0 0.0 \n", + "median_income -0.0 -0.0 0.0 1.0 \n", + "rooms_per_person 0.0 -0.1 -0.0 0.2 \n", + "target 0.0 -0.0 0.1 0.7 \n", + "\n", + " rooms_per_person target \n", + "latitude 0.1 -0.1 \n", + "longitude -0.1 -0.0 \n", + "housing_median_age -0.1 0.1 \n", + "total_rooms 0.1 0.1 \n", + "total_bedrooms 0.0 0.0 \n", + "population -0.1 -0.0 \n", + "households -0.0 0.1 \n", + "median_income 0.2 0.7 \n", + "rooms_per_person 1.0 0.2 \n", + "target 0.2 1.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "RQpktkNpia2P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n", + "\n", + "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n", + "\n", + "Use this information to try removing features. You can also try developing additional synthetic features, such as ratios of two raw features.\n", + "\n", + "For convenience, we've included the training code from the previous exercise." + ] + }, + { + "metadata": { + "id": "bjR5jWpFr2xs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jsvKHzRciH9T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g3kjQV9WH3pb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "varLu7RNH3pf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters." + ] + }, + { + "metadata": { + "id": "DSgUxRIlH3pg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "153aa543-ca66-4b30-8e33-94463ce0b9e7" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = [\n", + " \"population\",\n", + " \"households\",\n", + "]\n", + "\n", + "assert minimal_features, \"You must select at least one feature!\"\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 223.82\n", + " period 01 : 210.38\n", + " period 02 : 198.37\n", + " period 03 : 190.17\n", + " period 04 : 183.59\n", + " period 05 : 179.89\n", + " period 06 : 178.22\n", + " period 07 : 177.55\n", + " period 08 : 177.66\n", + " period 09 : 178.04\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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dapOV/WA+THqmzIcRQghRukmA+YdcLZ0ZUKs36TkZLDr3C9m56oQL3xrOdGxY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\nTcID01YjUh0hdVmX5Ut9sdkdOFnajENFdYMOyc5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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "IGINhMIJ5Wyt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "BAGoXFPZ5ZE3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "c09bd40d-a1fb-43aa-8bd1-38ffd9ea3487" + }, + "cell_type": "code", + "source": [ + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 167.40\n", + " period 01 : 125.96\n", + " period 02 : 119.52\n", + " period 03 : 119.51\n", + " period 04 : 116.36\n", + " period 05 : 117.27\n", + " period 06 : 116.37\n", + " period 07 : 114.81\n", + " period 08 : 116.12\n", + " period 09 : 114.46\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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iIo9hgHFBvJP1YII1CgSqfJGaY4Aoip7uGhERUY/EAOOCWE00BAjN1sEIgoB4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\nIQDPP/88Nm/ejFWrVrW7K9TE1NQUHn/8cYyOjuLzn/88/vK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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RidI9YhKOiY2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Make Better Use of Latitude\n", + "\n", + "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n", + "\n", + "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco." + ] + }, + { + "metadata": { + "id": "hfGUKj2IR_F1", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 364 + }, + "outputId": "e2dd5086-9771-4d96-b0e3-aaaa232ff4ae" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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mLGtCjcKx+eKW5fPR3euSPI+qihIsWVQJM0Pjpd+cwaHTl+CaCMBh5xTDhAxr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\nH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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "6N0p91k2iFCP", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Try creating some synthetic features that do a better job with latitude.**\n", + "\n", + "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n", + "\n", + "Or you could break the space into 10 different buckets. `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n", + "\n", + "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n", + "\n", + "What's the best validation performance you can get?" + ] + }, + { + "metadata": { + "id": "wduJ2B28yMFl", + "colab_type": "code", + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "8135d122-e764-47ab-eca6-b80ed4a05793" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#\n", + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 229.13\n", + " period 01 : 218.85\n", + " period 02 : 208.68\n", + " period 03 : 198.58\n", + " period 04 : 188.60\n", + " period 05 : 178.76\n", + " period 06 : 169.07\n", + " period 07 : 159.60\n", + " period 08 : 150.33\n", + " period 09 : 141.30\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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PHxvX9p5LtG+PDjrLtu3MKuGdz7OorG0kMtiHm6b1Js7me1Hadif5i0WbJBft\nkmy0S7JxziV5jNqdtF7A/KSmsZYVuZ+ytXAnCgqXRY5mZtwUPA3uX023rqGJ97/K5ds9BSgKXD60\nB3PGxeJpcv+eTu4iA16bJBftkmy0S7JxjhQwLnBHp8ouz+U/mSsori/Fz8PK/F5XkhTcr0PbOJfM\nw+W8tTaTovJ6An09WTg1sdMugCcDXpskF+2SbLRLsnGObOboAnc8mx/oFcBo23AURSHDns2Oot0c\nr84nzhqDl5uvxgT5eTEuyYYKpOXZ+X5/IUX2OhJ6+OFhdP/iex1J1k3QJslFuyQb7ZJsnCN7IbnA\nXZ1Kr9PTyz+eQSEDOF5TQEZ5Dpvzt+Gh9yDKN9Ktj1zr9Tr6xgS0LoCXlndyATyrj4nIYJ9OswCe\nDHhtkly0S7LRLsnGOVLAuMDdncpi8mFE+FD8Pa1klueypzSNdHsWUZYeWD3cuyaA1dvE2IE2zJ5G\n9ufZ2Z5ZzIHjlSRE+uHt6d4dtjuCDHhtkly0S7LRLsnGOVLAuOBidCpFUYiyRDIyfBiVJ6rIsGez\nuWAbDY4G4qwxGNy4r5KiKPSMsDKyXyiF9nrS8ux8uycfg15HrM2CTsNXY2TAa5Pkol2SjXZJNs65\nZLtRu0tnm8R7PullWbyX9RFlDXYCPP35Ra8r6R/Ux+3tqqrK1vQi3l2fQ019E9FhFm5K7U10mDZX\nh5RJb9okuWiXZKNdko1zZBKvCy5FVRxsDmK0bTgtagsZ9my2F+2msLaIntZYPA3nrj4vlKIoRIb4\nMGZgOFW1jaQdtLNxTwEnmhzER1ox6N22zmG7yF8s2iS5aJdko12SjXPkFpILLlWn0uv09A5IICm4\nH0er81tvK3kZvOhhsbl1oq2HUc+QXsHER1jJPlrB3gNlbM8oxhbkTbCfl9vadZUMeG2SXLRLstEu\nycY5UsC44FJ3Kl+ThVHhw/A1+ZBpz+WHkn1klecQ4xuFxeTj1rZD/E8+cu1wqOw9WMbmtELKKhtI\n6OGHSQOPXF/qbMTZSS7aJdlol2TjHClgXKCFTqUoCtG+PRgRPoTyhgoy7Nl8l7+V5pZmYq3R6N04\nydeg19EvNoCk+EDy8qvYl2fnu30F+Fs8iQjyvqSPXGshG3EmyUW7JBvtkmycIwWMC7TUqTwNngwJ\nTSLKEkFuRR5pZRnsLN5DuHcoQV7uXU3Xz8eDMQPD8TTpScuzsy2jmEOF1SRE+mH2vDTbEWgpG/Ff\nkot2STbaJdk4RwoYF2ixU4Wag0mxDae5pZn0siy2Fu6kpK6Mnn4xeOhNbmtXp1NIiPRjeJ8Q8ktr\n2Z9n59u9+Xga9cSE+V70qzE/yjHCAAAgAElEQVRazEZILlom2WiXZOMcKWBcoNVOZdAZ6BuYSP+g\nPhytPkaGPZvv87fjY/Qm0se9k3x9vIyk9A8jyOpFxiE7u7JL2Z9nJ87mi6+3+wqon9NqNt2d5KJd\nko12STbOkQLGBVrvVFYPX0aFJ2M2mskoz2F3yT5yKg4S6xuFj8nbbe0qikJUqIXRA8Ipr25oXQDP\n4VCJj/BFr3P/I9daz6a7kly0S7LRLsnGOVLAuKAzdCqdoiPWGs2IsCGU1Je1TvJtQT05yVdxXzHh\nadIzrHcIMWEWso5WsCe3jB2ZJfQI8SHQ6t6NKTtDNt2R5KJdko12STbOkQLGBZ2pU3kZPBkakkSE\nTzg5FQdJK8tgd/FebN5hBHoFuLXtsAAzYwfaaGxysO9gGZv2FVBZc4KESD+MBvcUUJ0pm+5EctEu\nyUa7JBvnSAHjgs7WqRRFIcw7lBRbMiccjaSXZbOlcAflDRX09IvFpHffJo1Gg44BPQPpHxvAwYIq\n9h20811aAcFWL2xBHX87q7Nl011ILtol2WiXZOMcKWBc0Fk7lVFnpF9gb/oEJHK4+ijp9iy+L9iO\nr8lChE+4Wyf5Bvh6Mi7JhkGvkJZnZ2t6EUeLa0iI9MPLo+Meue6s2XR1kot2STbaJdk4RwoYF3T2\nTuXvaSUlfDgeeg8y7DnsLtlLbuUhYq1R+BjdN8lXp1NIjPJnWO8QjpXUkpZnZ+PefLw9jUSFWTqk\ngOrs2XRVkot2STbaJdk4RwoYF3SFTqVTdPT0i2FY6GBK6kvJtGfz3fGttKgtxPpGuXUlX4vZRMqA\nMPwsHqQfsrMzq4TMw+X0jLBiMV/YI9ddIZuuSHLRLslGuyQb50gB44Ku1KnMRi+GhQ7Cdsok310l\newk3hxLkxkm+iqIQE+ZLSv9wyir/+8g1QM8IKzpd+67GdKVsuhLJRbskG+2SbJwjBYwLulqnUhSF\ncO9QUmzDafxxku/FWsnXy8PA8D6hRAb7kHmknB9yy9iVU0J0qIUAX9cfue5q2XQVkot2STbaJdk4\nRwoYF3TVTmXUGegX2Jt+gb05Un2cDHs2m/O34W0wE2lx70q+tiBvxg0Mp66hmX0H7WzaW0BNfRMJ\nkVaXHrnuqtl0dpKLdkk22iXZOEcKGBd09U7l52ElxTYcb6OZrPJcdpfsI6s8lxjfHlhMPm5r12jQ\nkxQfRJ9of3KPV7LvYBlb0gsJ9TcTFmB26hhdPZvOSnLRLslGuyQb50gB44Lu0KkURSHWGsWI8KHY\nGypaV/JtdDQSZ4126yTfQKsn45LCUVBIO2jn+/1FFJTVktDDD09T2+12h2w6I8lFuyQb7ZJsnNNW\nAaOoqqpexHPpECUl1W47dnCwxa3H16K00gyWZa/E3lBOoKc/83tdSf+gPm5v91hJDW+tyeRAfhXe\nngbmT4xnzIBzr1nTHbPpDCQX7ZJstEuycU5wsOWcr8kVmJ/pjlVxiDmY0bYRtKgtpNuz2V60m4Ka\nQuL8YvA0uG9/I19vE2MGhGMxm0g7ZGdHZgk5xypJiLTi7XXmCsLdMZvOQHLRLslGuyQb58gtJBd0\n105l0OnpHZBAUnA/jlUXkFF+cpKvh96DKN9It03yVRSFOJsvKf3CKLLXkZZn55s9+Rh0J7+vO6Xd\n7pqN1kku2iXZaJdk4xwpYFzQ3TuVr8nCyPBh+Hn4klV+gD2laewvyyTKNwKrh6/b2vXyMDCibyi2\nIG8yD5ezO6eUPTmlxIRb8PM52YG7ezZaJblol2SjXZKNc6SAcYF0qpNXRaJ8IxkZPoyqE9Wtj1zX\nNdUTZ43GoOu4/Y1+3m5EsA9jBtqorm9i38GTC+CdaHQQH2nF1+LZ7bPRIhkz2iXZaJdk4xyZxOsC\nmVh1pkx7DsuyPqK4vhQ/DytXJ1xBUnB/t64dA5B+yM7ba7MorqgnyOrJ738xmB4BXm5tU7hOxox2\nSTbaJdk4RybxukCq4jMFeQUy2jYcRdGRac9mR/EPHKk+Tpw1BrPRfQVFsJ8XY5NstKgqaQftfLXz\nKMXldST08MPD6L5HvYVrZMxol2SjXZKNc+QKjAukKm5bUW0x/8laQU7FQUw6I9NjJzOxx1i3rh0D\ncKSomnfW55B7tAIfLyO/mBhPSv8wt18FEucnY0a7JBvtkmycI1dgXCBVcdt8TN6MCBtKkFcg2RUH\n2Fuazp6S/URabPh7+rmtXauPB7MnJEBLC/vz7GzPLCbnWCXxkVZ8zvLItbh4ZMxol2SjXZKNc2QS\nrwukU52foihEWmyMsiVT11RPuj2LLQU7qDxRRU9rDEa9ewoKHx8Pwv08GdkvlKLyevb/uMu1TuHk\nI9ft3OVaXBgZM9ol2WiXZOMcuYXkArms57rcijzey1pBQW0RFqMPcxNmMSx0UIff3jk1G1VV2Z5Z\nzLvrc6iqbSQy2IebpvUmzua+R73F2cmY0S7JRrskG+fILSQXSFXsugBPf1JswzHpjGSWZ7OreC8H\nKw8Ta43G2+jcRo3OODWbnx65HpsUTu2Pj1xv3JNPbUMT8RGu7XItLoyMGe2SbLRLsnGO3EJygXSq\n9tEpOuL9YhkWOojiulIyyk9uEImqEmONQq9ceEFxtmxMBj2DEoLpHeVHzvEq9h04uct1iAu7XIsL\nI2NGuyQb7ZJsnCMFjAukU10Ys9FMcuhgwn3CyC0/wL6yDHYX7yXcO4xAr4ALOnZb2QRZvRh/yi7X\nW/YXcby0loRIK54m9yy8J06SMaNdko12STbOkQLGBdKpLpyiKIR7h5JiG84JRyPpZdlsKdxBWb2d\nOGsMHnpTu457vmz0Oh19ov0Z2iuYo8U1pOXZ+XZPAT5eBqJCLfLItZvImNEuyUa7JBvnSAHjAulU\nHceoM9IvsDf9AntzpOoY6fZsvs/fjrfRTIRPuMsFhbPZ+HqbGD0wHKuPB+mH7OzIKiHzcDk9I6xY\nzO0rnsS5yZjRLslGuyQb50gB4wLpVB3Pz8PKqPBkzEYzGeU5/FCyj+zyA8RYo7CYfJw+jivZKIpC\nbLgvKf3DKats+PFqTD4tKvS0WdHLI9cdRsaMdkk22iXZOEcKGBdIp3IPnaIj1hrNiLAhlDWUk2HP\n5rv8bTS1NBNrjXZqJd/2ZOPlYWB4n1B6hPiQdbSCH3JL2ZlVTI8QHwKtnu39OOIUMma0S7LRLsnG\nOVLAuEA6lXt5GTwZGppElCWC3Io80soy2Fn0AyHmYELMQW2+90KyCQ/0ZlySjRONDvYdLGPTvgIq\na06QEGnFaJB9lS6EjBntkmy0S7JxjhQwLpBOdXGEmoNJsQ3HoTrIsGezrXAXhbVF9LTG4Gk4e4e9\n0GyMBh0DewbSPzaAgwVV7Dto57t9hQRZPQkPNMsk33aSMaNdko12STbOkQLGBdKpLh6DzkCfgF4M\nDOrLsZr81ttKngYPoiwRZxQUHZVNgK8n45JsGA060vLsbM0o4khRDQmRVrw85JFrV8mY0S7JRrsk\nG+dIAeMC6VQXn6+HhVHhw7B6WMgqz2VPSRrp9iyiLD2wevx3GemOzEanU+jVw4/hfUI4XnLyketv\n9uTjadQTE+YrV2NcIGNGuyQb7ZJsnCMFjAukU10aiqIQ7duDEWHDqGysIsOezeaCbdQ31xNnjcGg\nM7glGx8vIyn9wwi0epJxqJxd2aXsO2gnzuaL1VseuXaGjBntkmy0S7JxjhQwLpBOdWl5GjwYHDKA\nON9oDlQeYn9ZJtsKdxHkFUBccA+3ZKMoCtGhFsYMCKei5gRpeSf3VWpqbiE+wopeL/sqtUXGjHZJ\nNtol2ThHdqN2gewQqh2NjibWHd7AF4e/xqE6GGYbyBXRMwj08ndru/sOlrF0XRallQ2E+HlxQ2oi\nfWMubBuErkzGjHZJNtol2Tinrd2opYD5GelU2lNYW8R7WR+RU3EQk87I9NjJTOwx1qm1Y9rrRKOD\nlZsO8vn2o6gqjO4fxi8mJeDjZXRbm52VjBntkmy0S7JxjhQwLpBOpU2qqpJRm85bu5dT01SLzTuM\naxKvoqdfjFvbPVxYzZtrMjlcVI2Pl5FrL09gZN9QmeR7Chkz2iXZaJdk45y2ChiZA/Mzcl9SmxRF\noW9ET5KsSdQ115Fuz+L7gu1UNFQQ5xeDqZ0bRJ6Pn48HY5PCMXsY2H/IzvaMYg7kVxEfacXbU67G\ngIwZLZNstEuycY5M4nWBdCrt8vb2oKlBZUBQX/oEJHCk+lhrIeNj9G7XBpHO0CkK8RFWRvQNpdBe\nx/48O9/+kI9erxBn80XXza/GyJjRLslGuyQb50gB4wLpVNp1ajb+nn6khA/Hy+BFZnkOu3/cIDLa\nt4dLG0S61L6nkZF9QwkLNJNxuJzdOaXsySklOsyCv+Xcg6yrkzGjXZKNdkk2zpECxgXSqbTr59no\nFB1xP24QaW/dIHIrjY5GYq3RGNwwyVdRFCKDfRg70EZ1fRP7DtrZuDef+hPNxEdaMXTDR65lzGiX\nZKNdko1zLtlj1E8++SQ7d+6kubmZX//61wwYMIC7774bh8NBcHAwTz31FCaTiU8++YS33noLnU7H\n/Pnzufrqq9s8rkzi7Z7Ol82+0nTez/4Ye0M5AZ7+zO81mwFBfd16ThmHy3lrbSbF5fUE+nqycGov\nBvZse1PKrkbGjHZJNtol2TjnkjyFtGXLFl577TWWLFlCeXk5c+bMYdSoUYwbN45p06bx7LPPEhYW\nxpVXXsmcOXNYvnw5RqORefPm8c477+Dn53fOY0sB0z05k02jo5E1h75k/ZFvaFFbSArqx7xeVxDg\n6b61YxqbHHz6/SHWbDmCo0VleJ8Qrr28V7dZyVfGjHZJNtol2TjnkjyFFB4ezuTJkzEajZhMJl5+\n+WWKi4t56KGH0Ov1eHp6smrVKkJCQigrK2PWrFkYDAYyMzPx8PAgNjb2nMeWW0jdkzPZ6HV6egck\nMCi4PwW1ha0bRBp0eqItPdApHX+LR6/X0Sc6gCG9gjlSVN26kq+P2UhUqE+Xf+Raxox2STbaJdk4\np61bSG7belev12M2mwFYvnw548aNY9OmTZhMJ/8qDQwMpKSkhNLSUgIC/rvKaUBAACUlJW0e29/f\njMHgvkXM2qr4xKXlbDbBwRYGxvyJbw5tYemeFXyUu5qdJT9w67BrSQzq6bZzG9g7jLWb83jrswze\nXJPJjuwSFl09iIhg90ws1goZM9ol2WiXZHNh3FbA/GT9+vUsX76c119/nSlTprR+/1x3rpy5o1Ve\nXtdh5/dzcllPu9qTTT+f/jwwPJaPc9ewuWAbD375NCnhw5kdPw0fo7dbznN4YjDx4Rb+/UU2u3NK\nWfTUV8xKiWbayOguOclXxox2STbaJdk4p60iz63/mm7cuJGXXnqJJUuWYLFYMJvNNDQ0AFBUVERI\nSAghISGUlpa2vqe4uJiQkBB3npboZnyM3lzfZx53DvkdNu8wNhds4+EtT/N9wQ6nCub2CPD15Pdz\nB3LbnAF4exn4aGMef35jO7nHKt3SnhBCdDduK2Cqq6t58sknefnll1sn5KakpLBu3ToAPv/8c8aO\nHUtSUhL79u2jqqqK2tpadu3axbBhw9x1WqIb6+kXw73JtzMnfgaNjkbeyXiff+x+iYLaIre1OTQx\nmL/9z0gmDI4gv7SWx97ZydJ1WdQ1NLutTSGE6A7c9hTSsmXLeOGFF06bjPv444/zwAMPcOLECWw2\nG4899hhGo5G1a9fy2muvoSgKCxYs4Iorrmjz2PIUUvfUkdnYG8pZnv0Je0r3o1N0XB41nmkxk9y2\nJQFAzrEK3lqbRX5pLX4+Jq67vBdDE4M7/SRfGTPaJdlol2TjHNnM0QXSqbTLHdmcunZMoKc/83td\nSf+gPh3axqmaHS18tuUwn24+TLOjhUHxQVw/uReBVk+3teluMma0S7LRLsnGObKZowvk0Tbtckc2\noeZgRttG0KK2kG7PZnvRbo7XFBBnjcbL0PFFhU6nkBjlT3KfEI6X1JCWZ+fbPfkYDTpiwy2dcl8l\nGTPaJdlol2TjHNlKwAXSqbTLXdkYTlk7Jr+mgAx7Npvyt7p17RgfLyMp/cMI9vP6775KuZ1zXyUZ\nM9ol2WiXZOMcKWBcIJ1Ku9ydjcXkw4jwoQR6+pNdcYC9pensLU0nwseGv+e5V4ZuL0VRiAq1MGZg\nONV1jaT9uK9SbX0T8ZFWjIbO8ci1jBntkmy0S7JxjhQwLpBOpV0XIxtFUehhiWCULZm6pjrS7Vl8\nX7CdyhNVxFljMOmNHd6mh1HPkF7BJPbwI/d4FfsOlvH9/kKC/bwID3TPWjUdScaMdkk22iXZOEcK\nGBdIp9Kui5mNSW9iYHA/evsncLjqaGsh42uyEOET7pYnh4L8vBifZEOnwP48O1vSizhSVE1CpBUv\nD7evOdluMma0S7LRLsnGOVLAuEA6lXZdimwCPP0YbRuOh96DTHsOu0r2klNxkBjfHviYOn57AL1O\noXe0P8N6h3C8pJa0PDvf7MnHw6AnNtxXk49cy5jRLslGuyQb50gB4wLpVNp1qbLRKTp6+sWQHDqE\nsoby1g0im1qaibVGodd1/L5cFrOJ0QPCCLR6knGonF05pew5UEZMmAU/H21N8pUxo12SjXZJNs6R\nAsYF0qm061JnYzZ6MSx0ED18bORW5JFWlsGOoh8I9gokxBzc4e0pikL0j5N8q2pPTvL9dk8+dQ3N\nJERaNbOv0qXORZybZKNdko1zpIBxgXQq7dJKNqHeIYyO+GntmCy2F+0mv6bQbWvH/DTJNyHSSu7x\nSs1N8tVKLuJMko12STbOkQLGBdKptEtL2fy0dkxScD+O1xT+eFtpK0adgShLpFvWjgn282L8IBsK\nCmkHT07yPVZcQ0Kk3yWd5KulXMTpJBvtkmycIwWMC6RTaZcWs/E1WRgZPpQAT3+yyw+wp3Q/e0vT\niXTT2jF6nY4+0f4MTQzhWPF/V/L1MOqJCbs0k3y1mIs4SbLRLsnGOVLAuEA6lXZpNZvWtWPCk6lt\nXTtmB5UnquhpjcHohrVjfM0mRg8IJ8DXk4zD5ezKLmXfwTJiw32xXuRJvlrNRUg2WibZOEcKGBdI\np9IurWfz09oxif7xHKo64va1YxRFITrMwpgB4VTUnvhxkm8B9Y3NJET4XbRJvlrPpTuTbLRLsnGO\nFDAukE6lXZ0lmwBPf1Jsw/HQm8i057C7de2YKHxMHT/p1sOkZ2hiCPERVnKPVbL3QBlb9hcS6m8m\nLMDc4e39XGfJpTuSbLRLsnGOFDAukE6lXZ0pm5Nrx8SSHDqY0oYyMuw5fJe/leaWZmKt0W5ZOybE\n34txSTZUIC3Pzvf7izheUkO8myf5dqZcuhvJRrskG+e0VcAoqqqqF/FcOkRJSbXbjh0cbHHr8UX7\ndeZs9pTs54Psjyk/UUGQZwDzE6+kX2Bvt7V3vKSGt9ZmkXu8Ei8PPXPH9+SyQRHodB0/ybcz59LV\nSTbaJdk4JzjYcs7X5ArMz0hVrF2dOZsw7xBSbMNxqA4y7NlsK9xFfk2B29aO8fU2MXpgOH4WD9IP\nlbMru4S0PDsxYZYOn+TbmXPp6iQb7ZJsnCO3kFwgnUq7Ons2Bp2BPgG9Tls7ZlP+VvSKjhjfHh2+\ndoyiKMSE+TJmQBjl1Sd+fOS6gIYmB/ERHbeSb2fPpSuTbLRLsnGO3EJygVzW066ulE2L2sLWwl2s\nzF1NTVMtYd6hXNPrShL8e7qtzX0Hy1i6LovSygaCrJ4smJLIwJ6BF3zcrpRLVyPZaJdk4xy5heQC\nqYq1qytlc3LtGBsptuHUOxrIKMtmS+EOSuvLiLNG46Hv+LVcQv3NjBtkQ1V/muRbSEFZLfGRVjxN\n7Z/k25Vy6WokG+2SbJwjt5BcIJ1Ku7piNia9kQFBfegX2Juj1cdJt2ezOX8bHnoPoiwRHb52jEGv\no29MAEMSgjlSVN16W8nsaSA6zNKu9rpiLl2FZKNdko1zpIBxgXQq7erK2fh5WEmxDcfX5ENWeS57\nStJIK8sg0mLDz8Pa4e35epsYMzAcq7eJ9MN2dmaVsP+QnbhwX3y9TS4dqyvn0tlJNtol2ThHChgX\nSKfSrq6ejaIoRPv2YGT4MGoaa0+u5Ju/ncoTVcRZYzB18JYEiqIQG+7L6AHh2KtOtO6r1NjUQk8X\nJvl29Vw6M8lGuyQb58gkXhfIxCrt6m7Z5JQf4L3slRTWFuFj9ObKntMZET7ULTtdA+w9UMrSddmU\nVZ2c5HvD1ET6x51/km93y6UzkWy0S7JxjkzidYFUxdrV3bIJ9ApgjG0EngZPMstz2F2yj6zyXKJ9\nI/E1nXtQt1dogJnxSTYcDpW0PDub9xdSaK8jIdIPT9O5Vw7ubrl0JpKNdkk2zpFbSC6QTqVd3TEb\nnaIjzhrDiLAh2BsqyLBn813+Nuqb64m1RmPUdewWAQa9jn6xAQxKCOJwUQ1peXY27snH28tAVOjZ\nJ/l2x1w6C8lGuyQb58gtJBfIZT3tkmxgf1kW72evpLS+DKvJwtyEWQwJSerwp5UAWlpUvtp9nA+/\nOUBDo4P4SCs3Tk0kItjntJ+TXLRLstEuycY5cgvJBVIVa5dkAyHmIMbYRqDT6cksz2Fn8R4OVh4m\nxrdHh+90rSgKcTZfUvqHU1bVwP4fJ/k2NbcQH2FF/+MkX8lFuyQb7ZJsnCO3kFwgnUq7JJuT9Do9\nvfx7MixkEMV1pWSUn9yS4ORO11EdvtO1l4eB4X1CiQ61kH2sgr0HytiWUUx4kDchfl6Si4ZJNtol\n2ThHbiG5QC7raZdkcyZVVdlTksYHOZ9QcaKSQE9/ru41mwFBfd3SXkNjMys35vHFjqOoKozqF8rv\nrh5MU4P8Q6xFMma0S7JxTlu3kKSA+RnpVNol2ZxbQ/MJ1h76ki+PfkuL2sKAoL5cnXAFgV4Bbmnv\ncGE1b63N5FBhNT5eRuZd1pMxA8PRuWEujmg/GTPaJdk4RwoYF0in0i7J5vzyawp5P3slORUHMeqM\npMZMYlLUuA5/WglOTvL9cucxVm46SP2Jkztc3zA1kcgQn/O/WVwUMma0S7JxjhQwLpBOpV2SjXNU\nVWV70W5W5H5KdWMNoeZg5ve6kt4BCW5pT2cy8MKy3ezMKkGvU5iS3IMrRsfi0cbaMeLikDGjXZKN\nc+QpJBfIxCrtkmycoygKET7hpIQPp7GlkfSybLYW7qSotphYazSeBs8ObS8owJt+UX7EhFnIOVbJ\n3gNlbNlfSIifmbBAc4e2JVwjY0a7JBvnuOUppEOHDuHn59fec7ogUsB0T5KNa4x6I/0Ce9M/qA/H\navLJ+HGna5PeRJQlosO2JPgpl7AAM+MG2QBIy7OzJb2II0XVJERa8fLo+FtY4vxkzGiXZOOctgqY\nNv8Fu/nmm0/7evHixa3//6GHHrrA0xJCXAxRlkj+OPQ2rk28Cp2iY3nOJzyx43kOVh7q8LY8jHrm\nju/Jn29Oplekld05pdy/ZCvrth3B0dLS4e0JIbqvNguY5ubm077esmVL6//vhFNnhOi2dIqOMREj\neWjknxgVnszxmgKe2bmYf2d8QE1jbYe3FxHswz3XD+Hm6b0xGnQs25DLX9/cwYHjlR3elhCie2qz\ngPn58uSnFi3uWLpcCOFeFpMPC/pczZ1DfkeETzibC7bz1y1P8d3xrbSoHXuFRFEUxg608bdbRzBm\nYDhHi2t4dOlO3l6bSW1DU4e2JYTofly6CS5FixBdQ0+/GO4Z9gfmJsyiWW3m3awPeWbnYo5WH+/w\ntixmE7+c3od7rhtMeJA3X/+Qz/2vbOH7/YVyJVcI0W5tzqyrrKzk+++/b/26qqqKLVu2oKoqVVVV\nbj85IYT76HV6JvYYy5CQgazI+ZSdxXt4YvvzjItMYVbcFLwMXh3aXmKUP3++OZl1246w6rtDLFmV\nzqa9BSyY0ovwwI7dx0kI0fW1uQ7MwoUL23zz0qVLO/yEnCHrwHRPko17ZdpzWJb9EcV1pVhMPlwV\nP5Pk0MHnvfLanlxKKur59xfZ7D1QhkGvMH1kNDNGRWM0yNoxHUnGjHZJNs6RhexcIJ1KuyQb92tq\naebLI9+y9tB6mlqaSfCL4xeJcwj3Dj3ne9qbi6qq7Mwq4d312VTUNBLi78XCKYn0i3XP9gfdkYwZ\n7ZJsnNPuhexqamp49913GTRoEADvvfce999/P99//z3JycmYzZdmkSpZB6Z7kmzcT6/oiPeLZVjo\nYMoaysiw57ApfyuNjkZirdEYzrLTdXtzURQFW5A345JsNDW3kJZXxua0QgrtdSREWPE0ydoxF0rG\njHZJNs5p90J29957LwaDgZSUFPLy8rjrrrt45JFH8PX15T//+Q+pqanuON/zkgKme5JsLh6z0Yth\noYPp4WPjYOVh9pdlsq1wFwFe/oSag0+7rXShuRgNOgbEBTIoPogjRdWk5dn5dk8BZg890aEWeXjg\nAsiY0S7JxjntXsju6NGj3HXXXQCsW7eO1NRUUlJSuOaaaygtLe3YsxRCaM7A4H48OOIupkZPpKqx\nmiX73ubFvW9QWl/W4W1Fh1m4f+EwFkzpBags/Tybvy3dyeFCucwuhDhTmwXMqbeItm3bxsiRI1u/\nlr+KhOgeTHoTV/RM5f8Nv4NE/3j2l2Xy8NZn+CzvC5ocHbuei06nMHFIJH+7dSTD+4SQV1DFX9/a\nzn/W51B/ovn8BxBCdBttFjAOh4OysjKOHDnC7t27GT16NAC1tbXU19dflBMUQmhDmHcIvx90Kzf3\nuw5vgxer877gb9ue5YeC9A5vy8/Hg9/M7s+dv0gi2OrFFzuO8sCrW9mZVSxrxwghgPPMgQkMDOSm\nm25i6dKl3HbbbaSkpNDQ0MC1117L3LlzGThw4EU81f+SOTDdk2Rz6SmKgs0njBTbCJpamkgvy2bj\n4a3k1xQSa43q8LVjQvzNjB9kQ6co7M+zszW9mEOF1cRHWDF7Gju0ra5Ixox2STbOaWsOzHkfo25q\nauLEiRP4+Pi0fm/Tplk03VcAACAASURBVE2MGTOm487QRfIYdfck2WjPsep8Vhz8hKyyg5h0RlJj\nJjExahxGXcc/QVRQVss7n2eTcbgck0HHFWNimZLcA4O+Y3bV7opkzGiXZOOcdq8Dk5+f3+aBbTZb\n+8/qAkgB0z1JNtoUFOTD6n3f8FHuaqqbagjxCuLqXrPpG5jY4W2pqsqW/UW8tyGH6romIoK8WTg1\nkV49/Dq8ra5Axox2STbOaXcB07t3b2JjYwkODgbO3Mzx7bff7sDTdJ4UMN2TZKNNP+VS11TP6rzP\n+ebYZlRUBgX3Z27CLAI8/Tu8zdqGJj78+gBf/3Dyj6wxA8O5+rKeWMymDm+rM5Mxo12SjXPaXcB8\n/PHHfPzxx9TW1jJjxgxmzpxJQMClXyVTCpjuSbLRpp/ncqw6n2XZKzlYeQjjj7eVJrnptlLu8Ure\nXpvFsZIafLyMzJ8Qz+gBYfKU5I9kzGiXZOOcC95KoKCggI8++ohVq1YRERHB7NmzmTx5Mp6enh16\nos6SAqZ7kmy06Wy5qKrKtsJdF+W2kqOlhS+2H+PjTXmcaHLQq4cfC6cmEhEkG0TKmNEuycY5HboX\n0gcffMDTTz+Nw+Fgx44dF3xy7SEFTPck2WhTW7lczNtKZZUNvLs+m905peh1CqkjopiZEoOHsftu\nECljRrskG+dccAFTVVXFJ598wooVK3A4HMyePZuZM2cSEhLSoSfqLClguifJRpucyeVYdT7vZ6/k\nwEW4rbQ7p4R/f5GNveoEQVZPFkxJZGDPwA5vpzOQMaNdko1z2l3AbNq0iQ8//JC0tDSmTJnC7Nmz\n6dWrl9MNZ2dn87vf/Y6bbrqJBQsWsH37dp599lkMBgNms5knn3wSq9XKq6++ytq1a1EUhUWLFjF+\n/Pg2jysFTPck2WiTs7m03lY6sJrqxpO3leb1mk0/N9xWamhs5pPvDvH5tqO0qCrDEoO59vJe+FvO\nvaZEVyRjRrskG+dc0FNIMTExJCUlodOdudbCY489ds4D19XV8etf/5qYmBgSExNZsGABV111FU8/\n/TRxcXG89NJL6HQ6pk2bxu233857771HTU0N1113HatXr0avP/dlXylguifJRptczaW+uZ7VB7/g\n62PfoaKSFNyfufGzCPTq+NtKR4treHtdJgeOV+Fp0jNnXBwTh0SgP8u/Z12RjBntkmyc01YB0+b1\n258eky4vL8ff//R/XI4dO9ZmoyaTiSVLlrBkyZLW7/n7+1NRUQFAZWUlcXFxbN26lbFjx2IymQgI\nCCAiIoLc3FwSEzv+rzIhxKXnZfBiXq8rGGVLZlnWR+wpSSO9LIvUmIlMihrfobeVeoT4cN+CoWzc\nk8/yrw/wn/U5bN5XyA2picSG+3ZYO0KIi6/NP0N0Oh133XUXDz74IA899BChoaEMHz6c7Oxs/vGP\nf/z/9u4zOsrr3vf4d5p6710aJAEGRO/dFGO6TTGYQHLvzcm9OSlrnVwnJ77EsZ3lOFn4JDdZiZ3u\n3JNjh4Dp1fQmML0joy4B6r3XmXnuC9vE2EaeYUbMHun/eYcy2s+e9ds7/uvZz352jw0bjcbP7VJa\nv3493/72t5k3bx6XL1/m2Wefpaam5oGt2WFhYVRXVzvxlYQQniA+IJbvjf5XvvrEKnyM3uwpPMjr\n539JVm2OS6+j1+mYMTKe178xkcnDYrhT2cxP/3aJdw/l0NYhB0QK4al6/FPnV7/6Ff/5n/9Jamoq\nR48e5eWXX8ZmsxEcHMyWLVscvthrr73Gm2++yZgxY9iwYQMbN2783Gfs2RQVGuqH0dh7Owt6umUl\n3EuyUZMzuSyKmsmswRN479YeDuSf5HfX32Zc/Ai+NmolUf6ue/g2MhL+z38P50Z+Nb/beoNjV0q5\nmlfDN5ZmMHVkXJ99d4zMGXVJNs7psYDR6/WkpqYCMHv2bH7+85/zwx/+kLlz5z7SxXJychgzZgwA\nkydPZs+ePUycOJGioqL7n6msrPzS3U319W2PdH17yLqkuiQbNbkql4WJ8xkZOpLNOTu4WHqda+Uf\nMi95FnOSpmMyuO7gxthgH17+2lgOnL/Dng/u8Ma7l9h3Joy1Tw0kOtTPZddRgcwZdUk29umpyOtx\nCemzf5HExsY+cvECEBERQX5+PgA3b94kOTmZiRMncuLECbq6uqisrKSqqoq0tLRHvoYQwnN9sqz0\ntSGr8TF6s7foIK9f+L9k1Wa79Domo57FU8y89i/jGWoOI6uojh//5QK7zxTRbbG59FpCiN7h0NNy\njtxivXXrFhs2bKC0tBSj0cjBgwf5yU9+wksvvYTJZCI4OJif/exnBAUF8dxzz7F27Vp0Oh2vvvrq\nF+54EkL0DzqdjvExo8mIeIJ9RYc5WfIBv7v+V0ZEDGV5+mLCfV13nEl0qB//+7kRXMyu4h9H8tiZ\nWcTZrErWPjWQoSnuPzZFCPFwPW6jzsjIIDz8n2vQtbW1hIeHo2kaOp2OEydOPI4+fo5so+6fJBs1\n9XYupS3lbM7ZSUFjESa9kXnJs12+rATQ1mFhx6lCjl0tQdNg/BNRrJqV7tHvjpE5oy7Jxj6P/B6Y\n0tLSHhuOj49/9F45QQqY/kmyUdPjyEXTNC5WXmVH/j6aupqJ8A3nuYFLGRo+2OXXulPRzH8dzKGo\n/KN3xzwz1czssQke+e4YmTPqkmzs49KzkFQgBUz/JNmo6XHm0m7pYH/RRy/Bs2k2hkcMZYWLl5UA\nbJrGqetlbDtRQGuHhYTIANbNG0h6QohLr9PbZM6oS7KxT08FjOHVV1999fF1xTXa2rp6rW1/f+9e\nbV88OslGTY8zF5PeyJDwQYyIHEp5ayW363I5XXYOTdNICUrEoHfN6xV0Oh0pMUFMHR5LS3s3t4rq\nOH2jnNrGDlITgj3mgEiZM+qSbOzj7//wJVy5A/MZUhWrS7JRk7ty+aJlpZXpSxgW8YTLr5VX0sA7\nB3MpqW7B38fI8pmpTB8Rh17xd8fInFGXZGMfuQPjAKmK1SXZqMldueh0OuIDYpkSNwGLzcLtulwu\nVl6lpLkMc1ASfiZfl10rPMiH6SNj8fcx8eGdeq7kVHOzsI6UmEBCAtR9yFfmjLokG/v0dAdGCpjP\nkEGlLslGTe7O5ZNlpZGRwyhvrei1ZSW9TkdqfDBThsXS0NJJVlEdp66X0dLWTVp8EKZefDv4o3J3\nNuLhJBv7yBKSA+S2nrokGzWplIumaVyqvMb2/L29vqyUVVzHu4dyqaxrI8jfi1VPpjFxaLRSRxKo\nlI14kGRjH1lCcoBUxeqSbNSkUi4PW1a611xKiouXlaJCfJkxIg4vo56s4jouZleRc7cBc1wQQX5e\nLruOM1TKRjxIsrGPLCE5QAaVuiQbNamYy6eXlSo+3q10puwcNs1GSlCSy5aVDHodAxNDmDgkmpqG\nDrKK6zh1rYzObitpccEYDe59d4yK2YiPSDb2kSUkB8htPXVJNmpSPZdPlpV25O+lsauZCJ8wVg5c\n2ivLSlfzqtl4OI/apg7Cgrx5fvZARg+McNuykurZ9GeSjX1kCckBUhWrS7JRk+q5fLKsNPmTZaX6\nvI+XlUpICUp26bJSbLg/M0bGodPBrcI6zt+upLiimQHxwfj7uPboA3uonk1/JtnYR5aQHCCDSl2S\njZo8JZfPLyvlcabsHFYXLysZDXqeSA5j3OAoymvbyCqq48TVMjRNY0Bc0GM9ksBTsumPJBv7yBKS\nA+S2nrokGzV5Yi6apnH5491KnywrrRi4hIyIIS6/zsXsKv5xNI/Gli6iQn1ZO3cgwwaEf/kvu4An\nZtNfSDb2kSUkB0hVrC7JRk2emItOpyPu42Ulq83K7fqPdivdbSrBHJyEn8nPZdeJjwxgxog4ui02\nsorq+CCrgtLqFlLjg/H1NrrkOg/jidn0F5KNfWQJyQEyqNQl2ajJk3Mx6Y08ET7w/rJSdn0ep8vO\nY7FZMLtwWclk1JMxIJyR6RGUVLdyq6iOk9fKMBr0pMQGotf3zkO+npxNXyfZ2EeWkBwgt/XUJdmo\nqa/komkal6uusyN/Hw2djYR6h7AsfRGjIjNcuovIpmmcuVHOlhMFtLR3Ex/hz7p5gxiY6PqTrvtK\nNn2RZGMfWUJygFTF6pJs1NRXcvloWSmGKXETAMipy+Ny1XXyG4pICkwg0CvAZddJjglk2og42jot\n3Cqs4/TNcqob2kmLD8bby3VHEvSVbPoiycY+cgfGAVIVq0uyUVNfzaWqrYZteXu4VXsbvU7P9PhJ\nLDQ/5dJt1wAFZY28czCHu5Ut+HkbWT5jADNGxrtkWamvZtMXSDb26ekOjBQwnyGDSl2SjZr6ei63\nam6zNW831e21BJj8WZL6NJNix6HXuW47tM2mcfxqKdtPFdDeaSUlJpB18wZhjg1yqt2+no0nk2zs\nI0tIDpDbeuqSbNTU13OJ8otkSvxEvPVe5DTkc636Flm12cQHxBLqE+ySa+h0OgbEBTE1I5am1i5u\nFdWReb2MptYu0hKC8XrEk677ejaeTLKxjywhOUCqYnVJNmrqT7k0dDayI38flyqvATAxZixL0+YT\n5PXwvxIfRfadet45lEN5bRuBfiaeezKNycNiHH6YuD9l42kkG/vIHRgHSFWsLslGTf0pFx+jD6Oi\nMhgUmsa95tKPDoksvYBJbyApMMFly0oRIb7MGBmHj5eBrOI6LmVXk32nnpTYIIL87T/puj9l42kk\nG/vIHRgHSFWsLslGTf01F6vNypmy8+wpPEibpZ0YvyhWDlzK4LB0l16ntrGDfxzN40puNXqdjrnj\nElgyxWzXS/D6azaeQLKxjzzE6wAZVOqSbNTU33Np6W5lT+FBzpSeR0NjZOQwlqUtItw3zKXXuVFQ\nw98P51Ld0EFooDerZ6czdlBkj8tK/T0blUk29pElJAfIbT11STZq6u+5eBm8yIh4goyIIZS1VnK7\nLpfTvXBIZHSYHzNGxKHX68gqquPC7SoKyppIjQsiwPeLT7ru79moTLKxjywhOUCqYnVJNmqSXP5J\n0zQuVl5lZ/4+GruaCfMJZXnaIkZEDnPp23wr69r4++FcbhXVYTTomD8hmYWTkvEyPVgsSTbqkmzs\nI0tIDpBBpS7JRk2Sy+d1WDo4UHyMY/cysWpWBoems3LgEmL8o112DU3TuJxTzT+O5lHf3ElEsA9r\nnxrI8NSI+5+RbNQl2dhHlpAcILf11CXZqEly+Tyj3sjgsHRGRw2nur2W2/W5nC47T7ulHXNwMia9\n86dQ63Q64iL8mT4iDptN41ZRHWezKrlb2UxafDB+PkbJRmGSjX1kCckBUhWrS7JRk+TSM03TuFnz\nIdvy9lDTUUegVwBLUxcwIWa0S9/mW1LdwrsHc8gtacTLpGfJFDNr5g+hob7VZdcQriPzxj6yhOQA\nGVTqkmzUJLnYp9vazdF7pzhQfIxuWzfmoCRWDlxKclCiy66haRof3KrgveP5NLd1kxAVwKpZaQxN\nce2OKOE8mTf2kQLGATKo1CXZqElycUx9RwPb8/dypeoGOnRMih3LktT5LjvtGqC1o5vtJws5ca0U\nTYOxgyJZNSud8GAfl11DOEfmjX2kgHGADCp1STZqklweTW59AVtyd1HWWoGv0YeF5qeYHj/JZduu\nARo7rbz53lUKSpvwMupZODmFp8cnYTK6bulKPBqZN/aRh3gdIA9WqUuyUZPk8mjCfcOYEjeeAFMA\nuQ2F3KjJ4np1FjH+US57CV5ibDCjUsOJDPEl914D1/NruXC7kqhQX6LD/FxyDfFoZN7YRx7idYBU\nxeqSbNQkuTivuauF3QUHOFt+EQ2N0VHDWZa2iFCfEKfa/XQ2bR0Wdp0u4ujlEmyaxsi0CFbPTiMq\nVAoZd5B5Yx+5A+MAqYrVJdmoSXJxnrfBi+GRQxgaPpjSlgpu1+WSWXoOTYOUoMRHXlb6dDYmo56M\nAeGMHhRJWU0rWcV1nLhWhsVqY0BcEEaDLCs9TjJv7CN3YBwgVbG6JBs1SS6uZdNsXKi4ws6C/TR3\ntRDhE8by9MVkRAxx+G2+D8tG0zQuZlex+Vg+9c2dhAf5sHp2OqMHRrj0jcHi4WTe2EfuwDhAqmJ1\nSTZqklxcS6fTkRAYx5S48VhtNm7X53Kp8hrFTfdIDkwgwMvf7rYelo1OpyM+MoAZI+OwaRpZRXWc\nv11JQVkT5thAAv28XPmVxBeQeWMfuQPjAKmK1SXZqEly6V0VrZVsyd1Ndn0eBp2BJxOnMj9lNj7G\nL98SbW825bWtbDySR1ZRHQa9jqfGJbJ4Sgo+Xs6/MVh8MZk39pFt1A6QQaUuyUZNkkvv0zSN6zVZ\nbM/bQ21HPcFegTyTtpBx0aN6XPJxJBtN07iaV8M/juRR29RBSIAXq2alM/6JKFlW6gUyb+wjS0gO\nkNt66pJs1CS59D6dTkeMfxRT4iZi0BvIqc/jStUNsuvzSQyMI9g76At/z5FsdDodseH+zBgZh0Gv\nI6u4novZVeTcbSAlNpAgf1lWciWZN/aRJSQHSFWsLslGTZLL41fbXsf2/H1cq76JDh1T4ieweMA8\nAkwPPh/jTDZVDe1sOpLHtfwa9Dods8bE88xUM34+Jld8hX5P5o19ZAnJATKo1CXZqElycZ/sujy2\n5O6ioq0KP6MviwfMY0rchPvbrl2RzY2CGjYezqOqoZ0gPxMrn0xj0rAY9LKs5BSZN/aRAsYBMqjU\nJdmoSXJxL6vNysmSM+wrOkKHtYP4gFieG/gMaSFml2XTbbFy8MI99n5QTJfFRmp8EGvnDiI55uH/\ncRE9k3ljHylgHCCDSl2SjZokFzU0dTWzq+B9zpVfAmBs9Ej+ZfwqrK2uO1uptrGDzcfzuZRdhQ6Y\nMSqeZdMHEOAry0qOknljHylgHCCDSl2SjZokF7UUNd5lS+4u7jTfw9vgxVPJTzIrcTpeBtcVGR8W\n1/H3w7mU17bh72Nk+YxUpo+IQ6+XZSV7ybyxjxQwDpBBpS7JRk2Si3psmo1z5ZfYW3SQxs5mwnxC\neTZtIaMiM1y2JdpitXHkUgm7zhTR2WUlOSaQtXMHkhof7JL2+zqZN/aRAsYBMqjUJdmoSXJRl3+I\nkXcv7eL4vdNYNSupwWZWDlxCYmC8y67R0NLJluP5nM2qBGBqRiwrZqbKtusvIfPGPvIeGAfI3nx1\nSTZqklzUFRLoT5JPMmOiR1Lf0UB2fS5nyi5Q39FASnAS3oaHv2PDXj5eRsYMiuKJ5FCKK5q5VVTH\nyetleJv0JMcEym6lh5B5Yx95D4wDpCpWl2SjJslFXZ/NJrsuj215eyhrrcDH4M3TKbOZmTgVk941\nRwZYbTZOXC1jx6lC2jotJET685W5AxmUFOqS9vsSmTf2kSUkB8igUpdkoybJRV1flI3VZuVM2Xn2\nFh2itbuNCN9wlqUtYvgjnHb9ME2tXWw7WUDmjXIAJg6JZuWTaYQGOn/Hp6+QeWMfKWAcIINKXZKN\nmiQXdfWUTVt3G/uLjnCy9ANsmo3BoeksT19MXECMy65fUNbI3w/lUlzRjLeXgaVTzMwZm4DRoHfZ\nNTyVzBv7SAHjABlU6pJs1CS5qMuebCpaK9mWt5cP63LQoWNa/EQWmp8iwMu/x9+zl82mkXmjjG0n\nC2lp7yY23I81cwYy1BzmkvY9lcwb+0gB4wAZVOqSbNQkuajLkWxu1dxme/5eKtuq8TX6stA8l+nx\nk+4fS+CslvZudmQWcuJqKZoGYwZFsnpWOuHBPi5p39PIvLGPFDAOkEGlLslGTZKLuhzNxmKzcKr0\nLPuLDtNu6SDaL4rl6YsZGj7IZX26U9HM3w/nkl/aiJdRz8LJKTw9PhGT0XVvDPYEMm/sIwWMA2RQ\nqUuyUZPkoq5Hzaa5q4W9RYc4U3oeDY2h4YNZnraIaP8ol/RL0zQ+uFXBlhMFNLV2ERXiy/Nz0hmR\nFuGS9j2BzBv7SAHjABlU6pJs1CS5qMvZbEpbytmat4fc+nz0Oj0zEiazIGUOfiY/l/SvrcPC7jNF\nHLlUgk3TGJEazvNz0okKdU37KpN5Yx+3vcguNzeXVatWodfrGT58ON3d3fz7v/87f/7zn9m3bx+z\nZs3Cx8eH3bt3s379erZu3YpOp2Po0KE9tisvsuufJBs1SS7qcjabIK9AJsSMJiEwjuLGu3xYl8MH\n5RfwMXqTEBCHXufcbiKTUc+wAeGMGRRJeW0rWcX1nLhWRrfVxoC4oD69W0nmjX16epFdrxUwbW1t\n/OAHPyAjI4OIiAiGDx/Opk2b6Ojo4M0336Srq4uGhgZiYmJ44YUX2LhxIytWrOBHP/oRCxYswMfn\n4Q92SQHTP0k2apJc1OWKbHQ6HTH+UUyNn4i3wYvc+gKuV2dxvTqLaL9IInyd300U5O/F5GExxEX4\nk1fSyI2CWs5lVRAe5ENsuJ/L3k+jEpk39nFLAaPT6Vi0aBE5OTn4+voyfPhwfvOb3/DVr36V6Oho\nhg0bxoABA7h06RK1tbUsXrwYo9FIdnY23t7emM3mh7YtBUz/JNmoSXJRlyuzMej0pIaYmRg7jjZL\nO9l1eZyvuExpcxlJgYn4O7mspNPpiI8MYMbIODQNsorqOH+7ioLSRsyxQQT69a2zlWTe2KenAsY1\n74/+ooaNRozGB5svLS3l1KlT/Md//AcRERG88sor1NTUEBb2zwo+LCyM6urqHtsODfXD2ItPrPe0\n5ibcS7JRk+SiLldnE0kg30v4HxTWzeb/Xd3C9ZossupyWDhwFsuGzMfX5Py26H9dGcqSmWn8acdN\nruRU8fLbF1g6PZVVcwfi52NywbdQg8wb5/RaAfNFNE3DbDbzne98h9/97nf88Y9/ZMiQIZ/7zJep\nr2/rrS7Kg1UKk2zUJLmoqzezCSSM72b8T65UXWdH/n52ZR/iWOEHLBkwn4mxY5x+PsYL+PYzQ7ma\nF80/juSx/UQ+Ry/eZcXMVCYNi/H4QyJl3tinpyLvsT4hFRERwbhx4wCYOnUq+fn5REVFUVNTc/8z\nVVVVREW5ZqueEEKI3qPT6RgTPZKXJ36fhea5dFo6+Xv2Ft649FvyG4pc0v7ogZG8/o0JLJ1qpr3T\nwtv7bvOzdy5TUNbogm8gPNljLWCmT59OZmYmAFlZWZjNZkaMGMHNmzdpamqitbWVK1euMHbs2MfZ\nLSGEEE7wMnixwDyXlyf+gHHRo7jXXMqvrvyev976O3Ud9c63bzKwdKqZ178xkXGDoygsa+L1/7rM\n23s/pKGl0wXfQHiiXnsPzK1bt9iwYQOlpaUYjUaio6P5xS9+weuvv051dTV+fn5s2LCBiIgIDhw4\nwNtvv41Op2Pt2rUsWbKkx7blPTD9k2SjJslFXe7KpqjxDlvydnOn6R4mvZE5STOYm/wk3gbXPIib\nc7eejUfyuFfVgreXgSWTU5gzNhGT0XO2Xcu8sY+8yM4BMqjUJdmoSXJRlzuzsWk2LlZcZVfBfhq7\nmgnxDmZp6nzGRY9yybZom03j5PUydpz66JDIqFBfVs9KZ0RauEdsu5Z5Yx8pYBwgg0pdko2aJBd1\nqZBNh6WTw3eOc+TeKSw2C+agJFYMXEJKUJJL2m/t6GZXZhHHrpRi0zSGmcN4fk46seGuOU27t6iQ\njSeQAsYBMqjUJdmoSXJRl0rZ1LTXsTN/H1erbwIwPmY0S1PnE+Id7JL2S6tb+MfRPD4srseg1zF7\nTAJLppjx83msm23tplI2KpMCxgEyqNQl2ahJclGXitnk1RewNW8PJS1leOlNzEuZxazE6XgZnH+/\ni6ZpXM2rYdPRPGoaOwj0M7F8RipTM2LR69VaVlIxGxVJAeMAGVTqkmzUJLmoS9VsbJqNs2UX2V14\ngJbuVsJ8Qnk2bSGjIjNc8vxKt8XKwQv32Hf2Dp3dVpKjA1kzN530hBAX9N41VM1GNVLAOEAGlbok\nGzVJLupSPZt2SzvvFx/lxL0zWDUraSFmVqQvITEw3iXt1zd3svVEPmezKgGYOCSaFTNTCQty/m3B\nzlI9G1VIAeMAGVTqkmzUJLmoy1OyqWqrZnv+Pm7WfIgOHZNix7E4dR5BXq551X5+SSN/P5LLnYpm\nvEx6Fk5K4enxiZh68UiaL+Mp2bibFDAOkEGlLslGTZKLujwtm9t1uWzL20N5ayU+Bm+eTpnNzMSp\nmPTOP4hr0zTO3Chn28kCmtq6iQj2YdWsdEYPjHDLtmtPy8ZdpIBxgAwqdUk2apJc1OWJ2VhtVk6X\nnWdf4SFaLW1E+IazLG0RwyOGuKTQaOuwsOeDIo5cKsFq03giOZQ1c9KJjwxwQe/t54nZuIMUMA6Q\nQaUuyUZNkou6PDmb1u429hcd5lTpWWyajcGh6SxLX0R8QKxL2i+vbWXT0XxuFtai1+l4cnQ8z0wz\n4/+YTrv25GweJylgHCCDSl2SjZokF3X1hWzKWyvZlreH23W56NAxOW4cC83zCPZ2zfMx1/M/2nZd\nWd9OgK+JZ6eZmTEyvte3XfeFbB4HKWAcIINKXZKNmiQXdfWVbDRN48O6HLbn7aWirQpvgxdPJT/p\nsvfHWKw2Dl+6x54zxXR0WUmIDOArc9MZlBTqgt5/sb6STW+TAsYBMqjUJdmoSXJRV1/Lxmqzcqbs\nAvuKDtHS3UqodwhLUp9mbPRI9DrnD3JsbOlk28lCTt8sB2Ds4CieezKViGBfp9v+rL6WTW+RAsYB\nMqjUJdmoSXJRV1/Npt3SzsHi4xy/l4lFs5IcmMiy9EWkhZhd0n5ReRMbD+dSUNaEyahn/oQk5k9M\nxtvkum3XfTUbV5MCxgEyqNQl2ahJclFXX8+mpr2O3QXvc7nqOgAjIzN4JnUBkX7hTrdt0zTOZVWw\n5UQBjS1dhAd5s/LJNMYNjnLJbqi+no2rSAHjABlU6pJs1CS5qKu/ZFPYeIfteXsoarqLQWdgZsIU\nnk6ZhZ/Jz+m22zst7Dt7h0MX72KxagxKDOH5OekkRTv3EHF/ycZZUsA4QAaVuiQbNUku6upP2Wia\nxpWq6+wseJ+6P8AabAAAFVpJREFUjnr8jX4sMM9lWvxEDHrnl34q69vYfDSfa/k16HQwY2Q8z04z\nE+jn9Ujt9adsnCEFjANkUKlLslGT5KKu/phNt7Wb4yWnOVh8jA5rJ9F+kTybtpBh4U+4ZOnnVlEt\n/ziSR3ltG37eRp6ZZubJ0fEY9I49RNwfs3kUUsA4QAaVuiQbNUku6urP2TR3tbCv6DCnS8+hoTEw\nNI1laYtIDIxzum2L1caxK6XsOl1Ee6eF+Ah/np+TzpCUMLvb6M/ZOEIKGAfIoFKXZKMmyUVdkg2U\ntVSwo2AfH9bmoEPHhNgxLB4wjxDvYKfbbmrrYvvJQjKvl6EBowdG8tysNKJCvnzbtWRjHylgHCCD\nSl2SjZokF3VJNv90uzaX7fl7KWutwEtvYm7yTGYnzcDb8GjPsHzanYpmNh7JJa+kEaNBz7zxiSyc\nlIyP18MPoZRs7CMFjANkUKlLslGT5KIuyeZBNs3G2bKL7Ck6SHNXCyHewSwZ8DTjYkY5/SI8TdO4\ncLuK947nU9/cSUiAFyufTGPikOgvfPZGsrGPFDAOkEGlLslGTZKLuiSbL9Zh6eDwnRMcvXeKbpuF\nxMB4lqUtYmBoqtNtd3ZZ2X/uDu+fv4vFaiMtPpg1c9NJiQl64HOSjX2kgHGADCp1STZqklzUJdn0\nrK6jnt0FB7hYeRWAERFDeSZtAVF+kU63XdPQzubj+VzOqUYHTB0ey/IZqQT5f7RkJdnYRwoYB8ig\nUpdkoybJRV2SjX2Km+6yLW8vhY3F6HV6ZsRPZr55Dv4ueBHe7Tv1bDySS2l1K77eBpZMMTN7TAKx\nMcGSjR2kgHGATHh1STZqklzUJdnYT9M0rlXfYmf+Pmo66vAz+jLfPIfp8ZMw6h/+MK49rDYbJ6+V\nseNUIa0dFmLC/Phfy4aTHOF8gdTXSQHjAJnw6pJs1CS5qEuycVy3zcLJkjMcKD5Ku6WDSN9wnk1b\nyPCIoU6/CK+lvZudmYUcv1qKpsEwcxirZqURHxngot73PVLAOEAmvLokGzVJLuqSbB5dS1cr+4uP\nkFl6FptmIy3EzPK0xSQFJTjddkl1C9szi7iWW33/WIJnppkJesRjCfo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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "pZa8miwu6_tQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "PzABdyjq7IZU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n", + "\n", + "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`." + ] + }, + { + "metadata": { + "id": "xdVF8siZ7Lup", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U4iAdY6t7Pkh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "72e055ff-0462-458e-cc25-01f490c29bc7" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 229.10\n", + " period 01 : 218.84\n", + " period 02 : 208.65\n", + " period 03 : 198.57\n", + " period 04 : 188.59\n", + " period 05 : 178.75\n", + " period 06 : 169.08\n", + " period 07 : 159.59\n", + " period 08 : 150.32\n", + " period 09 : 141.30\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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0m66yfbv2l/H2qizKqhoICzJzW0oiid0DO6RtT5K/WLRJctEuyUa7JBvnXJTH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\nL4PM1Ei3XL8/0bhxjqaQXKBOZS5lYyblYq4vy8bhcHCs/CS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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..349fc8b --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,965 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "first_steps_with_tensor_flow.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ajVM7rkoYXeL", + "ci1ISxxrZ7v0" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# First Steps with TensorFlow" + ] + }, + { + "metadata": { + "id": "Bd2Zkk1LE2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Learn fundamental TensorFlow concepts\n", + " * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n", + " * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n", + " * Improve the accuracy of a model by tuning its hyperparameters" + ] + }, + { + "metadata": { + "id": "MxiIKhP4E2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California." + ] + }, + { + "metadata": { + "id": "6TjLjL9IU80G", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "In this first cell, we'll load the necessary libraries." + ] + }, + { + "metadata": { + "id": "rVFf5asKE2Zt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ipRyUHjhU80Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll load our data set." + ] + }, + { + "metadata": { + "id": "9ivCDWnwE2Zx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vVk_qlG6U80j", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use." + ] + }, + { + "metadata": { + "id": "r0eVyguIU80m", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "HzzlSs3PtTmt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Examine the Data\n", + "\n", + "It's a good idea to get to know your data a little bit before you work with it.\n", + "\n", + "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles." + ] + }, + { + "metadata": { + "id": "gzb10yoVrydW", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.describe()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Lr6wYl2bt2Ep", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Build the First Model\n", + "\n", + "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n", + "\n", + "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n", + "\n", + "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference." + ] + }, + { + "metadata": { + "id": "0cpcsieFhsNI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 1: Define Features and Configure Feature Columns" + ] + }, + { + "metadata": { + "id": "EL8-9d4ZJNR7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n", + "\n", + "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n", + "\n", + "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n", + "\n", + "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n", + "\n", + "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:" + ] + }, + { + "metadata": { + "id": "rhEbFCZ86cDZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the input feature: total_rooms.\n", + "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n", + "\n", + "# Configure a numeric feature column for total_rooms.\n", + "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "K_3S8teX7Rd2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument." + ] + }, + { + "metadata": { + "id": "UMl3qrU5MGV6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 2: Define the Target" + ] + }, + { + "metadata": { + "id": "cw4nrfcB7kyk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:" + ] + }, + { + "metadata": { + "id": "l1NvvNkH8Kbt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the label.\n", + "targets = california_housing_dataframe[\"median_house_value\"]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4M-rTFHL2UkA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 3: Configure the LinearRegressor" + ] + }, + { + "metadata": { + "id": "fUfGQUNp7jdL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n", + "\n", + "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. " + ] + }, + { + "metadata": { + "id": "ubhtW-NGU802", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Use gradient descent as the optimizer for training the model.\n", + "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n", + "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + "\n", + "# Configure the linear regression model with our feature columns and optimizer.\n", + "# Set a learning rate of 0.0000001 for Gradient Descent.\n", + "linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-0IztwdK2f3F", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 4: Define the Input Function" + ] + }, + { + "metadata": { + "id": "S5M5j6xSCHxx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n", + "the data, as well as how to batch, shuffle, and repeat it during model training.\n", + "\n", + "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n", + "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n", + "\n", + "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n", + "\n", + "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n", + "the size of the dataset from which `shuffle` will randomly sample.\n", + "\n", + "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor." + ] + }, + { + "metadata": { + "id": "RKZ9zNcHJtwc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wwa6UeA1V5F_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** We'll continue to use this same input function in later exercises. For more\n", + "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)." + ] + }, + { + "metadata": { + "id": "4YS50CQb2ooO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 5: Train the Model" + ] + }, + { + "metadata": { + "id": "yP92XkzhU803", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n", + "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n", + "train for 100 steps." + ] + }, + { + "metadata": { + "id": "5M-Kt6w8U803", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = linear_regressor.train(\n", + " input_fn = lambda:my_input_fn(my_feature, targets),\n", + " steps=100\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7Nwxqxlx2sOv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 6: Evaluate the Model" + ] + }, + { + "metadata": { + "id": "KoDaF2dlJQG5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's make predictions on that training data, to see how well our model fit it during training.\n", + "\n", + "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n" + ] + }, + { + "metadata": { + "id": "pDIxp6vcU809", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "calibration_data = pd.DataFrame()\n", + "calibration_data[\"predictions\"] = pd.Series(predictions)\n", + "calibration_data[\"targets\"] = pd.Series(targets)\n", + "calibration_data.describe()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "E2-bf8Hq36y8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n", + "\n", + "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n", + "\n", + "First, we'll get a uniform random sample of the data so we can make a readable scatter plot." + ] + }, + { + "metadata": { + "id": "SGRIi3mAU81H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "sample = california_housing_dataframe.sample(n=300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "N-JwuJBKU81J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red." + ] + }, + { + "metadata": { + "id": "7G12E76-339G", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Get the min and max total_rooms values.\n", + "x_0 = sample[\"total_rooms\"].min()\n", + "x_1 = sample[\"total_rooms\"].max()\n", + "\n", + "# Retrieve the final weight and bias generated during training.\n", + "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n", + "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + "# Get the predicted median_house_values for the min and max total_rooms values.\n", + "y_0 = weight * x_0 + bias \n", + "y_1 = weight * x_1 + bias\n", + "\n", + "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n", + "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n", + "\n", + "# Label the graph axes.\n", + "plt.ylabel(\"median_house_value\")\n", + "plt.xlabel(\"total_rooms\")\n", + "\n", + "# Plot a scatter plot from our data sample.\n", + "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n", + "\n", + "# Display graph.\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "t0lRt4USU81L", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This initial line looks way off. See if you can look back at the summary stats and see the same information encoded there.\n", + "\n", + "Together, these initial sanity checks suggest we may be able to find a much better line." + ] + }, + { + "metadata": { + "id": "AZWF67uv0HTG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Tweak the Model Hyperparameters\n", + "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n", + "\n", + "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n", + "\n", + "For each period, we'll compute and graph training loss. This may help you judge when a model is converged, or if it needs more iterations.\n", + "\n", + "We'll also plot the feature weight and bias term values learned by the model over time. This is another way to see how things converge." + ] + }, + { + "metadata": { + "id": "wgSMeD5UU81N", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]]\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label]\n", + "\n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Output a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kg8A4ArBU81Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Achieve an RMSE of 180 or Below\n", + "\n", + "Tweak the model hyperparameters to improve loss and better match the target distribution.\n", + "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination." + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00001,\n", + " steps=100,\n", + " batch_size=1\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ajVM7rkoYXeL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "T3zmldDwYy5c", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=500,\n", + " batch_size=5\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "M8H0_D4vYa49", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality." + ] + }, + { + "metadata": { + "id": "QU5sLyYTqzqL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Is There a Standard Heuristic for Model Tuning?\n", + "\n", + "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n", + "\n", + "That said, here are a few rules of thumb that may help guide you:\n", + "\n", + " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n", + " * If the training has not converged, try running it for longer.\n", + " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n", + " * But sometimes the exact opposite may happen if the learning rate is too high.\n", + " * If the training error varies wildly, try decreasing the learning rate.\n", + " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n", + " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n", + "\n", + "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify." + ] + }, + { + "metadata": { + "id": "GpV-uF_cBCBU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Feature\n", + "\n", + "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n", + "\n", + "Don't take more than 5 minutes on this portion." + ] + }, + { + "metadata": { + "id": "YMyOxzb0ZlAH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ci1ISxxrZ7v0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "SjdQQCduZ7BV", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=1000,\n", + " batch_size=5,\n", + " input_feature=\"population\"\n", + ")" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb new file mode 100644 index 0000000..46868bb --- /dev/null +++ b/improving_neural_net_performance.ipynb @@ -0,0 +1,2001 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "improving_neural_net_performance.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "jFfc3saSxg6t", + "FSPZIiYgyh93", + "GhFtWjQRzD2l", + "P8BLQ7T71JWd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "cellView": "both", + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "eV16J6oUY-HN" + }, + "cell_type": "markdown", + "source": [ + "# Improving Neural Net Performance" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "0Rwl1iXIKxkm" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n", + "\n", + "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lBPTONWzKxkn" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, we'll load the data." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "VtYVuONUKxko", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "B8qC-jTIKxkr", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ah6LjMIJ2spZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "b1e1f29a-5111-4d43-818f-640bb31dddab" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "NqIbXxx222ea" + }, + "cell_type": "markdown", + "source": [ + "## Train the Neural Network\n", + "\n", + "Next, we'll train the neural network." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6k3xYlSg27VB", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "De9jwyy4wTUT", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural network model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "W-51R3yIKxk4", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " my_optimizer,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A tuple `(estimator, training_losses, validation_losses)`:\n", + " estimator: the trained `DNNRegressor` object.\n", + " training_losses: a `list` containing the training loss values taken during training.\n", + " validation_losses: a `list` containing the validation loss values taken during training.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor, training_rmse, validation_rmse" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KueReMZ9Kxk7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 775 + }, + "outputId": "45fd70f1-cd09-40b6-8584-499e60c3b822" + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 167.12\n", + " period 01 : 141.43\n", + " period 02 : 124.62\n", + " period 03 : 112.03\n", + " period 04 : 106.84\n", + " period 05 : 103.49\n", + " period 06 : 104.53\n", + " period 07 : 102.56\n", + " period 08 : 102.75\n", + " period 09 : 101.98\n", + "Model training finished.\n", + "Final RMSE (on training data): 101.98\n", + "Final RMSE (on validation data): 103.87\n" + ], + "name": "stdout" + }, + { + "output_type": 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QkA8ZGVf3CHRjHzeah/qwefdpdh9MI8TfEzDRJzSaDae+Z8XeTfQM6WabwNeR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\nNjajNZ5vXQwjn2exkQAzDrJTaZfURpuysx2cbGhn/4l26o6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "flxmFt0KKxk9" + }, + "cell_type": "markdown", + "source": [ + "## Linear Scaling\n", + "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Dws5rIQjKxk-", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def linear_scale(series):\n", + " min_val = series.min()\n", + " max_val = series.max()\n", + " scale = (max_val - min_val) / 2.0\n", + " return series.apply(lambda x:((x - min_val) / scale) - 1.0)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "MVmuHI76N2Sz" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Normalize the Features Using Linear Scaling\n", + "\n", + "**Normalize the inputs to the scale -1, 1.**\n", + "\n", + "**Spend about 5 minutes training and evaluating on the newly normalized data. How well can you do?**\n", + "\n", + "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n", + "\n", + "Sanity check your normalized data. (What would happen if you forgot to normalize one feature?)\n" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yD948ZgAM6Cx", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "b0431207-8503-4cc2-c622-4df5a6e0bd17" + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " #\n", + " # Your code here: normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + " pass\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=3000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 232.32\n", + " period 01 : 222.96\n", + " period 02 : 206.05\n", + " period 03 : 180.28\n", + " period 04 : 147.49\n", + " period 05 : 120.61\n", + " period 06 : 116.72\n", + " period 07 : 114.44\n", + " period 08 : 112.05\n", + " period 09 : 109.52\n", + "Model training finished.\n", + "Final RMSE (on training data): 109.52\n", + "Final RMSE (on validation data): 112.12\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Vm4XZLD29ijNpZ7E3teMF7+AqOe3akHNTk0leDJfkxnBJbipGCpgHUBW+VCUl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\n2IANGzaEtz3++ONQKoP9XpPJhKKiIhw5cgS5ubnQ64N9run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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "jFfc3saSxg6t" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ax_IIQVRx4gr" + }, + "cell_type": "markdown", + "source": [ + "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n", + "\n", + "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "D-bJBXrJx-U_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "8e5ac34f-de64-427a-de66-470d34cd740f" + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n", + " steps=2000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 157.18\n", + " period 01 : 112.32\n", + " period 02 : 99.94\n", + " period 03 : 84.12\n", + " period 04 : 76.29\n", + " period 05 : 73.53\n", + " period 06 : 71.91\n", + " period 07 : 70.82\n", + " period 08 : 70.18\n", + " period 09 : 69.81\n", + "Model training finished.\n", + "Final RMSE (on training data): 69.81\n", + "Final RMSE (on validation data): 71.25\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RmRALTDMK89FCbxARdUaHHh26w0GpwgldLKoN1VJHIyIialNYYJpRf69rz0aS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\nERHlHQYYIiIiyjsMMERERJR3GGCIiIgo7zDAEBERUd5hgCE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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "MrwtdStNJ6ZQ" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Optimizer\n", + "\n", + "** Use the Adagrad and Adam optimizers and compare performance.**\n", + "\n", + "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n", + "\n", + "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "61GSlDvF7-7q", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 911 + }, + "outputId": "0cfc2814-4ddd-4fe6-e030-3ee97ac6316d" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.1),\n", + " steps=1000,\n", + " batch_size=200,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)\n", + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.005),\n", + " steps=1000,\n", + " batch_size=200,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)\n", + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 107.26\n", + " period 01 : 78.52\n", + " period 02 : 71.75\n", + " period 03 : 70.92\n", + " period 04 : 70.49\n", + " period 05 : 70.03\n", + " period 06 : 69.54\n", + " period 07 : 69.18\n", + " period 08 : 69.00\n", + " period 09 : 68.74\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.74\n", + "Final RMSE (on validation data): 70.20\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 198.02\n", + " period 01 : 117.48\n", + " period 02 : 109.61\n", + " period 03 : 100.25\n", + " period 04 : 85.25\n", + " period 05 : 72.95\n", + " period 06 : 69.79\n", + " period 07 : 69.08\n", + " period 08 : 68.51\n", + " period 09 : 68.23\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.23\n", + "Final RMSE (on validation data): 69.71\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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c/rp8ztixhBBClBPBwa/TsGHjR9omPv46YWFbAejWrSft2gUaIlq5Jo9tP4aG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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "FSPZIiYgyh93" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "X1QcIeiKyni4" + }, + "cell_type": "markdown", + "source": [ + "First, let's try Adagrad." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ntn4jJxnypGZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "339285ef-4a9f-4ada-965e-d12ecc9ccbe2" + }, + "cell_type": "code", + "source": [ + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 78.16\n", + " period 01 : 73.32\n", + " period 02 : 71.93\n", + " period 03 : 70.67\n", + " period 04 : 69.97\n", + " period 05 : 68.96\n", + " period 06 : 69.73\n", + " period 07 : 68.02\n", + " period 08 : 66.82\n", + " period 09 : 67.30\n", + "Model training finished.\n", + "Final RMSE (on training data): 67.30\n", + "Final RMSE (on validation data): 68.71\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6ZttO7joBrUZLfNoOjErTE3CFEEII0TgpVK6SRqMhZlgXDEaF7Qezmm3r6+zN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\nG4zsOpxttX6HBUTy5LAHCXD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DEG1MgLcLf7tnJJt/TOWLLT+zMSGdfSfy+P2kCxNwbX05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\nGYR0HvWSbNTLXrKpaujgwIky0nOqMVsUfD0cWTwjjPlTgnE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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "5JUsCdRRyso3" + }, + "cell_type": "markdown", + "source": [ + "Now let's try Adam." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "lZB8k0upyuY8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "a0554177-5bd9-4c5b-d934-d6f93352e24d" + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 194.59\n", + " period 01 : 121.71\n", + " period 02 : 113.75\n", + " period 03 : 106.86\n", + " period 04 : 93.56\n", + " period 05 : 78.49\n", + " period 06 : 73.15\n", + " period 07 : 70.83\n", + " period 08 : 69.88\n", + " period 09 : 69.11\n", + "Model training finished.\n", + "Final RMSE (on training data): 69.11\n", + "Final RMSE (on validation data): 70.45\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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3FjBdoHUaqbm8pQUCp5GIiIhsiwVMF4i6WMBknqqHv5sfsgynUNtUZ+eoiIiI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\nUnMo0RuzrfY4U0ryKCso4D0lC4jFY7zTUsOmY1s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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "twYgC8FGyxm6" + }, + "cell_type": "markdown", + "source": [ + "Let's print a graph of loss metrics side by side." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8RHIUEfqyzW0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376 + }, + "outputId": "3c85af20-e3dd-40c1-d07a-ef8045f8d0e3" + }, + "cell_type": "code", + "source": [ + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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rnGazmU6dujB79k089tjD7P9TruLjj/HEE8/g5eXNjBmTKC4u5t133+Rvf7uN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\nPUVwiBuFeWUUFZTZOywhhBB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SPKtD8qwOyXPjP2RULeSbNm3C0dGRe+65x7otPDycxYsX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\n9bQQddV+SquSKMFOOpxmdnfvoyPVOWC//CvlZ0dqqIvWTLo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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "UySPl7CAQ28C" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Explore Alternate Normalization Methods\n", + "\n", + "**Try alternate normalizations for various features to further improve performance.**\n", + "\n", + "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n", + "\n", + "For example, many features have a median of `-0.8` or so, rather than `0.0`." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "QWmm_6CGKxlH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 + }, + "outputId": "46d18226-c36a-4ffb-c8db-8a41a0c21e4a" + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Xx9jgEMHKxlJ" + }, + "cell_type": "markdown", + "source": [ + "We might be able to do better by choosing additional ways to transform these features.\n", + "\n", + "For example, a log scaling might help some features. Or clipping extreme values may make the remainder of the scale more informative." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "baKZa6MEKxlK", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def log_normalize(series):\n", + " return series.apply(lambda x:math.log(x+1.0))\n", + "\n", + "def clip(series, clip_to_min, clip_to_max):\n", + " return series.apply(lambda x:(\n", + " min(max(x, clip_to_min), clip_to_max)))\n", + "\n", + "def z_score_normalize(series):\n", + " mean = series.mean()\n", + " std_dv = series.std()\n", + " return series.apply(lambda x:(x - mean) / std_dv)\n", + "\n", + "def binary_threshold(series, threshold):\n", + " return series.apply(lambda x:(1 if x > threshold else 0))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "-wCCq_ClKxlO" + }, + "cell_type": "markdown", + "source": [ + "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n", + "\n", + "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8ToG-mLfMO9P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "cb31ac49-94d3-43cb-8d8e-ff22d19e6a0c" + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " #\n", + " # YOUR CODE HERE: Normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 210.88\n", + " period 01 : 132.74\n", + " period 02 : 113.62\n", + " period 03 : 111.86\n", + " period 04 : 109.83\n", + " period 05 : 107.25\n", + " period 06 : 104.09\n", + " period 07 : 99.79\n", + " period 08 : 95.01\n", + " period 09 : 90.05\n", + "Model training finished.\n", + "Final RMSE (on training data): 90.05\n", + "Final RMSE (on validation data): 92.18\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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M9EiKobTcQ/vQRAoqCzlQcijQZYmIiJwRFGBOQ+0wkq0kHtAwkoiISHNRgDkN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\ns8UrhxuGGlIoBhgiomVk0GnQVGdBU938EDCenEY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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "GhFtWjQRzD2l" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "OMoIsUMmzK9b" + }, + "cell_type": "markdown", + "source": [ + "These are only a few ways in which we could think about the data. Other transformations may work even better!\n", + "\n", + "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n", + "\n", + "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n", + "\n", + "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XDEYkPquzYCH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "7f69c7d1-7e1b-457c-ea21-ebb2ab7b77c9" + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 80.56\n", + " period 01 : 73.50\n", + " period 02 : 71.36\n", + " period 03 : 72.02\n", + " period 04 : 70.15\n", + " period 05 : 69.16\n", + " period 06 : 69.24\n", + " period 07 : 69.42\n", + " period 08 : 68.82\n", + " period 09 : 68.54\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.54\n", + "Final RMSE (on validation data): 70.31\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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o1q0bBg0aJFb3JhsQ1rD5W8vvqKzVa5GiTmvLsoiIiKyaaFM/CoUCa9euven5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\nEHax582E34nPbY04MnwMHePdAAC5TI4ay3LU5da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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "b7atJTbzU9Ca" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Use only Latitude and Longitude Features\n", + "\n", + "**Train a NN model that uses only latitude and longitude as features.**\n", + "\n", + "Real estate people are fond of saying that location is the only important feature in housing price.\n", + "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n", + "\n", + "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n", + "\n", + "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5McjahpamOc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "b89370da-8134-4239-9a01-913741f00a63" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#\n", + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.01),\n", + " steps=1000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 232.67\n", + " period 01 : 223.30\n", + " period 02 : 208.79\n", + " period 03 : 187.47\n", + " period 04 : 160.27\n", + " period 05 : 130.87\n", + " period 06 : 110.75\n", + " period 07 : 108.43\n", + " period 08 : 107.49\n", + " period 09 : 106.68\n", + "Model training finished.\n", + "Final RMSE (on training data): 106.68\n", + "Final RMSE (on validation data): 109.16\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Fqz1qVdUThY2NsWbzsJNcjJdkY7wkm+qRBqYGjH2nqqhQOHo2nW1RiRw//9ek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\nTuW3/e+jZVQqH+1dxdxNC/H6vI1sUUR+LBUYEZEfwG6zcu9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "P8BLQ7T71JWd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1hwaFCE71OPZ" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "djKtt4mz1ZEc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "18cddf67-64eb-4f5c-f764-c97c4400ed67" + }, + "cell_type": "code", + "source": [ + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 176.73\n", + " period 01 : 108.67\n", + " period 02 : 106.57\n", + " period 03 : 104.96\n", + " period 04 : 104.28\n", + " period 05 : 102.25\n", + " period 06 : 101.42\n", + " period 07 : 100.69\n", + " period 08 : 100.28\n", + " period 09 : 99.94\n", + "Model training finished.\n", + "Final RMSE (on training data): 99.94\n", + "Final RMSE (on validation data): 102.55\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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YYIiIiCjhMMAQERFRwmGAISIiooTDAENEREQJhwGGiIiIEg4DDBERESUcBhgiIiJKOAww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\ndPSm/VDTM5hBXzqL3sEM/nN6GMdODo07X214Oyo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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Dw2Mr9JZ1cRi" + }, + "cell_type": "markdown", + "source": [ + "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances." + ] + } + ] +} \ No newline at end of file diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb new file mode 100644 index 0000000..19148df --- /dev/null +++ b/intro_to_neural_nets.ipynb @@ -0,0 +1,1229 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_neural_nets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "O2q5RRCKqYaU", + "vvT2jDWjrKew" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eV16J6oUY-HN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Intro to Neural Networks" + ] + }, + { + "metadata": { + "id": "_wIcUFLSKNdx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n", + " * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model" + ] + }, + { + "metadata": { + "id": "_ZZ7f7prKNdy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n", + "\n", + "One important set of nonlinearities was around latitude and longitude, but there may be others.\n", + "\n", + "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly." + ] + }, + { + "metadata": { + "id": "J2kqX6VZTHUy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's load and prepare the data." + ] + }, + { + "metadata": { + "id": "AGOM1TUiKNdz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2I8E2qhyKNd4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pQzcj2B1T5dA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "6b15f1d3-e97b-46df-b9ab-4dae09427a77" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.7 2653.8 541.1 \n", + "std 2.1 2.0 12.6 2215.6 428.3 \n", + "min 32.5 -124.3 1.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1461.0 296.0 \n", + "50% 34.2 -118.5 29.0 2119.5 431.0 \n", + "75% 37.7 -118.0 37.0 3156.2 650.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1429.8 502.6 3.9 2.0 \n", + "std 1142.5 390.7 1.9 1.0 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 789.0 282.0 2.6 1.5 \n", + "50% 1167.0 408.0 3.6 1.9 \n", + "75% 1718.0 605.0 4.8 2.3 \n", + "max 28566.0 6082.0 15.0 52.0 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RWq0xecNKNeG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Building a Neural Network\n", + "\n", + "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n", + "\n", + "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n", + "\n", + "`hidden_units=[3,10]`\n", + "\n", + "The preceding assignment specifies a neural net with two hidden layers:\n", + "\n", + "* The first hidden layer contains 3 nodes.\n", + "* The second hidden layer contains 10 nodes.\n", + "\n", + "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n", + "\n", + "By default, all hidden layers will use ReLu activation and will be fully connected." + ] + }, + { + "metadata": { + "id": "ni0S6zHcTb04", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zvCqgNdzpaFg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural net regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U52Ychv9KNeH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `DNNRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2QhdcCy-Y8QR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train a NN Model\n", + "\n", + "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n", + "\n", + "Run the following block to train a NN model. \n", + "\n", + "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n", + "\n", + "Your task here is to modify various learning settings to improve accuracy on validation data.\n", + "\n", + "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n", + "\n", + "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n", + "\n", + "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n" + ] + }, + { + "metadata": { + "id": "rXmtSW1yKNeK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "e28b87dd-62da-4d3a-cb88-04b1ea303635" + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=10000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 144.15\n", + " period 01 : 114.16\n", + " period 02 : 107.14\n", + " period 03 : 103.90\n", + " period 04 : 99.23\n", + " period 05 : 99.60\n", + " period 06 : 97.93\n", + " period 07 : 98.08\n", + " period 08 : 96.94\n", + " period 09 : 99.94\n", + "Model training finished.\n", + "Final RMSE (on training data): 99.94\n", + "Final RMSE (on validation data): 99.61\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IiDQ6CjAiIiLS6CjAiIiINDHffPNlnV73wgvPcuTI4TNu/9Of7r9QJV1wCjAiIiJNSEbG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\nUh1ir28//nBqlfuKEjcb69rZWNdOtX3x1nK6HiT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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "O2q5RRCKqYaU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution" + ] + }, + { + "metadata": { + "id": "j2Yd5VfrqcC3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search." + ] + }, + { + "metadata": { + "id": "IjkpSqmxqnSM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "26a183b1-2895-4979-8e11-35136e1a1c3a" + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=2000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 167.85\n", + " period 01 : 170.59\n", + " period 02 : 166.65\n", + " period 03 : 167.46\n", + " period 04 : 166.50\n", + " period 05 : 161.52\n", + " period 06 : 157.01\n", + " period 07 : 153.52\n", + " period 08 : 153.38\n", + " period 09 : 145.57\n", + "Model training finished.\n", + "Final RMSE (on training data): 145.57\n", + "Final RMSE (on validation data): 146.32\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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47648xF/HdKBHex9zhyWEEKKBkwLmKhHtvWnm60zPtp4ye6YO9Wjvg5O9NR+s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\nSnzeRQBCXIIY3iKSUI92dT4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P0HNz06LRWJkkbgAvLyeT7VvcGcmNZTJXXrxw4qlmD3J3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\n8xjllVrITkRERLyAr93K9FEJNDqcvL05z7Q6FGBERETkoozo3ZEu0UF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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "c6diezCSeH4Y", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Evaluate on Test Data\n", + "\n", + "**Confirm that your validation performance results hold up on test data.**\n", + "\n", + "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n", + "\n", + "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)." + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7a12ac8d-b043-4ac4-a84c-3a93cbfb3030" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "# YOUR CODE HERE\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 143.97\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "vvT2jDWjrKew", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "FyDh7Qy6rQb0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n", + "\n", + "Note that we don't have to randomize the test data, since we will use all records." + ] + }, + { + "metadata": { + "id": "vhb0CtdvrWZx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9d937361-2013-4d62-d0c7-29c584ab0d29" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 143.97\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb new file mode 100644 index 0000000..c562a8f --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,1872 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# Intro to pandas" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QvJBqX8_Bctk" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", + " * Access and manipulate data within a `DataFrame` and `Series`\n", + " * Import CSV data into a *pandas* `DataFrame`\n", + " * Reindex a `DataFrame` to shuffle data" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TIFJ83ZTBctl" + }, + "cell_type": "markdown", + "source": [ + "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", + "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "s_JOISVgmn9v" + }, + "cell_type": "markdown", + "source": [ + "## Basic Concepts\n", + "\n", + "The following line imports the *pandas* API and prints the API version:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aSRYu62xUi3g", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7ca15d87-231b-4c5d-dd99-b822fc732415" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "daQreKXIUslr" + }, + "cell_type": "markdown", + "source": [ + "The primary data structures in *pandas* are implemented as two classes:\n", + "\n", + " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", + " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", + "\n", + "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fjnAk1xcU0yc" + }, + "cell_type": "markdown", + "source": [ + "One way to create a `Series` is to construct a `Series` object. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DFZ42Uq7UFDj", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "8bf78419-3409-4f02-fc77-07f1833138f4" + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "6f223cd2-8964-4e1c-96ce-811f72c418fd" + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785\n", + "2 Sacramento 485199" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "bbf6226b-9aab-430b-da73-21b8acd4858c" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean -119.562108 35.625225 28.589353 2643.664412 \n", + "std 2.005166 2.137340 12.586937 2179.947071 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.790000 33.930000 18.000000 1462.000000 \n", + "50% -118.490000 34.250000 29.000000 2127.000000 \n", + "75% -118.000000 37.720000 37.000000 3151.250000 \n", + "max -114.310000 41.950000 52.000000 37937.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean 539.410824 1429.573941 501.221941 3.883578 \n", + "std 421.499452 1147.852959 384.520841 1.908157 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 297.000000 790.000000 282.000000 2.566375 \n", + "50% 434.000000 1167.000000 409.000000 3.544600 \n", + "75% 648.250000 1721.000000 605.250000 4.767000 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 17000.000000 \n", + "mean 207300.912353 \n", + "std 115983.764387 \n", + "min 14999.000000 \n", + "25% 119400.000000 \n", + "50% 180400.000000 \n", + "75% 265000.000000 \n", + "max 500001.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WrkBjfz5kEQu" + }, + "cell_type": "markdown", + "source": [ + "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "s3ND3bgOkB5k", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "b0566413-21ee-48d9-de1b-ed49335499f0" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -114.31 34.19 15.0 5612.0 1283.0 \n", + "1 -114.47 34.40 19.0 7650.0 1901.0 \n", + "2 -114.56 33.69 17.0 720.0 174.0 \n", + "3 -114.57 33.64 14.0 1501.0 337.0 \n", + "4 -114.57 33.57 20.0 1454.0 326.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 1015.0 472.0 1.4936 66900.0 \n", + "1 1129.0 463.0 1.8200 80100.0 \n", + "2 333.0 117.0 1.6509 85700.0 \n", + "3 515.0 226.0 3.1917 73400.0 \n", + "4 624.0 262.0 1.9250 65500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396 + }, + "outputId": "8b4be17a-0fb2-4e28-f963-f4aab1d51175" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "XtYZ7114n3b-" + }, + "cell_type": "markdown", + "source": [ + "## Accessing Data\n", + "\n", + "You can access `DataFrame` data using familiar Python dict/list operations:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_TFm7-looBFF", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "eb972950-56e8-4f7a-e63c-031b1d9f3226" + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "c0463c12-b139-4193-94dd-5e517a26695a" + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 128 + }, + "outputId": "690198d6-e041-4b53-f6b5-8fa9905c8337" + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "e7457599-65d5-41e8-b7e3-e93567b47d7d" + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "92d27c2c-40e7-4c63-8418-1c68862c63af" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "e4e3caba-b614-4149-8d4d-ff73d9f087f7" + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "ae7bff6b-3b6b-4378-e3f8-4fbdf82c79cf" + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
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" + ], + "text/plain": [ + " City name Population Area square miles Population density\n", + "0 San Francisco 852469 46.87 18187.945381\n", + "1 San Jose 1015785 176.53 5754.177760\n", + "2 Sacramento 485199 97.92 4955.055147" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "9a66814d-d261-4c5b-9cfa-d58aa0c7e77f" + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
2Sacramento48519997.924955.055147False
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "f7dbb8a5-0b91-40fc-b29f-b50d75fb03e9" + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
2Sacramento48519997.924955.055147False
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "3d043277-0673-482e-b630-35237aed02fd" + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "04820fe4-8e73-4c35-bfcd-1a00de1537bd" + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "98feb2e1-fd7c-47b3-d86e-6c8572220f46" + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Is wide and has saint name \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "8a969746-e6db-470c-bdbd-ca46c2383909" + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco85246946.8718187.945381False
2Sacramento48519997.924955.055147False
1San Jose1015785176.535754.177760True
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "2 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "outputId": "7a90d1ec-a80c-42ed-c576-beecf41e2a6f" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco852469.046.8718187.945381False
4NaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "4 NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "4 NaN \n", + "5 NaN \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "outputId": "4f30274c-43d3-47bb-ba7b-f76c252416d2" + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco852469.046.8718187.945381False
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "4 NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "4 NaN \n", + "5 NaN \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "2l82PhPbwz7g" + }, + "cell_type": "markdown", + "source": [ + "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n", + "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n", + "in which the index values are browser names).\n", + "\n", + "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n", + "sanitizing the input." + ] + } + ] +} \ No newline at end of file diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb new file mode 100644 index 0000000..5e3bd56 --- /dev/null +++ b/logistic_regression.ipynb @@ -0,0 +1,1694 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "logistic_regression.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "dPpJUV862FYI", + "i2e3TlyL57Qs", + "wCugvl0JdWYL" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Logistic Regression" + ] + }, + { + "metadata": { + "id": "LEAHZv4rIYHX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n", + " * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem" + ] + }, + { + "metadata": { + "id": "CnkCZqdIIYHY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now." + ] + }, + { + "metadata": { + "id": "9pltCyy2K3dd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Frame the Problem as Binary Classification\n", + "\n", + "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n", + "\n", + "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`." + ] + }, + { + "metadata": { + "id": "67IJwZX1Vvjt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and prepare the input features and targets." + ] + }, + { + "metadata": { + "id": "fOlbcJ4EIYHd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "lTB73MNeIYHf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`." + ] + }, + { + "metadata": { + "id": "kPSqspaqIYHg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FwOYWmXqWA6D", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "9d650342-4997-4899-b600-24c514774aff" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2635.0 537.8 \n", + "std 2.1 2.0 12.6 2165.7 420.8 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1454.0 296.0 \n", + "50% 34.2 -118.5 29.0 2122.0 432.0 \n", + "75% 37.7 -118.0 37.0 3144.5 646.0 \n", + "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1427.4 500.5 3.9 2.0 \n", + "std 1149.1 386.3 1.9 1.2 \n", + "min 6.0 1.0 0.5 0.1 \n", + "25% 787.8 281.0 2.6 1.5 \n", + "50% 1162.0 407.0 3.6 1.9 \n", + "75% 1711.0 602.0 4.8 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "uon1LB3A31VN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## How Would Linear Regression Fare?\n", + "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n", + "\n", + "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)." + ] + }, + { + "metadata": { + "id": "smmUYRDtWOV_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "B5OwSrr1yIKD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SE2-hq8PIYHz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_regressor_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TDBD8xeeIYH2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + }, + "outputId": "9fae3e5e-634f-4d21-aa2a-e51299f74026" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 0.45\n", + " period 01 : 0.46\n", + " period 02 : 0.45\n", + " period 03 : 0.44\n", + " period 04 : 0.44\n", + " period 05 : 0.45\n", + " period 06 : 0.44\n", + " period 07 : 0.45\n", + " period 08 : 0.44\n", + " period 09 : 0.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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b5UtFTSMnkvL1XY4QQgg9k4AjjMasyH6oVLArPkvuZyWEEL2cBBxhNNydrAgd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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "JjBZ_q7aD9gh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Can We Calculate LogLoss for These Predictions?\n", + "\n", + "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n", + "\n", + "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability. For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n", + "\n", + "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily. Remember, `LogLoss` is defined as:\n", + "\n", + "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n", + "\n", + "\n", + "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n", + "\n", + "Given the predictions and the targets, can we calculate `LogLoss`?" + ] + }, + { + "metadata": { + "id": "dPpJUV862FYI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to display the solution." + ] + }, + { + "metadata": { + "id": "kXFQ5uig2RoP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "e1347c32-4068-4fac-9399-0c0e76e7d658" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "rYpy336F9wBg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n", + "\n", + "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n", + "\n", + "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n" + ] + }, + { + "metadata": { + "id": "JElcb--E9wBm", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer)\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VM0wmnFUIYH9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "88f9a775-5fcf-46b2-a280-1339a3fe7235" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000002,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.61\n", + " period 01 : 0.60\n", + " period 02 : 0.59\n", + " period 03 : 0.59\n", + " period 04 : 0.58\n", + " period 05 : 0.57\n", + " period 06 : 0.57\n", + " period 07 : 0.56\n", + " period 08 : 0.55\n", + " period 09 : 0.55\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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i3qpDzF1xsE5nmYmIiFwtCjNyFoPBwNCWg7k5Yggny/P59ODHjB8WRpCPCws3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\n8+4NHOg6pENQ49iYlZn58+eP7G2pq6vDbrePHGL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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i2e3TlyL57Qs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see the solution.\n", + "\n" + ] + }, + { + "metadata": { + "id": "5YxXd2hn6MuF", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) \n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "UPM_T1FXsTaL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "04e9168c-853b-4c23-b2eb-4303ede8b631" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.61\n", + " period 01 : 0.58\n", + " period 02 : 0.56\n", + " period 03 : 0.56\n", + " period 04 : 0.54\n", + " period 05 : 0.54\n", + " period 06 : 0.53\n", + " period 07 : 0.54\n", + " period 08 : 0.53\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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wn8Ph4In1f6asoZQRTTO4e+oId5fU5ry1Nx2d+uK51BvPpL60nFsOM4n7GIbB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\nobzKOLoop1qlQE5KOBam65GVGDalY35uVmNrH14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IiIjIqzHMEBERkVdjmCEiIiKvxjBD\nREREXo1hhoiIiLwawwwRERF5NYYZIiIi8mr/D/0j5MtslIKcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i-Xo83_aR6s_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n", + "\n", + "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n", + "\n", + "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC." + ] + }, + { + "metadata": { + "id": "DKSQ87VVIYIA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "b559deef-4b0d-4eeb-c789-1c53c6d644ea" + }, + "cell_type": "code", + "source": [ + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "AUC on the validation set: 0.72\n", + "Accuracy on the validation set: 0.76\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "47xGS2uNIYIE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n", + "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n", + "obtain the true positive and false positive rates needed to plot a ROC curve." + ] + }, + { + "metadata": { + "id": "xaU7ttj8IYIF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "1842e6ca-153f-4182-d956-7ecd855bc575" + }, + "cell_type": "code", + "source": [ + "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + "# Get just the probabilities for the positive class.\n", + "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n", + "\n", + "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n", + " validation_targets, validation_probabilities)\n", + "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n", + "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n", + "_ = plt.legend(loc=2)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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9ZmnE6dNncvLkMeLj9TdVQ1ycnvfeewez2Ux9fR2///0rPPHETwFoamrEyckJf/8AyspK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\nsips20lT5TvN/1XUUMyq9GSKDMX4uvZ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "PIdhwfgzIYII", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n", + "\n", + "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n", + "\n", + "**Verify if all metrics improve at the same time.**" + ] + }, + { + "metadata": { + "id": "XKIqjsqcCaxO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "4fa1da4b-cc00-4fa0-a850-b96fae1013c4" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.59\n", + " period 01 : 0.57\n", + " period 02 : 0.56\n", + " period 03 : 0.55\n", + " period 04 : 0.54\n", + " period 05 : 0.54\n", + " period 06 : 0.54\n", + " period 07 : 0.53\n", + " period 08 : 0.53\n", + " period 09 : 0.52\n", + "Model training finished.\n", + "AUC on the validation set: 0.73\n", + "Accuracy on the validation set: 0.77\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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JrVZrflxcXAyNRmN+PGXKFPPfY2JikJ6ejri4ODz99NPm5ePGjYO7e+uTbcvL9e1YdUu/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\n3LiR/v5+nn/+edzcpnbHUhEREWfy9HDjrsxYVi2O5uPSJt7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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "wCugvl0JdWYL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "id": "VHosS1g2aetf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One possible solution that works is to just train for longer, as long as we don't overfit. \n", + "\n", + "We can do this by increasing the number the steps, the batch size, or both.\n", + "\n", + "All metrics improve at the same time, so our loss metric is a good proxy\n", + "for both AUC and accuracy.\n", + "\n", + "Notice how it takes many, many more iterations just to squeeze a few more \n", + "units of AUC. This commonly happens. But often even this small gain is worth \n", + "the costs." + ] + }, + { + "metadata": { + "id": "dWgTEYMddaA-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "6dce2c24-0acb-49c2-fcb5-d3c416494258" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000003,\n", + " steps=20000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.49\n", + " period 01 : 0.48\n", + " period 02 : 0.47\n", + " period 03 : 0.47\n", + " period 04 : 0.47\n", + " period 05 : 0.47\n", + " period 06 : 0.47\n", + " period 07 : 0.47\n", + " period 08 : 0.47\n", + " period 09 : 0.46\n", + "Model training finished.\n", + "AUC on the validation set: 0.80\n", + "Accuracy on the validation set: 0.79\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UERFpvkwrcAYOHEh4eDixsbE8+eSTzJ49m4SEBD7//PPztunevTs1NTVMmjSJ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\npNb9pMNPPBFHmaFkDkeZwoBDRAuKUiHHknIrlpRbAQDxRAJ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ScmYX7xdZMXE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Build a Linear Model for MNIST\n", + "\n", + "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n", + "\n", + "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix). The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n", + "\n", + "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training." + ] + }, + { + "metadata": { + "id": "cpoVC4TSdw5Z", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " \n", + " # There are 784 pixels in each image.\n", + " return set([tf.feature_column.numeric_column('pixels', shape=784)])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kMmL89yGeTfz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls." + ] + }, + { + "metadata": { + "id": "OeS47Bmn5Ms2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n", + " \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n", + "\n", + " Args:\n", + " features: The training features.\n", + " labels: The training labels.\n", + " batch_size: Batch size to use during training.\n", + "\n", + " Returns:\n", + " A function that returns batches of training features and labels during\n", + " training.\n", + " \"\"\"\n", + " def _input_fn(num_epochs=None, shuffle=True):\n", + " # Input pipelines are reset with each call to .train(). To ensure model\n", + " # gets a good sampling of data, even when number of steps is small, we \n", + " # shuffle all the data before creating the Dataset object\n", + " idx = np.random.permutation(features.index)\n", + " raw_features = {\"pixels\":features.reindex(idx)}\n", + " raw_targets = np.array(labels[idx])\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8zoGWAoohrwS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_predict_input_fn(features, labels, batch_size):\n", + " \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n", + "\n", + " Args:\n", + " features: The features to base predictions on.\n", + " labels: The labels of the prediction examples.\n", + "\n", + " Returns:\n", + " A function that returns features and labels for predictions.\n", + " \"\"\"\n", + " def _input_fn():\n", + " raw_features = {\"pixels\": features.values}\n", + " raw_targets = np.array(labels)\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size)\n", + " \n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "G6DjSLZMu8Um", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, and a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `LinearClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + "\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create a LinearClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(),\n", + " n_classes=10,\n", + " optimizer=my_optimizer,\n", + " config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ItHIUyv2u8Ur", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n", + "\n", + "Stop if you get anything above about 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "yaiIhIQqu8Uv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "a79a1753-b62f-47ca-8ac8-b76b50134ce8" + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.02,\n", + " steps=500,\n", + " batch_size=30,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 6.37\n", + " period 01 : 4.66\n", + " period 02 : 4.56\n", + " period 03 : 4.37\n", + " period 04 : 4.31\n", + " period 05 : 3.92\n", + " period 06 : 3.91\n", + " period 07 : 3.83\n", + " period 08 : 3.69\n", + " period 09 : 3.63\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.89\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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EEGONhLcbXHzmu/9T5xcXKpG7zoUQQgydhLcbpCeFERLow/7cSqydfU+XmtEV\n3tnVEt5CCCGGTsLbDbQaDXOnRNPY0sHx032fEg/zDyU2yER+bSHtne0qtlAIIcRYIuHtJq6uNJYR\nkYbVZuVkTYEazRJCCDEGSXi7yfhoPTHGQI4UmGlu7Xu6VLnuLYQQ4kpJeLuJoijMzzBh7bRx8GRV\nn/slhSQQqAsguzqv30fLhBBCiL5IeLvR1VOiAdjTz6lzrUZLWngKNW21lDX1vyKZEEII0RsJbzeK\nMAQweVwo+cW1VPczXeqFU+eyUIkQQoihkPB2s8wL06Xm9D2qnmKcjIIiU6UKIYQYEglvN7tqchQ6\nrYasfqZL1fsGMz5kHKfrimju6HuELoQQQvRGwtvNAv11zJgUQZm5mbPlDX3ul2FMxWa3kWvJV7F1\nQgghxgIJbw+Ynz7wM99y3VsIIcRQSXh7QMaEcIIDfNiXW9HndKnx+lhCfPXkmE9is/c9paoQQgjR\nk4S3B+i0Gq5Oi6ahuYOcs5Ze99EoGqYYJ9PQ0UhxQ4nKLRRCCDGaSXh7yLyMgZ/5zjCmAXCiWu46\nF0II4TqXw7uxsRGA6upqDh48iM0mp3r7MyEmhOiwAA6fqqalzdrrPqnhyWgUjUyVKoQQYlBcCu/n\nn3+ezZs3U1tby9q1a3nrrbfYsGGDh5s2uimKQmaGiQ6rjUN9TJcaoAsg2ZDEuYbz1Lf3fWe6EEII\ncSmXwjsnJ4evf/3rbN68mVtuuYVf/vKXFBUVebpto968C3edZ/dz13nEhbvOT6rSJiGEEKOfS+F9\nYbKR7du3s3TpUgDa2/tfj3rfvn3MmzeP9evXs379ep5//vlu2/fs2cPq1atZs2YNr7766lDaPuJF\nhQaQHG8gr6gGS31rr/tkyCNjQgghBknnyk5JSUmsWrWK8PBw0tLS2LRpEwaDYcDj5s6dy69+9ate\nt73wwgu8+eabREdHc+edd7J8+XKSk5MH1/pRYH66iYLzdezNqWDVvPGXbY8OjMLoH06uOZ9OWyda\njXYYWimEEGI0cWnk/cILL/DSSy/x+9//HoBJkybx05/+dMgfWlxcjMFgICYmBo1Gw+LFi8nKyhry\n+41kc9Ki0GkVsk70Pl2qoiikG1Np7WzldN1Z9RsohBBi1HFp5J2bm0tVVRVpaWn8/Oc/58iRI3z7\n29/mqquu6ve4goICHnjgAerq6nj44Ye55pprAKiqqiI8PNy5X3h4OMXFxf2+V1hYIDqde0elkZF6\nt75fr58BzJliIut4GY0ddibEhVy2zzXWmews2cPpltPMT5nh8TYNBzVqLaTOapE6q0Pq3DeXwvuF\nF17gxz/+MQcPHuT48eM8/fSc6MpBAAAgAElEQVTTPPfcc/zP//xPn8ckJiby8MMPs3LlSoqLi7nr\nrrv417/+ha+v75AaWlPTPKTj+hIZqaeqSp07vGclR5B1vIyPdhWy9rpJl22PUmLx0fhwoPgYy2OX\nqdImNalZa28mdVaH1FkdUmeHvn6Bcem0uZ+fH4mJiXz22WfcfvvtJCcno9H0f2h0dDSrVq1CURQS\nEhKIiIigosKxTGZUVBTV1dXOfSsqKoiKinL1Zxl1pk00EuSvY19OBZ29PB/vq/VhcthEypoqMLfU\nDEMLhRBCjCYuhXdLSwubN29m69atLFiwgNraWurr6/s95oMPPuDNN98EHKfJzWYz0dGOWcfi4+Np\nbGzk/PnzWK1Wtm3b5jylPhb56DTMSYumrqmd3KLew/niQiUy25oQQoj+uRTejz32GP/85z957LHH\nCA4O5q233uKee+7p95ilS5dy4MAB1q1bx4MPPsiGDRv48MMP+fTTTwHYsGEDjz/+OP/2b//GqlWr\nSEpKuuIfZiQbaKWx9K6pUuWRMSGEEANR7L3dAt2L5uZmzpw5g6IoJCUlERAQ4Om2dePuax9qX0+x\n2+088bss6pra+cW3F+Dve/ntBi/se4nqFgs/XbgBX62Pam3zNLl2pQ6pszqkzuqQOjtc0TXvrVu3\ncv311/PMM8/w1FNPsXz5cnbs2OHWBo51iqKQmW6ivcPGl/m9T5eabkylw9bBqdpClVsnhBBiNHEp\nvN944w0++OAD3nvvPd5//33++te/snHjRk+3bczJdE6XWtHr9guzrZ2ollPnQggh+uZSePv4+HR7\nLjs6Ohofn7FzWlct0eGBTIwNIeeshdrGtsu2TzAkEqDzJ9uc2+uELkIIIQS4GN5BQUH8/ve/Jy8v\nj7y8PN544w2CgoI83bYxaV66Cbsd9vYy+tZqtKSGp2BuraGiuXIYWieEEGI0cCm8f/CDH3D27Fme\neOIJnnzySUpKSvjhD3/o6baNSXPTotBqlD5XGrvwyJis8S2EEKIvLs2wZjQaee6557q9VlhY2O1U\nunCNPtCXqROMHCmo5nxlI/FRwd22pxsnA5BdncdXEhYPRxOFEEKMcC6NvHvz7LPPurMdXmV+Rt/r\nfIf46knQx1NQd4YWa+/LiAohhPBuQw5vuaFq6KYnGwnw07E3pwKb7fI6ZhhTsdlt5FlODUPrhBBC\njHRDDm9FUdzZDq/io9MyJzWSmoY28s5dPl1qesSF694yVaoQQojL9XvN+7333utzW1VV7xONCNdk\nppvYebSMrBPlTEnsfu9Agj4evU8wOeaT2Ow2NMqQf8cSQggxBvUb3ocOHepz24wZY3PdabVMGheK\nMcSfg/lV3NnRiZ/PxbXKNYqGKcbJ7Cs/xPmGUhJC4oexpUIIIUaafsP7Rz/6kVrt8DoaRWFeejQf\nZRVx+FQV86aYum1PN6ayr/wQ2eY8CW8hhBDduPSo2Lp16y67xq3VaklKSuLBBx90LvUpBmd+homP\nsorIOlFxWXinhaegUTScMOexMukrw9RCIYQQI5FLF1Pnz5+PyWTi7rvv5t5772XcuHHMnj2bpKQk\nnnzySU+3ccyKMQaRaNKTfcZCXVN7t22BPgFMMIynqL6YhvbGYWqhEEKIkcil8D506BAvvfQS119/\nPV/5ylf48Y9/THZ2Nvfccw8dHR2ebuOYlplhwma3sz/n8ulSM4xp2LGTYz45DC0TQggxUrkU3maz\nGYvF4vx3Q0MDpaWl1NfX09Ag661eiavTotEoCnt6mbDlwlSp2TJVqhBCiEu4dM37rrvuYuXKlcTF\nxaEoCufPn+c//uM/2LZtG2vWrPF0G8e0kCBfMiaEc6zQTGl1E7ERFxd8iQmKJswvlBxLPp22TrQa\nbT/vJIQQwlu4FN6rV69mxYoVnD17FpvNRkJCAqGhoZ5um9fITDdxrNBMVnY5ty2e6HxdURTSI1L5\nomQvZ+rPkRyaNIytFEIIMVK4dNq8qamJP/3pT/z6179m48aNvPvuu7S2yrzb7jJjUgT+vlr2Zpdj\n6zHtbIacOhdCCNGDS+H99NNP09jYyNq1a7n99tuprq7mqaee8nTbvIafj5bZkyMx17dxqri227bJ\nYcnoNDpOVMtUqUIIIRxcOm1eXV3Nyy+/7Pz3tddey/r16z3WKG80P93E7uPl7DlRzuSEMOfrvlpf\nUkInkmM5SU1rLWH+crlCCCG8nUsj75aWFlpaWpz/bm5upq2tzWON8kaTx4cRpvfj4MlK2js6u227\nuFCJnDoXQgjhYnivWbOGlStX8vDDD/Pwww9zww03sG7dOk+3zatcmC61pa2To4XmbtvkurcQQohL\nuRTeq1ev5p133uHmm2/mlltu4f/+7/8oKCjwdNu8Tma6Y4rUrBPdn/mOCDASHRjFScspOjplUhwh\nhPB2Ll3zBoiJiSEmJsb572PHjnmkQd4sPjKYhKhgjp82U9/cTkigr3NbunEynxfv4h+nN5Menkq8\nPha9b/AwtlYIIcRwcTm8e7L3eKRJuEdmhol3Py/gQG4l182+uJrYrKhpbCv+wvkHIMwvlHh9LOP0\ncYwLdvwd6me4bBEZIYQQY8uQw1sCwjOunhLNX7YVkJVd3i28kwzjeX7+kxTVF1PcWEpxQwnnG0o4\nXp3D8eoc537BPkGOMNfHEd8V6BEB4WgUl66QCCGEGAX6De/Fixf3GtJ2u52amhqPNcqbhQb7MSUx\nnOwzFsotzZjCA53bwvxDCfMPZUbUVOdrdW31jiDvCvTihhJyLfnkWvKd+/hr/YnXx3SN0B3BHh0Y\nKdOtCiHEKNVveL/99ttqtUNcYn66iewzFrJOlHPLogn97mvwC8HgF0JGRJrztaaOZs43lFLcWNIV\n6KUU1p6loPaMcx8fjY7YYEegJwTHEa+PJTbIhI/Wx2M/lxBCCPfoN7zj4uLUaoe4xKyUSPx8tGRl\nl3PzwqRBX6II8glkcngyk8OTna+1WtsobSqjuOHiCP18QylF9cXOfTSKhpigaOfoPF4fS3xwDP46\nf7f9bEIIIa7ckK95C8/x89UyKyWSrOxyCkrqmBR/5bOq+ev8mGBIZIIh0flah81KWVO5Y5R+IdAb\nyyhpLGNv+UEAFBQiA43OQL8Q6sE+QX18khBCCE+T8B6hMjOiycouJ+tEuVvCuzc+Gh0J+ngS9Bdv\njLPZbVQ0VznD/ML19EOVRzlUedS5X5hfKAmXhPk4fRwG3xC5kVEIIVQg4T1CTRkfjiHYlwN5ldzx\nlRR8dOrcLX7h1HlMUDRzTbMAxw2K5lZLt1PuxQ0lHK3O5mh1tvNYvW9wt1PuCfo4jP7hqrRbCCG8\niYT3CKXRKMybEs2W/cUcK6xm9uSoYWuLoihEBBiJCDAys+tOd7vdTl17fbdT7sWNpeRYTpJjOek8\nNkAXwJy4acwKn0Fy6AQZmQshhBtIeI9gmekmtuwvJiu7YljDuzeKohDqZyDUz9DtTvfGjqZugX6m\n/hw7i/axs2gfUQERZMbM4eqY2Rj8Qoax9UIIMbpJeI9g46KCiYsM4mhBNY0tHQQHjPzHuIJ9gkgN\nn0Rq+CTAMUKvppyPcnZwpOoY/zi9mX+e2UK6cTLzY+aSbkyV582FEGKQPBrera2tfPWrX+XBBx/k\n1ltvdb6+dOlSTCYTWq3jS/vFF18kOjrak00ZlRRFYX66ib9uL+RAXiXXzhx9j+4pisKUyBQilRia\nO27iYMURssr2c7w6l+PVuYT46rnaNJv5sXOICowc7uYKIcSo4NHw3rhxIwaDoddtr7/+OkFB8rjR\nQK6eEs172wvJOlE+KsP7UoE+ASyKz2RRfCbFDaVkle1nf/lhPj23nU/PbSc5NIn5MXOZGTUVX63v\nwG8ohBBeymPhXVhYSEFBAUuWLPHUR3iF8BB/UseHkVtUQ2VtC1GhAcPdJLcYp49lnP5mbpl4A0er\nTrC77AD5NQUU1J7hL/n/4Kro6cyPnUuCPl5uchNCiB4Uu4eWB7v//vt5+umn2bRpE3FxcZedNp81\naxYlJSXMnj2bxx9/fMAvaKu1E53OO6+Nbt1/jl++e5h1y1O54/rJw90cj6lorGLbmSy2n8nC0lIL\nwHhDHNdOmM+i8VcT7CdnaoQQAjw08t60aRMzZsxg3LhxvW5/5JFHWLhwIQaDgYceeogtW7awYsWK\nft+zpqbZrW2MjNRTVdXg1vf0lJRYPb46DVv3F3HdjJhRNxJ1tdYa/LnOdC3XRi8mx3ySrLIDHKvO\n4Y+H/8r/Hnmf6ZEZzI+dS0rYRFklrRejqU+PZlJndUidHSIj9b2+7pHw3r59O8XFxWzfvp3y8nJ8\nfX0xmUzMnz8fgJtvvtm576JFi8jPzx8wvL1ZgJ+OmSmR7Mup4MQZC1MnGIe7SR6lUTRkRKSREZFG\nQ3sj+8oPkVV6wDnLm9E/jMyYOcyLuYowf8/MPieEECOZx06bX/DKK690O23e0NDAd77zHTZu3Iiv\nry/f+c53WL58OStXruz3fdz9G9ho+60ut6iGn71zGAWYn2HilkUTCA8ZHQuGuKPWdrudM/VF7OkK\n8fbOdhQU0sJTyIydw7SIKeg03v3k42jr06OV1FkdUmcHVUfevXn//ffR6/UsW7aMRYsWsWbNGvz8\n/JgyZYqMul2QNj6Mx9fO4N3PCth9opz9eZUsnzuOlVePJ8Bv7IeWoijOhVVWT7qRQ5VHySo94JzR\nLdgniLmmWcyPnUtMkDx2KIQY2zw+8nYXbx95X2Cz2dlzopz3dxZS29iOPtCHmxcksXB6LDrtyLwO\n7MlalzaWk1V2gP3lX9LY0QRAUkgC82PnMitqmlctZzpa+/RoI3VWh9TZoa+Rt4T3KNXW0cm/9p/j\n433naGvvJMYYyOolE5mRHDHibmhTo9ZWm5Vj1TlklR4g15KPHTu+Wl9mRzkeOUsKSRhxdXG30d6n\nRwupszqkzg4S3j2MlY5R19TOP744w44jJdjtMHlcKLcvTSYpZuTMHa52rS2tNewtO0hW2UEsrTUA\nmAKjyIydw9Wm2eh9g1Vri5rGSp8e6aTO6pA6O0h49zDWOkZJdRPvbSvgaKEZgHnp0dy6aAIRhuGf\n1GW4am2z28ivKWRP6X6OVp3Aau9Eo2iYFjGF+bFzSQtPGVOPnI21Pj1SSZ3VIXV2kPDuYax2jNyz\nFt7dVsC5ikZ0Wg3L5sRzw7xEAv2H76a2kVDrxo4mDpQfJqvsACWNZQCE+hmYF3MVmTFziAgY/euO\nj4Q6ewOpszqkzg4S3j2M5Y5hs9vZl13B33YWYqlvIzjAh69dk8iSmXHDclPbSKq13W7nXMN59pQd\n4GD5EVo7WwGYHJbM/Jg5TI/MwEc78ldv681IqvNYJnVWh9TZQcK7B2/oGO0dnXx6sJiPsopobe8k\nOiyA1UuSmZWi7k1tI7XW7Z3tHK48zp6y/RTUngEgUBfAHNNMkkMnENa1XnmIr35ULFs6Uus81kid\n1SF1dpDw7sGbOkZ9czsffHGG7YdLsdntJMcbWLM0mYmxva/45m6jodYVzVVklR5gX/kh6tu7t1Wj\naAjx1TvC3D/UGeph/qGOv0dIwI+GOo8FUmd1SJ0dJLx78MaOUWZu4r3thRw+VQ3A3LQobls8kUgP\nr1Q2mmrdaevkZE0BFc1V1LTVUttaR21bHTVtjr9tdluvxykoGPxCnGEe6m8gzK8r3Lv+29MBP5rq\nPJpJndUhdXaQ8O7BmzvGyXM1/GVbAWfKGtBpFZbOiuer8xMJDvDMtd6xUmub3UZDe6MzzGtaa6nt\nCvWL/11Pp72z1+MVFMcI3v9iqF8I+wuvGXxDhhzwY6XOI53UWR1SZwcJ7x68vWPY7HYO5Fby3vZC\nzPWtBPnruHF+ItfOisdH596b2ryp1o6Ab6K2rdYxWneO3Gup6frv2ra6AQP+wsj94ijeQGjXSD7U\nr/eA96Y6Dyepszqkzg4S3j1Ix3DosHby2aES/rnnLC1tViJD/Vm9JJmrJke67aY2qXV3NruNxo4m\nalu7Qr0r5GvaartG8a4EfDChfqEXR+/+oSyedBU+bbLmuadJf1aH1NlBwrsH6RjdNbZ08MHuM2z7\nsoROm52JsSGsWTqJ5Pgrv6lNaj14NruNpo5malprndfbL5yav/RavPWSgPfV+rA25Vaujpk9jC0f\n+6Q/q0Pq7CDh3YN0jN5V1DTzt+2FHDxZBcDsyZGsXjKR6LDAIb+n1Noz7HY7jR1N1LTVcr6hlL8X\nfkRzRwsL4uaxetLX8PHyJVI9RfqzOqTODhLePUjH6F/B+Tre/fwUhaX1aDUK186K42vXJA3ppjap\ntTo6/Vv4yc7fUtJYRoI+nm9krMcYEDbczRpzpD+rQ+rs0Fd4azds2LBB3aYMTXNzu1vfLyjIz+3v\nOZaEh/izcFoMcZHBnCmr58RpC9uPlKLRQKJJj1bj+k1tUmt1RIeFMzUkg9q2OnIsJ9lf/iVxwTFE\nBkYMd9PGFOnP6pA6OwQF+fX6uoS36JOiKMRFBLFkRhzBAT6cKq7lSIGZrBMVhAT5EBsR5NJNbVJr\ndQQF+dHW0sm0iHRC/EI4Xp3N/vIvAZgYmjTml0RVi/RndUidHSS8e5CO4TqtRmFinIHFM2Kx2ezk\nFtVwIK+K46fNmMIDB1y5TGqtjgt1VhSF8SHxTDFOJsd8kmPVORQ1FJNuTMV3lM7bPpJIf1aH1NlB\nwrsH6RiD56vTkpFkZF66ifqmdrLP1LD7eDnnKhpIiA5GH+jb63FSa3X0rHOon4G5plmUNJaRa8nn\ny8qjTAxNwuA3ctZ6H42kP6tD6uwg4d2DdIyhC/L34arUKDImhFNubib7bA3bD5dS19xOkikEP9/u\nE4hIrdXRW519tb5cFT0DgGPVOewrP4TBV884fdxwNHFMkP6sDqmzg4R3D9Ixrly43p8FU2MYF6Wn\nqLyeE2csbD9SAnTd1Na1/KjUWh191VlRFFLCJjJeH8+J6ly+rDxGTWstqeEpw76Yymgk/VkdUmcH\nCe8epGO4h6IoxEYEsWRmHCFBvpw6X8fRAjN7sssJDvAhLjKYYKm1Kgbq01GBkcyKmk5h3RmyzXnk\nmPNIDZ9EoM/Qn+H3RvLdoQ6ps4OEdw/SMdxLo1GYEBvC4hlx2LGTe7aWgyerOFJQTajeH0OgDxqN\n3O3sSa706UCfAK42zaa+vZFsSx77yr8kJiia6MBIlVo5+sl3hzqkzg59hbdM0iI8orquhfd3nmZv\ndgUAhiBfFk6PZfH0WIwG/2Fu3dg02D69p/QA7+b/HavNyorE67ghaRkaxb2L0oxF8t2hDqmzg8yw\n1oN0DHWUVDex/2QVW/efo6XNiqLAtAlGlsyMY+oEo4zG3Wgofbq4oYTXj7+FudVCatgk7k1fR7Cv\nLG7SH/nuUIfU2UHCuwfpGOqJjNRzvrSW/bkVbD9cypmyegCMIX4smhHHomkxGIJ7PzUkXDfUPt3c\n0cyfcv6PE+Y8wvxC+cbUO0kMSfBAC8cG+e5Qh9TZQcK7B+kY6ulZ66LyBrYfKWFvdgVtHZ1oNQoz\nJ0WwZGYcqePD0MhMYENyJX3aZrfxr6JtfHj6X2gUDasnfY2FcfNkVrZeyHeHOqTODhLePUjHUE9f\ntW5ps7I3u5xth0s4X9UEQHRYAItnxLFgWsyQFkHxZu7o07mWfP6Q/TZNHc3MNc3ijsm34qvtffId\nbyXfHeqQOjtIePcgHUM9A9XabrdTWFrP9sMl7M+txNppQ6fVMCc1kiUz40iOM8gI0AXu6tOW1hre\nOPG/FNUXExtk4ptT1xMld6M7yXeHOqTODhLePUjHUM9gat3Y0sGe42VsO1JKhaUZgLhIx+Iomekm\nAv1ljeq+uLNPd9is/O3UP9lVkoW/1p+7ptzO9MgMt7z3aCffHeqQOjtIePcgHUM9Q6m13W4n71wt\n2w+X8GV+FZ02O74+GuZNiWbJzDgSTTI/d0+e6NP7yg7xzsn36bB1sCxhCTdOWO71s7LJd4c6pM4O\nfYW3DGPEiKQoCmnjw0gbH0ZdYxtfHC9jx5FSdh4tY+fRMhJNepbMjOPqtOjL5lIX7nN1zGzi9bG8\nfvx/+PTcdorqi7k3Yx0hvr1/oQgh1CEjb+Fx7qq1zWZ3zJ9+uISjhdXY7RDgp2V+egyLZ8YSHxns\nhtaOXp7s0y3WFt7K+QtHq7Mx+Ibwjal3MsGQ6JHPGunku0MdUmcHOW3eg3QM9Xii1pb6VnYeLWXH\n0VLqGh1TKE6KN7BkZhxXTY7ER+d9o3FP92m73c7Wczv4R+FmFEXh1uSvsiT+Gq+7mVC+O9QhdXaQ\n8O5BOoZ6PFlra6eNowVmth8pIfuMBYDgAB8WTI1h8YxYosO9Z9ENtfp0fk0hvz/xZxo6GpkdNZ11\nqavx13nPJDvy3aEOqbODhHcP0jHUo1atK2ua2XGklF3Hymhs6QBgSmIYS2bEMWNSBDrt2J63W80+\nXdtWx5sn/pfTdUWYgqL5ZsZ6TEFRqnz2cJPvDnVInR0kvHuQjqEetWvdYbVxKL+S7YdLyS+uBbxj\nYRS169xp6+TvhR+xrfgL/LS+3Jl2O7Oipqn2+cNFvjvUIXV26Cu8PbokaGtrKytWrCA4OJi0tDTn\n63v27OG73/0uf/vb36isrGTu3LkDvpcsCTp6qV1rrUYhPjKYBdNiuCo1Cq2icLa8gZyzFrYeKuZs\nWT0BfjqiQgPG1PVateusUTRMMU7GFBjJseocDlYcptXayuSw5DG9Opl8d6hD6uzQ15KgHn1UbOPG\njRgMhstef+GFF3jzzTeJjo7mzjvvZPny5SQnJ3uyKcJLxUUEsW5ZCrctmehcGOVooZmjhWbnwigL\np8UQKgujDNns6BnEBsfw+vG3+Lx4F0X1xdyXcScGP3kWXwhP8divx4WFhRQUFLBkyZJurxcXF2Mw\nGIiJiUGj0bB48WKysrI81QwhAPDz0bJwWixP330Vz9wzh8UzYmlssfL3naf53m/28Orfj5N91oJt\ndFxFGnFigqL5z6seZmbUNArrzvKjA7/gVM3p4W6WEGOWx8L7Jz/5CU888cRlr1dVVREeHu78d3h4\nOFVVVZ5qhhCXGW/Sc/eKVF5++BrWX59CjDGQQyereOn/jvBfr+3lk33nqGloG+5mjjr+On/uS/83\nbpt0I00dzfzqyGtsPbeDUXJbjRCjikdOm2/atIkZM2Ywbtw4t71nWFggOjc/u9vXjQDC/UZqrRPi\nw/j69amcLKphc9ZZvjhSwl+2FfCXbQUkxoRwVVo0s1OjSE0MHxV3q4+EOq+JWsX0cSn8fM8b/L3g\nI0pbS/nW3PUE+gQMd9PcZiTU2RtInfvmkfDevn07xcXFbN++nfLycnx9fTGZTMyfP5+oqCiqq6ud\n+1ZUVBAVNfAjJjU1zW5to9zJqJ7RUGtjkA93fmUSN1+TyN7sco4Wmjl5roazZfW89/kpAvy0TEkM\nZ9oEIxkTjITpR9418pFUZyPRfG/2I/wh+8/sO3+YM5ZivplxF7HBpuFu2hUbSXUey6TODsP2qNgr\nr7xCXFwct956q/O1G264gd/97neYTCbWrFnDiy++SFJSUr/vI4+KjV6jtdZt7Z3knqvh+GkzxwvN\nVNe1OreNiwpm2kQjUycYmRgXglYz/KPykVjnTlsnH5z+hK3nduCr8WFd6mrmmGYOd7OuyEis81gk\ndXYY9oVJ3n//ffR6PcuWLWPDhg08/vjjAKxatWrA4BZiOPj5apmRHMGM5AjsdjvllmaOF5o5ftrM\nyeJaiisb+SiriAA/HemJYUztCnO5c/0irUbLLck3kBSSwFu5f+GPOe9wpr6IW5O/ik4j6yIJMVQy\nSYvwuLFY69Z2K3lFtY5R+enuo/KEqGBnkKs5Kh/pda5oruKN429R2lROUkgC92XcSZh/6HA3a9BG\nep3HCqmzg8yw1oN0DPWM9Vrb7XbKzM3OIM8vrsXa6fi/VaCfjilJjmvlUyeEY/DgqHw01Lmts523\n897jYMURgn2C+ErCYvx1fugUHTrNhT9ax9+KDh+tru9tGh0aRaP6RDujoc5jgdTZQcK7B+kY6vG2\nWre2W8ktquH4aQvHC82Y6y8ZlUcHM3WCZ0blo6XOdrudXSVZvHfqn3TaO6/ovRSUrkD3cfzdFeo9\ng/5C+Pe9TXvxFwSl/33Hm6Kg2XdMzyI3EoyW/uxpEt49SMdQjzfX+sKo/FjhxVF5p+3iqDw9Kbwr\nzK98VD7a6lzZXEVpYzlWm5UOeydWm/WyPx12K1Zbj22X7dtJh60Dax/72uw2t7fdR+ODKSiKmKBo\nYgKjiQmOxhQYjTEgTELdTUZbf/YUCe8epGOoR2p9UfdReTXm+ouTwYyP1jN1oiPMJ8QOflQude6d\nzW67+MuArROrraPbLwEdA/xiYLVb6ei0dv1yYKVVaeGs+TwVzZV02KzdPstH44MpMBJTkImYrnA3\nBUUTERAuoT5I0p8dJLx7kI6hHql17+x2O6Xmi3ewXzoqD/K/OCrPmGDEEOQ74PtJndVxoc42u43q\nFgvlTRWUNVVQ1lRJeVM55b2Guo7owK6Relegx0io90v6s4OEdw/SMdQjtXZNS5uVvCLHc+XHTpux\nXDoqN+mZOsHItK5RuUZz+U1aUmd1DFRnm92GuaWGsqZyypsqKW2qoLy5gvKmSjpsHd32vRDqjlPw\nF0frEQFGrw916c8OEt49SMdQj9R68Ox2O6XVTY7T6/2MyqdOMBLSNSqXOqtjqHW22W1YWmu6RukX\n//QW6jqNjujAyMtG6pFeFOrSnx0kvHuQjqEeqfWVa2m7cK3ccYr90lF5YteofFlmIsE+3vHFPpzc\n3Z97hnp5U6Vz1N7eT6ibum6UiwmMIiLAiFbj3rUfhpt8bzhIePcgHUM9Umv3stvtlFQ3OadtPXW+\nzjkqH2/Ss2haDFdPiSbQ32eYWzo2qdWfHaFee8k19QvhXnF5qCtaoi4Zqcc4r6mP3lCX7w0HCe8e\npGOoR2rtWS1tVrLPWGrx6JcAABSESURBVDiYX83B3Apsdjs+Og1XTY5i0fQYUsaFqj6RyVg23P3Z\nZrdR01p72an3suYK2jvbu+17IdTH6eNIDZ9EWngKet/gYWr54Ax3nUcKCe8epGOoR2qtjshIPfmn\nq9lzooxdx8qorGkBICosgIXTYrhmaozMu+4GI7U/9wx1x+n3istCfVxwLGnGyaSFpzDBMH7EzjE/\nUuusNgnvHqRjqEdqrY5L62y328kvrmXn0TIOnqykw2pDoyhMm2hk4bQYpk40jor1yUei0dafbXYb\npY3l5FryybHkc7r2DNaume18tb6khE4kzZjClPAUIgMiRsxZmtFWZ0+R8O5BOoZ6pNbq6KvOza0d\n7MupYOexMorKHdsNQb7Mn2pi4bRYTOGBajd1VBvt/bmts51TNYXkWvLJteRT0Vzl3Gb0D3cGeUpY\nMgE6/2Fr52ivs7tIePcgHUM9Umt1uFLncxUN7DpWxt7scppaHROJpMQbWDg9lqtSo/DzGZ03N6lp\nrPVnc4vFGeQnawposTrm4tcoGpJCxjPFmEJaeArj9HGqPqY21uo8VBLePUjHUI/UWh2DqXOHtZND\n+VXsOlpGblENAAF+Wq5Oi2bh9FgSTfoRc/p0pBnL/bnT1snZ+mJnmBfVF2Ona34Bn0BSwyZ1XS+f\nRKifwaNtGct1HgwJ7x6kY6hHaq2Ooda5sraFL46Vsft4GTUNjufH4yODWDg9lsx0E8EB8sjZpbyp\nPzd1NJNnOeUM89q2Oue22CBT1yn2yUw0JOKjdW8/8aY690fCuwfpGOqRWqvjSutss9k5ccbCrmOl\nHDlVTafNjk6rMCslkoXTY0kbH4ZGRuNe25/tdjtlTRXOIC+oPe2cw91H48OksAmkhTuul0cHRl3x\nmRtvrXNPEt49SMdQj9RaHe6sc31TO3tOlLPrWCll5mYAIgz+LJgaw4JpMYSHDN+NTMNN+rNDe2cH\nhbVnyLGcJNeST1lThXNbmF8oaeEppBlTSA1LJtBn8DdFSp0dJLx7kI6hHqm1OjxRZ7vdTmFJPTuP\nlXIgt5K2jk4UID0pnEXTY5kxKcLrHjmT/ty7mtZaci2nyLWcJM9yimarY54BBYXEkATnXezjQ8a5\ndOOb1NlBwrsH6RjqkVqrw9N1bmmzciCvkl1HSyksrQcgOMCH+RkmFk6LIS5ydMzcdaWkPw/MZrdR\nVH+evK5ny8/Wn8NmtwEQoAsgNSzZeb08zD+01/eQOjtIePcgHUM9Umt1qFnnkuomdh0tZc+Jchpb\nHPNsT4wNYeH0WOakRhHgNzJn7XIH6c+D19zRQn5NATld18strTXObabAKNKMKaSFT2ZSaBK+Wlkl\n71IS3j1Ix1CP1Fodw1Fna6eNI6eq2XmslOzTFuyAn4+WOalRLJoey8S4kDH3yJn05ytjt9upbK4i\nx5JPniWf/JpC50IrOo2OZEMSacYU5iRmoGn1I9gnaMz1ocGQ8O5B/g+oHqm1Ooa7zua6VnYfd8yr\nbq53TPQRYwxk4bT/396dx0ZR938Af+/OXm136UVLKaXllqcUuZEixQMQBCMBlFak+ouGxCAxGjwa\nsFaj8ZcSfWIUAl4kpGqogiDKqUK1v9BCOSzQh0uOSu+D3d677ezu74/dbktpfeTYnZ3Z9ytpujM7\ns/3sN9N9z3dm9juxmJ4U47nvuNxJ3c5K0+EQcdly1T1863mUN1fe8LxGrUGYPhTh+lDXb0MYwjyP\nQxGuD1N0wDO8e+A/oO+wrX3DX9rZ4XTibKkZ+cUVOHGhFqLdCUGtwvgR/ZEybiCShkZCrZbvB62/\ntLNSNdiacO76BdSI1ai01MFibYDZZkFTe7NnwJieNCrBFegGd6jrwxBmcAV+52OjNsSnI8TdLQzv\nHvgP6Dtsa9/wx3ZubutAQUkV8osrUVbbDAAIN+lx/9gYzLg3FtFhQb2u53A4YXc4INqdcDidsNud\nsLvn2R1d0w6HE6LDAbvd6V7HvYxn+W7r9Fiv1+XsTtg9f8/RtWy35RJi+yElKQZRfdROd0fP7Vl0\niGiwNcFss8Bia4DF1gCz1fXYbGuAxWpB438J+NAePfbOx66efBhMOv8LeIZ3D/74QadUbGvf8Od2\ndjqduFrVhPziChw5W402m+uuVqEhupvC0+Ho6+PXf6hVKtyXOAALkhMQ2z9E6nIU6Xa2Z7vDDout\n0R3uFneou8LdbLPAYm1AY3tTnwEvqASE6fv1cng+zHPY3qQz+nyM994wvMnr2Na+IZd2tnXYcexc\njWc4VrVaBUGthiCooFGr3NMqCILa9bvbtFql8iw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SMJEQEZEk/w/A0QemaEz+0gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "266KQvZoMxMv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "lRWcn24DM3qa", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here is a set of parameters that should attain roughly 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "TGlBMrUoM1K_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "ec62f945-4692-45a1-d88e-f0973f7df440" + }, + "cell_type": "code", + "source": [ + "_ = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=30,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.57\n", + " period 01 : 4.21\n", + " period 02 : 3.74\n", + " period 03 : 3.61\n", + " period 04 : 3.56\n", + " period 05 : 3.51\n", + " period 06 : 3.45\n", + " period 07 : 3.51\n", + " period 08 : 3.45\n", + " period 09 : 3.32\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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U9XbIwc6GebMHc3N/bxLTS/jrfw9SVGbe0ed6uoZws89w0soy2Jmxx6xtCyGE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\nPj2pnb8Yn8/HmcZODlW5Kavy4Pb2AqMnAflz7RR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "mk095OfpPdOx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Replace the Linear Classifier with a Neural Network\n", + "\n", + "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n", + "\n", + "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class." + ] + }, + { + "metadata": { + "id": "rm8P_Ttwu8U4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n", + "#\n", + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \n", + "\n", + " periods = 10\n", + " \n", + " steps_per_period = steps / periods \n", + " \n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " \n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + "\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " \n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " \n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " \n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " \n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " \n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " \n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " \n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " \n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " \n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " \n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "UXgyG_OVqXyi", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "8db61e5b-e6ea-49d8-9723-a426210d18b3" + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.01,\n", + " steps=2000,\n", + " batch_size=100,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 3.80\n", + " period 01 : 2.94\n", + " period 02 : 2.67\n", + " period 03 : 2.40\n", + " period 04 : 2.24\n", + " period 05 : 2.07\n", + " period 06 : 2.09\n", + " period 07 : 1.96\n", + " period 08 : 1.84\n", + " period 09 : 1.85\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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p53BmibnDMZmeLgE82mcOfxn2FEN8B1JQW8SHKV/odzJram4yd4hCCNGlSPE2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\nIk6vhFETCamEa+Zix3gTEYlcmEyKjGQdMpJ1eDx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RSmTZtNWqVSuMHDmy3mO+vr7w9fWVoxwRkUXSy6YtnkdCRKRZDBIiIpKAHQkREUkiGBgk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\nUXrpSBgkREQaZdB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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "TOfmiSvqu8U9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n" + ] + }, + { + "metadata": { + "id": "evlB5ubzu8VJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 346 + }, + "outputId": "63dd69c4-0350-435f-abac-0b0884f51f36" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZfzsTYGPPU8I", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "qXvrOgtUR-zD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we verify the accuracy on the test set." + ] + }, + { + "metadata": { + "id": "scQNpDePSFjt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 346 + }, + "outputId": "d8bc279a-c2c0-4bfc-bf86-7d69fd4321fc" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 1 2 3 4 5 6 7 8 9 \\\n", + "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 \n", + "mean 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "std 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "min 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "25% 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "50% 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "75% 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "max 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " 10 ... 775 776 777 778 779 780 781 \\\n", + "count 10000.0 ... 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 \n", + "mean 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "std 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "min 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "25% 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "50% 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "75% 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "max 0.0 ... 1.0 1.0 0.6 0.0 0.0 0.0 0.0 \n", + "\n", + " 782 783 784 \n", + "count 10000.0 10000.0 10000.0 \n", + "mean 0.0 0.0 0.0 \n", + "std 0.0 0.0 0.0 \n", + "min 0.0 0.0 0.0 \n", + "25% 0.0 0.0 0.0 \n", + "50% 0.0 0.0 0.0 \n", + "75% 0.0 0.0 0.0 \n", + "max 0.0 0.0 0.0 \n", + "\n", + "[8 rows x 784 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "metadata": { + "id": "EVaWpWKvSHmu", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ad45522d-cc83-4db0-b8d8-516c4edf9856" + }, + "cell_type": "code", + "source": [ + "predict_test_input_fn = create_predict_input_fn(\n", + " test_examples, test_targets, batch_size=100)\n", + "\n", + "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n", + " \n", + "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n", + "print(\"Accuracy on test data: %0.2f\" % accuracy)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Accuracy on test data: 0.95\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "WX2mQBAEcisO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Visualize the weights of the first hidden layer.\n", + "\n", + "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n", + "\n", + "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n", + "\n", + "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained." + ] + }, + { + "metadata": { + "id": "eUC0Z8nbafgG", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1172 + }, + "outputId": "4c4439f8-c553-41d5-f116-f4a2f0fafc3c" + }, + "cell_type": "code", + "source": [ + "print(classifier.get_variable_names())\n", + "\n", + "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n", + "\n", + "print(\"weights0 shape:\", weights0.shape)\n", + "\n", + "num_nodes = weights0.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights0.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n", + "weights0 shape: (784, 100)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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dumqVFg9bgt/TkMHnRBexZXeOxr7FzIH3oKJ9PfrNQ08IaterlFKOf6J+F+xB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\nFhb3WgBSkcOXIu18z8u7WFxsI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s7vJoyDyz++z+JarkFG+dkXSMt+9tMHWVzhRtjdZWXg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k6reGveHPXPE69Tbsf7Ji+ruv9pLfA6/dbtir/4AKDnPlrosj7quyEXFt2hCudkt6FL+iF6zD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\ntleOz0KfaSiBrGZVps1KNhSp07+5Galu0jZ5zkML6T1FnyAtbNE8pDl6iM8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eYtauJ4hdrWJLEuDogapY2nea2R7rGy3lrwWP69sjZlt1iPZY0Yef9hjDFdtThHvcKpMFQ4\n8xpjyGmqvQR9NNhWn6/pIsaVZxh+e1cVz0OZd+Oeq+B9uD8lXsW1Seiea5JxON31+P32Op2yHuCB\nF3HP7Wm715Brg/sB7O2ix3NdsOZzWJOky5G8NzOG3bCCJok6i8KtKSaEj1WuDb6peH4RnMHOjJ89\n/oXVThcuiwlhvD+PmoN6O1uf2mK1x8zluVK6BMq5LMCbn3nkbMX8MmqZ+R9o5oyiKIqiKIqiKIqi\nKMoQog9nFEVRFEVRFEVRFEVRhpAflDV1idRzrwi2CU5cPcJq73h8s9X2s0mKojKRetl8DinabQVs\nU5V+ywKr3VRaaLUnDmPryryvYPtot7/8b2R6mDHGNInUcJkOV2SzQW2fBllK/Bqkcjdkc0qdu7BQ\nO/YG5D5T7phJcflvIK22qBSfMe02lno4OQ9e2q8xxjRfQOpY66UGei1YWIclrsU1bbzAFncyDbN8\nW77VjlvONpnymkjrTSljMsaYaJEmK61bm07iPLl4cfcs2IZ0vvBkpJxJW2hjjEkT9mrewi679ngZ\nxUm7YmchubP/ptqTeF9rPs6fXV4kpQGOxisCqZv1x9m6s7MV6ZoR43E9feL5eBpE35fnVsqYjDHG\nIwRjeNyPkTdZvjmP4kbcgZTggk8xDmRKq6cnpzHKzw4TKcHl2/MpruwE5FRJ85DSX3OUZY4Bws67\n+CtIKWIWcBp2zWG8L3JWojhWCjN1+8XnD4IyprYactCa7TwWYzORNhuWgXPT2coWtkUfI6169NWQ\nqrXYxvaY6RjP5Vtx7QJGcrpmTwNSd8fegNTSg69CKnRqB9s+zv/FIqtdsQPXru0Sz+uRiyA1k3bA\n82awBLSpFMeX4oTf7mGTv1ZuwXdJS0VXH5b5dNSIsclZrP8xPSLVOXJ2Ir124kXIzCbch/VAWikb\nY0zmDRhXkfswTlsL+Bp6xSDNfcn9OOff/Y1T8BvaYAe5+BqsL7nbMCaCfXkNXzB9vdX+7S2wzl28\ngKUP+dlIHQ4rQv+1yzqnLIacZaAf64VvEkuiL30oUpRHoS/a+29fh5C+DYLD/UAfjnHsZePoNc8w\nzLeVewutdvCYKIo7/Pe9VnvYWFicTlo/heL6upEO7hONa5q0ejrFNRaif/slw3a0JR+p9yEpnEbt\nm1JptV1y0G4va6a4zjKMiewc2KoHhLHkuLgIa/CMeyEdl3a4xhgTIWTh0sa48MNsiosSc4CjkZam\nsWtYxpUSgmv4yaOfWu2Jk0ZQXJ+QiNSVw8L1hXvZcvvpjb+y2j4++K7iY19TXOblsLEuPrvJakcO\nz7Lap15jS+tlf4ScqqsL6/R9Vz5JcW4u2Ke8sg1ypdfvepriMuJxbX63Edbe66ZmUdzD911ntd/9\nM+SM0TbL28xRi8xg0nwRe3SfmAB6bdfbWIfmrMec2nie18WuSsyBUlrg7MaypslrYGm+5S+4n/Cz\nSY/auzDPf/A0+k+3uC/KbGdpysYDkEmvmoQ5fvodsymufDP2sjuOQNKW/5uNFHfl735kteUaV32K\n94ARMdjD7Xwbc5K7K++NZ0+xSXMcyEAv5rjWcp57Gs9gTgmbism85hDv56SNc2ctzq1HIF+b4DHY\n57qLOUBKB40xJlTc30gr7A5xP+Lqx3sHn3j0P1dvrF12aXfZ55AZB44VmwzbnlLaRZMU2JYaUStK\nNXSLPUbRRt57JVzOslZHEyhkPxVf854/6QbcW6VOwx7bfj8/7tapVtvVG+e3al8RxZ0uwr+dRDvr\nSl4XpXzcPRD3ENIG29l2H73zI4zF1Y+stNq1R/k+cHwm7i9cfLDGhUzksdJeiT4jx9WFvVzaY/x8\nlH/wjsLamldZSXFyjv53aOaMoiiKoiiKoiiKoijKEKIPZxRFURRFURRFURRFUYaQH5Q1dXTD4cRe\nQV3qAeKikZo8/GYuH33xFaSJltUidXHanZzmV7wVcbJCdP7XWylOVpKuOQjpw4VLaHtu5/Rtf+H6\n0y0qcQ+/id1NZCp2m0gJvrAjh+Km3o208bhFSDHLfeMgf55wK+qT/7AREJP4va85Au84pISFTeL8\ncOnM4BmMFKyiT89QXOotSAUt/uy81ZYp38Zw9e1kkQJnlydceB3V5X1Ccb2CJiEN8cLXLDuTzha+\nSUi7rdpeQHFhwl2r4RRSybpqOAVVXqCQTKSrt5U1UlhXNVLnouYjRbvueDnFNZ3HbzdTjUORbkZ2\nuVd0FqqI+8QiJbNiK0uFvIRjVpBwseqzOSVJp5KgYUj5q/YrprjCL5Ea31aE8ZJ5/3KrXV95hN5T\nvh+pix7BLIGURKTg+EgikcDp1u0V+F4pZQoawS5dstq//Ly6g5ziGLlkcF0pIpLEXLmOZZA12UiT\nPfz051Z7zD0zKK66DBKHun+hryYtZOcIOeZk29WWntvajjmxfAvSWFMn41z84a/v0nuO34kxt2Qs\nxvm4+/g3lX8HyWJIGo6vbP8xigufNMz8Oyp38NiWrjX9QiqS+7ltrohh1wtHIiW+JZ+zQ8zwlZCc\nOAlnqVHrWJ7rKsbw+JsggWnOY+eI4m+RMhsjHFlC/ViKMm0dPsNVuAGV1mHNrW7mVPOdp9632hVC\nLnbkIJ/L5g70j7oNkG21ibR/Y4y55okrrLZ0Sjj4PLs/jb4M5yJguFibm9hJpKe12wwmBZtx7Xy8\nOG0+8To4nki3vuJcvj4zfjbXap//B+Y6u8TVU0ihfQKxPtVf4v6TNP5aq11V9ZXVltIMNzeeA0Mz\nMHbkeZeyLWOMCZ0CiamUnjYL2bcxxsR2wyVFSo47yljGK53A0rIwtqOXpVBcY7aQSLOa8T+mrQjH\n0FnN63vGjddY7fV/g+OJlxenq+9+FNKhHLEv+eVbP6e4Y89+Y7Xj5mDdiJjMcqoT775otYOERODM\nu5hDg8axo4v8O6m8vu/tY8nUpp/9zmqf/hskU3kVLHOZkIq529kZc80j91/P3yqcjH57L14Lncxy\nZD+/wZVSBI/D/mvnn9npzFn01Q4hl/GK4jnQMxrzcusZSL3tbp5SIiglSgmxrH9NHolxunc/HJ6k\nxH/MFSyHrGzEeuwrSjz0tvNc1lwHiYSUNyRMTqC4biHzkZKf0JF8rNs2Q1a+8mY4/l36lp1Hd/4D\nc3HyxOuMI5HSdPu1kQ51BUL2GLWI91sNQrLvK+RFoVO5P554A/eLcaMwnuV8YAz3HXchqZflF1ps\nc7qUKjflYk/v7WX7Tb74TdLt1V6mIigN9zT+wzC2yzpZglV7CHOK/3DIqeznsvYY4qIHwdDQOxrf\nV1rLa0N0Le6FLh7A9U4ey/22S7h5Fm2ALCtwDPfbsyW4b7/5GlgW1R3ifbmnKOvQKty15PzaZruf\nmCAcLfPehntdQTWX7Jh6OZ5Z+CXi+vS08ZjtasY1nvsI3NvyXmOnR1kqoU5I1WYu4ecNETaHPTua\nOaMoiqIoiqIoiqIoijKE6MMZRVEURVEURVEURVGUIUQfziiKoiiKoiiKoiiKogwhP1hzJnE4tHK9\nHVyXQtoQew+Dvjr3jaMUV1oFzZ608uwR+i1jjKk7LS2UoeWz6/ekFW/kLGit2wuhRQ21WWCVfo6a\nMWl3ohhIdwtr3GuPwNbNV2jP0ueztXK7sFB280EdlGBRL8V+rKPioKe79AXbqtYJa6+JN7EFpyPo\naYF2zm4nXSXq9iSvgwWY3W63Yie05x5h0M+32vSaw67FZ/R1QVPZ18n9J0Cc39glwja4HrrxMH+u\nGxE+LxFxVbgGrS2sNZdHHrcEWvPqo1y/QtZmaMqHtlLqDo1h/XXjOegVA9JCKc4rgrWhjsRH1NCo\n3sV2dFKT6uYnakPZLP18hd71+IuwpwxP4mstLSmdXU5Y7d4W1mCWFWDMynpAR56B7WSgrUbM2PtR\nk+TCi9ANe8fyuZP9b8cbu632hKlsg9peCu32ycJCq+3vxfpTWWMhMAP1aOTcZYwxLu5su+lokq+E\nTXHuB3vpNRdPfHeTqPOR90+uzzL2JrY6/m+k/aIxrNu96qcPW+0509mmUOqyH3ryZqt9aiOu/UN3\nX0vvqTwHbXjsdOiNc1/jY42Yjzna1RXzf/NZtkENzpBWlDgeV1+27z2/AbajIVHoW+22+ifl2zFf\npc81DqX8K9SB8QjnWlruAahdUnMEc6vdzjXvY+juIyejDljl4RKKcxF1a2StKXsFs75O1E4o+gZ1\nBqYvRE0EOf6NMab0E6yL3omYXxbO5BN29iPotcPC8BnRS7m2SLuoB9Hfi7olsx5eTHFHnoUNeOQo\n1N6IymLL5b6uXjOYZNyBvcCld07Ra3J/En8Z5pyWIq5H9tGvUfdjxb2wG3Zy4b97bXthu9UekYTr\n4xXHa1xTMmq9ubhAZx8zCrp4d/cQek/xAXx2nLBKbqrhunFdDVgnPcX+zW6RLes2HDuI+nJjxwyn\nuARR46PpFNbFS3u41tmUB+aYwSJmOeq9VO8ppNfyvkHdrsT5WVa7onAzxUnb5ZXP/NZqf/PwHyiu\nrB57nfET8XlubmwV39OAvpP9HuoRSKvmTz7fTe95+H3UFVsxbqnVTopi6/YWsS58eBCfcamEa85E\nLZa1PNAXw6Zw7Y6ACPTtjo5Cqz3Q309xR59/yWrPePA3xtHkfYS+Gh3M59NfrtFibfAM5VoyZgRe\na9yEPUyTzXL72B7U1Fp86zyr/fpTH1HciulYZ4eL6xAajuPpquO954Ifox7lgX9hf1Ozl+v1+fph\n3ehuxB4mYATvxUo/wxx9qQz1E6WlujHGXP3YZVa7KQe/N3U11wry3Mw1aBzJgOgyNXt5HZNrRcx8\n7Pfrsrluo6yrI9eQmgP8eelLUdtN1i2rs1mMy/qCEdNQoKXuNOIi53K9ux6xj2o8hT1uqai/ZYwx\nw9ehLpmssVK1j6+1sxvGn6xFFpTBdRF7xfdGzsYxFX3EVtrxl480g0n1fpzr8VdMoNfkujbtTvR1\nZ9u+We63zxbgfKy21Xm9eibuB/xTsa79j/t+cc+ZuwN9OExYeF8s474U6IP5YfrPMc4zPHnNzf8X\n6jV1VGAsBmVyXbD4aVlWu6sLYzH9dl7f/P3RLz687xGrPekyPpf5olZN9GOrjR3NnFEURVEURVEU\nRVEURRlC9OGMoiiKoiiKoiiKoijKEPKDsqaqIkiSKl5j+6lRV8AO0ycOlmduvu4Ul1eA1PoTBZCV\nFP+zluLSYiAJkrave9/aR3GXP3uf1W4sR3pi/BVI9fIIYknDxAfustr9/Ug57ffhVHjXLCFzyYPM\nJXEup3nXlyIFs+ogUrYGejkV1D0AUo+cd2AJnrSO05tqjhWawSRkPM5tVz2nYUaINFePAJy3umOc\nHijtlr2i8bvsVnjSClbKuuy2njLtzcMX6WytJUgbl7a5xnCKYvRcvNZrs1ztqERqWl0/UvSip7Kd\nbVcX+nTp10iVazxZSXHtInU15Tp8hrMLp97ZpVuOpLUE6XuVtQ30mms2xpybkFW4if5njDFFnyBF\nXabFnjvF6ZoVDfj8c1vRb0NsMrNr18KyMf8MxsHILMgA8/dxirvHHozt0fdBBnD4mS8orrEA/eiv\nH39stecVs6Rn2XikSWatxGvnd+dQXNJISEe8ozFf+caxrKm/h+0NHU1vt5BE2q7Puf3og5Ougowh\ndDTLR+pzMY9+9JcvrfbUVLbSfvFr2LDuPAnb5H7bPLXz7zut9kkhZUqZkIhj9edjnfwLXHtnZ1xT\n32EsmyzdBKvggGSkH0fZLMuDwvB7m9yQ7tlexPbPo6/H3BkQh9Rfv70nKE7abDsa/9FIPe+uZ2ls\n/SnMHdIuu7WA5TBBiUjdd/XBuiNlTMYYEy9kG7vfhAxudAZLgOTcmFeJYzhwEX0qOZLTdLMewDW8\n+AbOeW/b989jAeK3t5e30GvnvkH69YgFkEvkvHSQ4kbfAD/luhNYZxovsvxgsGkWa7yU3xljTM0B\nSJw7hIw5bg1LnJfcgr1Bfw/GVX87n8PJyyEvk+tV/TneV3lG4FzJfUx5Cc5tdwNLwuNXQErc3489\nTUgUyxeLy7dYbd8ArJ87n3+N4tLW4vOWLcOccvKffB3DY9CH40SqffU/D1Bc/tuQjEU9/D/Tt/8T\nQuKwHj/0+p/ptadef8BqDwxAInfqH4cobsLNkLed+fAtqz3iyjEUly7GmJsb9ixtzSyXnnzPL6z2\nnsd+b7U3HsB5ue+p9fSeR6+4x2p/tP9lq/2vn79DcXMuw7HW1EDOJiVXxhjzyu8+tNqPb4JMyiOA\npUDZf/8Ex3ocUsv4UJZsd/agP88wjif1apxrOYcaw3JqJzdcxyqbjK1TWL1PSsb6MsDLnVn2EEoM\nFLwLi+zlE3lf7h6CvVREINY/9yD8/7ntF+g9oxZDRnShDGvhjOumUVzdQV4n/5uyL3Pp3zErMf6C\nSjF/D/Txj9r/IiRusRG4djV1vO7EjxsE7+X/j/Bp2GO12mTqTq5Y18p35uH/bdJ7n0TszbzFfWVA\nCvfHok2YDweEtbl93ZdSXnnOZDmGiAl8n/H1byFviwrE/tA31Jfiyrfgd4y6bbnVrnJiiWxYBq5h\n6W7x2gDfE4WKe7HWYlw3zxg+l6VfYm8be69xOGFTcR37ungd++5FSJITwyHLip7P+5G2AtxD9PTh\nmnTWtVGcfF7QWYv7rLwThRQnZetj52CMyWsa2tJK75HrU/l2XKvgcSwVzT2LMhETr8DexMl2f5f9\nGu5DUq/Hul959DzFuUxGP1n6e8gNezr5t1fvZvmbHc2cURRFURRFURRFURRFGUL04YyiKIqiKIqi\nKIqiKMoQ8oOyppAQpJUN9HAaXWg6UrUu/BPplbXVnEaXmYV010WzEq12/hucht7ZjrSloFSkVV31\n3K8orrdXVFOOQfptZTYqo9vT9vu74Y7UUYvUInc/TtWPHLbQagcG4XiKj31NcTKdLXoxp+dL/OLg\nQNIYhPTl3i5OhW8UTlVmqXE48nc22tKo+4UjRuVupOfa0/V7m3A+uqpwDquPcHrmqLuQdislIj6B\ntnTKAUjDyvagHTMLla6rT3DKaPMFSOFk1XDfZK7unzAFJ7GrCxW8PTzYTauhACmkIROQ6mZPjWzK\nqTP/jk5bpX6SxHx/t/h/okm420y+fQkkgbQAACAASURBVCa9dvQfwnlpJs5z+RmuXp66EumARVsg\ndziUy6m0t9640mpH7kFa54iRiRR3aD9SS5//EGnUs05ConLrqiX0Hr8UXKu+PqQhDluaRnGnN8Eh\n5pdXX221fYXjhTHGHLsESZZ0aEqfyRKfjjLMG9lvHPnez4texhIiR9Ms5FpRWSylkNLB6u8KrbaT\nGz9Dr96JNMz5c5CK3V3D/fGeZUjf9o1GWvB3j7OEbNUfrrTaxZsxFkmmacs/rtiNOfDUdvSDkRO5\n47sLN6P8tzDn+6Wz48zFi0ivT1w422r7pORRXNkXwm1iFY4pdDyP7d7OwXP6CRqFeX3f8zvptZSJ\nuKYewk3k4JaTFCdlA7FL0VftkqLjH8L9cEQKxrarbe3yjkbq8+TpGOd1+ZgzD9rG+VxxTWuaIR/r\n6eVzFx6JMVtzGPP9qHumUlzg3kKrLR3kAq7mlHSPQPQJN3+kNXtHcvp2b/vgyUSNMcZFuPW15rMs\nRLrHDbsCkou+bpZCy/1EWzGkp4E215WybyHvPJqP9oyx7LzRUYE5UTpW1AqZcdhkdqN0c8Mc3dOD\n39HayNdbuvd1d2NNC4vnsSgp2QjpeMLkBHqtvxdp+bViHyCdMYwxpqeNz5kj8fTEuj09jdeQL57+\nymoH+uy02pkL2cFGOqmdO4hzdt31P6W40tyNVvuhy7AvvXbObIp7YvcLVrtcyI3kWnPg9f30nhvv\nXmO1u9uwVt366vMU98CyK6z2Hz+F/Om+N56luPNCCuzigvH25PX8eX/64l9WO6kessk+mzurbzDL\nFhyNlKZ4x7J8Wq5DDScgeSotYxnkmLWQuDV8gXFpHy+d8h4gVJRAsMlMLmVDEj92LWSJ0gkwKZP3\ntVL++8Crt1ttWf7AGGMChFNP7wHItrrbWaLvGYI15NNn4TI2KYXXWSmH7RHy+sg4mzytjKWojqRX\nfG+fbe52ixGlL8TaFT6R91sNuTjnck4OTOX5NHiCcBIW5yw6iyVK3t7ot5XncY8YtQDnr9nmdDbn\nPkhWijdCsuLsyvuw1BshC+7vxzFI2ZsxxuS8vctqJ16Ouaenja+1syvmoWIx79rLNiStZ7mlo5FO\nhUfeYQloutiDSHfi3e/xfLbmD2uttn86rp2LBz9ySLoOv6XoE/zm2T+fT3FVBzB+tnyC+50layDd\nPVvCjl5nivGeOaNx3p1txxARgL658x3MgbMuZ+fkoLGQFTaXFVrt4XMv5+/94E2r7SX2ZR7B7OyZ\ndvsk80No5oyiKIqiKIqiKIqiKMoQog9nFEVRFEVRFEVRFEVRhhB9OKMoiqIoiqIoiqIoijKE/GDN\nmdYmaDPj5rHG8eifPrfaw1bBNrN7M+uLExdDl97ZBr1o6q1siVvyNbR97u7QSRbu2kpxvgnQVwfE\nQKvfJqzH2gqb6D3hs6GVTpoMzW5d3R6K6+qChrX0KKzpkqb/iOIiM6CvPvs6zoN/Gus7pXWWq7AM\n2/HUNxQ3/xGuy+FoXL2gdZM1ZowxxicRVnNeYdC3djexXefZDaiZkDJfnPcSPtdVou5A+HScd6lx\nN8YYP/G9daKOQX4ltIvNxVy/yFfUJCj5DPVowoSFnzHGVFyA3Zt3BN7T3c0a5eZcHFOPqKkTMTuR\n4qgGjdAldzdwXZ5+m72hIwkeD229u83WeHgWrkfuR6gZ0trJ19AjGPpqZ1FvYu0U1lY++fy7Vjsq\nCNdpzMwRFJeZgOv7yHpYg54qQk2UtJuz+Bg8UK+jcNcOqy1rZhhjzHOfY1z5eaP/rpvJ9XaKanBN\nN+xH3wk6zbaHP34QGtiOL1GbJfkGtldvyK4yg4ns9/lvcR2S8krUB0mfB8veyNETKe7oe9BOp8dD\nYz3i+hUUV7h9p9WWtWSWP8W1FFxdobnNuBo1bFpbUd+lp6fO9h7UBdj+KSx2E1ezHWlQEDTB7e2F\nVrsm7xjFhaXgfY2VZ8z34SzsqQ+9Cu1x0mieA6pyUVsr7tnLjCOpOQRtc/IYrjlQew79J+cQ6uVU\nNvJcNnUJ+p2seSFrKhhjzIx751jtClHrYNc2Pn9rx+Pae0ai749bjTEbtiWC3nPsJZw/qfF28XSj\nuJoj+L2yXoC0EzbGmHEPoGabrB1QsomtJsfdd4PVLq2HLajXZD6X9Wf5XDianhbM+Z4RPF/I2hHS\nKj5/A593X1FDK3wK+uCOx7dQnIcbzmmgD9ZZtwCey2XNk+Hp+DxvsTbXH+UaCc5umEdCxmKdaC3l\ntdnVS1i2u+Dzkq/m+b/sO9SQChwHnX13Pde0ChyN/iTtxks351Bcdx3WyfiHjEM5+EfYZ9/40u/o\ntd9deZfVXv83fPErtz9OcRnx6HdxIejTF3awxbiTqOtxx92YUzpKmiluzynY5T501VVW+5oXX7Ta\njY1cc/Hih9jnNov6ckXmLMVdOR3z6bRE2DNv/IxtxL2F/W5VCfabT3z8V4praYF99tM3/M1qX7Vs\nDsWNupHXfkdT8TXmys5WvofIq8Q84O6K+T9c1Iowhms+Za7HmtlWztenXcTJ2lItOVx3Ki4WdWH2\nf4A1Lj0N+55Lp7mWTHA+1sn4ZaiBVH2klOLG3I/z2yt+r99wnlObcrEnWHpDltXut9UADR5A/Y/D\nnx232gtvXERxlTvZ9t2R1OzDnB8ykWvAddbgXkjuQ8t3cv8OGIFzLvfdzq68JkmbY1nfJjyc76U6\nOjBXyvkvOBY1hC5+9hW9py0PNtABY3A89j2qszM+r7Md9R27G3nfnbAWdcVqjyNu2LyFFFd9EfVd\n/EehTou0pTbGmBpR3yvWwbUtjTGmYgtqonm5u9Nr8l66oxL1i3w8eB3b8TjqtI6aj7nDJ5rrSck6\naNGL8GNKRU1MY4xpL8V3RYt7kj+/gFqXo+J5//DqR7BEHxkLm/IPXuX7/gdfuNVqV72I+kAHPzlK\ncUkRWO/kPc60qUUUN+E2rDu7Hn3KaocLq3RjjCkX9RMjH1tp7GjmjKIoiqIoiqIoiqIoyhCiD2cU\nRVEURVEURVEURVGGkB+UNcXNgQ2Zly3tt6Mb9l6+cZAa1bWwVdvZF5HeFJCJFDFpE2mMMaGTkcIr\n08VkuqwxxoxYcrPVHhhA2puTC1K0h12TSe8p34Y0rbxeWAfWH2OrYWdXpAN6hCPtN3/PRorraUYa\n4rSfPmy1cw++TXHBYUgX9p6L35cwj6UZJXuRMhnFrn8Ooeogfr+PkIUZY0x3I1KOg9KQwuwZzCl8\nk++HXWSPsHYr21NIcZGTkbrl6oH0xa4mTi2VNpzS0vS7nbgGdpvjGWMhffCOR0prWyFLBlzF5/nG\nIO08MJCldEUipVemSXY3c1pizELY/ZV8CTlV/CpO9c19RaTB/c8stf+Ikq+QAufuzamGDfU4t54i\nfT4ykK91Zy3S0uMWQw5j/72LqiG5mHvjLKvtFc5zwLHdSEnt70ea7W/eg81oaxWPsZCR6Pv9PVtF\nm+3LvUWa5NhhsCeecxunW8/smmG1H7oHFqarJ7FN3d43MT+MnoDrWbHjEsVFLxiEPFGBtKuPWszf\nFeuBNGh5TRrLz1HcwseQUu/ujjRoT0+ePLob8Bmjr7lOxLG8paUFMoSmGlzT6n1I1yw4xqmb0rpz\n4RVItS/8gtP1T+dvt9rSVjZjLo+doESsG1K+EzKJf9OAkA76pSC91T+F08HLcwZPEnNCyD7m3s22\nwZHCHn3Pc5BXLl82jeKChfzk5F9h3xgQxan6tWKNKjiH1PhFV/EaEpSAvlN/Cqm5Mj34Dy+8R+95\n5KfoE60lmEMvfsap5iPWYT5wE/LcrY99QHEz74cFaWsBPk/O78YYk7/1S6tdkY2087ilgyudsNMq\n0tedXHk/4h6Etavp3BGrLeXJxhiT9x3Or1x3vG1p3r19mN+a2zEPb/pyN8VdKEPK+omXYZX89N13\nW+2pt86g9+z/O9K0Q3Zh7KRfP47iBvqx5np4YK2vL2DZmUzfD07HHjDvvX0UJ+V4n7+JcS7tTY0Z\n3LFoxD7Sw4Ptdtffu9pqd3Rg3r3tH49SXM5G9Mf9OyBJmjH7FxRXcPgTq90nbHAby3n/8f5rv7fa\nr78Mee6oj/7LaicuyqL3fPApJL7XXQ0pyl9f4b1nmD9kAfsuYZxvf/SfFLfxIPaUN8zFuBx+Ldvy\nfv4M7JmvXIS1/vl3P6G4uC3oY3/avNk4mvjLIf1oKWQZR9NmjJdJd2Heqz/Be4v4BdhvVxzFdXS1\n7Zekta+06fZLC6a4OiEfDPbF3kfKVjJX872GewD2rP3isyf+kksj1Odj3mgXsji3QN7znvwUksXh\nYxKttpMYe8YY4yEswbt7Ubqg+gDbCzfksTzZkYROxd7f2Y3/7u8ZivupPlFaISgzkuLOvwHZ6Igb\nsd8/9ufvKC5mqpCwCIl+VRVLlFxcMJdFp0JG1NgICVHUnER6T/d4SLKkdKetlO9hApPwmlw/5W81\nxph28RlBo7D3qi/lvVKrkNv5ivsbKVU1xpizL2Js/x/23jO+qjJ7+79J7733XggJJCH0DqErICCg\ngArYYayoo85YRnF0LCP23guIUpXekd5CgPTkJKT3k3rS/y/m/9vXWvs38nw+j4cnb9b31cKzzsk+\ne9/7vu99XNe61GJldlyTcH/YN3IZUuU+rC+lVZDceTrz58XCKsi7+3qw7jQV8vHXXo5zEzgV7RmO\nHslgea6ktUHq5EQttiD7UAedBGvNkiVa3NCC3xHu/wuXudddwH2e/twsLT7z+iGW556Eazd9MtZF\nvXX61R34HSB1Lf5WVQb/TlGreAsAPVI5IwiCIAiCIAiCIAiC0I/IjzOCIAiCIAiCIAiCIAj9yHVl\nTbUnUWIbODuavZZyL8q0r7yPMquoMVEsz38c/t1hRGmRpS3/0/bOKIkzHDykxfryuK4ulH7VlCLP\nkZSB0TIypZQyklI+O2+UR9WV8fLJuAUoUaTdxamMSSml/EZDjlBbi3K7kORZLK+9HZ3cnZxQdr77\nae4qEH3zQHUj8R2BUvuWct6R3iUCcgBTPc6brbsDy6s4iHK2gMm4pvYOvHybnpu2Kvyti1+cZnmF\n1XBTGTMG5/39DZCdffsiLz8OmjQIf9ce36mzs5bldXaipM7SEuWoJRd4yWPQdIxp6sTQmMtdnWrP\nQU5gQzrNF3zFyxJj7ueyKXMSOg8l/3q3JqudcPgw1aAE2FZXbkflIj5jURYaNIZLgEaTc0HdWZqL\n+f2SPAKOQj2t6JjfXE4dj/oUpcyAcmk7Ih1sK+f37PNLUZK46dgJLb78/XmW5x+NUsO5xHWqvIEf\n6+AxOFZ7f5RgOoe7szxTLe57FabMzqXDkBC07eHzSvr9kMg4h1BJGpdc2NmhzJW6oNVVH2Z5kfOI\nI0Q35s2Dz77D8mjn+fSlKG13JlKhjF+4/MLaEmXVIc0YSwGTIlheRzXOZ+qEFC0OG81dFc688rEW\npzwBuU3BHu7WZ/gdYzhxKcpCqWRPKaXS1oxVN4phN+N7UIc7pZQq/gUStFH34xhsPfh8WrIN48DZ\nA3MULQ1XSqkW4tQwaHqCFuvdhYzl+eq/QeXDejcDYw7GjlsCJMeZJdyBJKwCc7qJrMeJNyWyvNzP\nUJI+6CGMvbYafi/SNf3EdtzPlcd1TiJ9fO4wN26D8J2vbrnEXgseBDldZTbmMw8/LhVNmD9Yi89v\nwPePTNQ5T5Fy7txyyDHigvj1LqnFWvb9OuwTwhbjXO9/bQ97j4s91qSGVtxv+n2QPXEu7OlBnt5B\nw8GBXG8T9oDRS7mU7tdnv9fim5ZBOpOzL5vl9d7A6+g6CCX4tdUH2GumKnzHLx9CqTl1/FFKqQUv\nQHIygMiaSjK2sbx24vrTTRxigsaGs7y46Zi/7iJSstpzKJ+Pmc2dPbNLscc4eRBj8bFHlrC81U++\npcUHX/hci78/dozl/eO1+3F8abhu2T/tYHnznoP0i8rePr9vKcurr+AuZebmwkd4hrDVXZ/UeyCT\n2/0qXNAm6iTOxjLMH/u/xfm45YW5LC/vM8w5ztGQMlXr3OHil0IWeOFzyGCiiMOjjU7OUbwF4yd0\nLu7Z7C/4Oha9HNJE5xCMhX0vbGd5wd54rZXIn6hLpVJc9pGUSOTSunsvagmXYZkTO0/ybHWRO8p5\nJEK+VHcOcwp1FlRKKc9YzMl9xJAqdslgltfVDHkedXsdwLdKym8U9vhNTbivyvZDVmbtwvfJOXux\nNkeNw/sj0qewvNLTkPrR56jGbH5taGsOz1SM7eqjJX+Y10320wOs+DNw3Mrry2H+LMe3416fcu9E\n/iJpXxDli73P6eNcCh3ogfvKg0jXTHV8n+YUibxmA54Xh8Xx3xvsyN8qPYvzdiYPzz7UEVEppeZN\nwLzR0w4pXdlxLtEPnQY51S9PbdLisQtGsLz2Mtx/u3+BxHfug3wva0f2hLU5OC9ZW7kLKXXwvO/z\nm5UeqZwRBEEQBEEQBEEQBEHoR+THGUEQBEEQBEEQBEEQhH5EfpwRBEEQBEEQBEEQBEHoR67bcyb2\nAfTQoFZhSil1bTvsV8Pmoh9G0WZu+1p80qDFyfdAw+XgEsoPxAraw4DR6C3SVFbK8spzoN0s+BEa\nwoiFeE/WDxfZe6qItuuzl2D5+Mamp1melS00ky7h0Py1lHPNvJUV+ttc2w/7ZAsbbkkZQHqpFBzf\nqMVdxOpOKaUMv+JcxnBZt1ko3Y3jMlW0stdcE6HxdIuHfrt4C9cQdlRCK2g1AzrboJtjWV6TAXrL\nmuOw8Suu5X1hTMSKffc+9KNZNR/WYxU1vD9OlAkaejc3HGtTA7coayJ9GqwccO1pHyGllGotwWvB\nk4fiWHdxy1DfZFjrUQmvta6nS/UpaCF9/7eE8E/RUU97ydiz16gmlfYsGDGDayZbCnA+fVOhs+zu\n5vc2tVKtPQM9r6mU9zA4V4D+Hzc/At2lLbGTbC7hn21hiWPN2ADtt58X7/3S3oZ+LBMT0Gsjfh7v\nc3GN9NtJHI++Mq7x3Fb1xEfQoEfEos+DfkzQHjs3grhE9CcouMo1xxU7YXk/gPTl8EjjVordYbh3\nvnkG88q0Wfx69/WgD8m7n6PXj5Ult+F86NFFWvzg6n9p8asPrdTirFI+D88fgb9F9cDVp7h1p0ca\n7h2XCOiLK/N4jwRHYotdnoEeQwFjY1iedxquHdWXG37m644P6d0SyFtC/Gku/IZ1p2Mr78Uw7g70\nEmi8ir5aeovUoJmYNw0bL2sx7WWhlFKBk/H93dxwzi988jHLG7ic9I4gduNVhw1aPDV1CH2Lck/F\nuLryPfpn6a2QzxB7dCc7fI/k24ayPJ9RwVpc8TvuS8dgbg9efww9V9KILeavP/CeSUtfvlXdSJry\n0Adm+MO8f0XBl9hDxMzB/EP7AiilVD3pIxKRgO9v48V7DIVEYnyvnYsec23XjCwv5jSuCdXJm8j8\nH+DO58qaJmjhqcW9UyjPs7LDdq+zE+u0MY+vze2u+I523tiXtdXyfdD41RO0eP/b2FcNncnHGb1n\nzc13H6OP3EMpd7PXLh/HvmpsGvaHPaYelufojv5CrR1Yd555YD3LsyE9DVZNnqzFA2/nNsnFGVu1\nOGg8zoUD6e3z9Ly72HueuuUWLaZtM5be9zzLe2HZMi0efCd6xQXE8zXCNRq9Stbd9rgWU0topZSa\nn4L30f5URYd3szxmNR+pzE7icvTR6KhrZ69teQV9WKavRA8MCxv++NJGegKl34X7ubWcWyC7J2HP\n6zMSvaFKM/ga5+SPtStiFHqpBUVij3rq/VfZewKmkH6UF7EW2gfy3jRn/7VTixNW4jqOe2wyyzvz\nDvqaBMXjeIZP4otaG+mHsfHHfVo8Z9JIlpf3A9au8CTez+jPUn2C7Gd0zV/yvyRrSDj6dnXW82sd\nfQu+/+UP0fMpeE4cy7Mk156+1niliuW1VGCOb8rBnNdNetb8soHbdId5Y+/Y24m5ouwsfy6wIHbm\nvV3IM16pZnluxIKZ9rb0GcP7kvWRh4uK3dgLWrtwi2gHfz6WzM2EZXgIvbKRP0uHj8F9cGQLnttS\nB/N9mkMw5rqOBlxj10hund5cgnPlHoFepr4D+d6C9hW13Ik912wyJ7v48d5pnWQeiVyB/lEvr3yX\n5blno0daehKe2Q9uPM7yaE+bvAqs+wvmPc7yVs2cqcU3PTxNi5OW8+/U9cUpdT2kckYQBEEQBEEQ\nBEEQBKEfkR9nBEEQBEEQBEEQBEEQ+pHrypqKN6HcWlnwMrW6CpS4RgShFGj03x5gec3NKKNrzEYJ\nU0AkL1M7+8HbWuxOSi2pREIppeouwu4u8SGUkBdtwN9xceQlxS7O+PcLn3yixdue28ryfFxQFhU9\nBcfnN5xLd6ytUZ4fMAGSLktLLjfZ89yPWhw7GtZgbR3cQjdyIrcNMze0PNpvCi+HrNgLaYoNken4\njOayM1qin/0xbHVrdDapfsEop60qRUmhvQ0vzYvxxzX2JvZ5l06iFDn1juHsPbTsj1p8evqM0+Xh\n+HraIRPobOAllAOIxCb3m0NaHKD77lSOUXsOJfkhN8ezPL30z5zYeUE6orehp3aQNlkozdVbpHY1\nY9wZiIWtg67k9tJ23EvtRH7m78ZtZOf/HVIKCyuUeJrqiFSol1s50lLQwGCUj3Ya+T0RcxusEx39\nMKbKD3HpoI01zoUDkU+c+oSXJAZ5wuqQXndf3bXO/5RYdc9WZieESECDZvFS0KZ83C/NRIK26ysu\n96C2q7NSUQ7+8IvvsbyPP/6rFt8zF7IzagevFD8fgyNQtvrk25/h/VOnsvekkXuTHiv9LKWUciCW\nz41ZKCs+tomXdPoTqYZLHj5PLzszVUFykZNh0OLxf+GWjy0lN+5eHHMf5pvqo8V/mHdqN+SW45eN\nZq/Raz1wJa5N9WVut0hFDgVHfsZ/teLrcVsbJGyOfhjrEYsQd3dwG8uaMxhHISPCtNg90ZfljY/F\n/UflT+5RYSwvYzukEInEStvamtsGZ3wLKbCxDce07JVFLC+HWHMHvzxfmRsmQThfxl5razdpsa0n\n5l5qba6UUpcyUX4+dgmRFercoy9uRnn46DU4N05BXPLlHI3rVbYVdq+XyDmj8gallPJyRal4WzHk\nDfUXylkelb8aL+E62vlzO3i6ztLvETSDz1fUqjtxKPYwejtbem79A5VZWbcNe7gr2z9ir93yr0e0\n+PJnuHeo7E8ppSrOYr2bfdckLZ7rwC1SA5NR7t/cCBnlA9PuZHnv7oTksOA3yL3aijAn/eW1O9h7\n7L0wTzq5YI1Y18bv2YAknMBrW1CO39HeyfLiXHGtHngLf6uzma+z1k6Q8TZmYY/nN4Lvz62sbqyU\nonQz9n1eY7gMzo/sOyr2Yb/qEsfnlaZsSB/siUWzXhby86ZDWhz0G/ZOQxKjWN6V9ZAH+U/HXHFp\nM9bZyFuHsfc0l+H5JGgE5KHFh4+wPCplai7GnGLjxtfm9H/gearZiL1P5ZEilmdpi33V4uWQUmQf\nzWV5gV6e6kZh74cxUr6/kL0WeTv2c7ZE4tts4POppSWe1ez8MC9ZWPN9BZUH0bXexoM/+zVkQH5C\nbZtP7sJ8XNHAj8HBFvdERxX2H0GT+X6frmtVmVjr3ZO5dKe9HPOkawyxRi/jcruqvfyc/Q8+w4LZ\nv3M/OKPFkUP12X+eevKMM2QVl8rT62C1HWPum18PsLzbJmKPFDEdFuQdHdxm3NIOUiHDTuwJ9c8k\ntmTP6jUUc2APkYH7TeTPtlROVXMae51H193J8i79iD0/HQu6ZUw5kXHx6DNLtbivm68n9qQthFMA\nxlxHE9/LujvydVePVM4IgiAIgiAIgiAIgiD0I/LjjCAIgiAIgiAIgiAIQj9yXVkTLYNtLOalX8n3\no2SPdqou2L+D5SXMQgd90gRbdXRUsrzQ+XBE8PRCSZReKtRh/EGLXVxQKtfVgFKv2kbugBAxGiWJ\nc6agxKqykZe+D0lGaa6FDSmbq+QdwGuqUcpMy6r0Tg6dxJUp+xjKCweO5yWjrUU3rgRfKaVC58Kp\noLWC/y0v4mriQMoSe0zcUYo6OdGysvZPzrA86v5Esa7kQ23Q3SgHpdKFgS0oz+3TSWIaMjFmakwo\nU7Pz1MnYIlG62dGI0rY2XRkhlW7Vn0Ypn40Ll9KV/IQSZrsgnKMmA3eTGqCv5zYjtGt81gen2Wvx\n9+FcJilIDD965GuWN38Zxr6VI8oJc3Zyp5vE2XBQKT1Iyoh9eKkhLSdtNSC29Ua5XuN5fp9TPEZi\nHBkzeIf7VuJi0tmEUmwb3bUOW4rvW/Q1SksDPT1Ynh8pS64lLmJ1pOxVKaVsffjnm5sTr8MZwEI3\nXpraMVZpyWNvLy+bfPxlOH3s/Ahl8++98RjL+/C1n7R45X2wD8s7nMfy/Im8bPltKInuIlKzvGzu\nwuQSCulL7WnIFjoqeelmLyn5bLyMa6x3rLMmDlIht6B8uGAjl/lUGTEuZr0E55Lzr29neV5DuHuJ\nOfn1DTht3PQU176ZarAGUJlsM5ExKaWUPZEcXjuK+9l/JC+dbq3C2hM/ZZUW79r7JMuzPoA1qbMe\nkpyY2yD36mzh65MtuZd8k+Aqc+bVjSwv6WHIOVL/griri6+znoMxJjLfhhTPLZaX0lNHHFcHcr/p\npEDhCxPUjaTiAOa2Bp1jkR8pJb/wCdzDqEOOUkqND4Mcr2IPrkFzO5fQjrgH583eA+9pyONyKguy\n53IZiPPm1IP5rMtoUn/EhauQtwVV8fOesAjX2CMJ94deMnBtByQm1HWj4jCXUrglENebsWHI25PP\n8mw8+HpqTs59+qYWHz/G54pFabiGPS3Ypw174kGW11B3UosvvQM5bL3ufqk+ZNDi2Hux/43w5TLA\nnh7MgXY+mMcTF8D9bu1sLtMrIvf5p3vf0uLpr/yd5a2/834tXvAMZMUvP/A+yzM8CvnAYCLLr8jm\n6/G4Z2/T4hdfhzvViz/wtWTvS7yW0gAAIABJREFU85u0eMm7qcrcDLDEWrjv66PstTl/xRxbTsaW\nXp42+NGbtLhgC9wAu5q55GvBEjgCnd+PMeORxjV31s6QQ9HWAN5kXHV38/WO7pvbWjC/OIVwSTiV\nptP9a8nmbJZXth3PDT7j4e4TPmMsy6s4B2lGawn2ufo9hn0Qd+syJ005mEOdQ7hcs7u1Q5+ulFLK\nzptLO9rbIRNuLcCe8kIml2i6OeN7BM6BhO/ge9x5adwKnKe3/4b98G1TJ2ixn06u7+GCfS6VeDYV\n8+fAng4c06VNkIk26aSIns74PN8xYVpce4LvqbzGYFw56FoSUGLuH/aHr5mDoJswX+glOz0m/HvG\n2hlanHaGO53RvQV91re05M8Q7sF4Fnbyw3pS8COXvQ9f/ZQW9/bifrbzgJwq91P+XOQQgnMYMBn7\nf+qOqZRSvn5YW0uu4RoveHUhy6s8jPuZOtv1WfJ7jD67OAfiOxl+4OuThcX1a2OkckYQBEEQBEEQ\nBEEQBKEfkR9nBEEQBEEQBEEQBEEQ+pHrypos7fFycDrvZE4dWdpIN2oqv1BKqWs5m7W4rxuvUfcZ\npZRqKoJEpNO4R4ubdXKqzB0oDfKKwzF5j0PJX8xAXmaaR1wfWkhJ9VP/Xs7yLG3wfX/7+zYtjosN\nYXl+U+Bo8v5T32jxHQ/OYXnjHoArgzEPZe0Ze3lZVVSkmS0MdJTuRqlkd2sXe62ZlGAFE/cYlwhe\nEt1UiOOv2IvybZ8U7hxhqkZJX183SgJ9orxZXi0pg3OOwt+i5aOO/rwEzp6MGSqp6ajnZYQFv6Nc\nsK8LZXj+M3VjmMipIpZDItdW1czyIlemaLFhA8YflbT95zhIKftIZVacSfm8SySX7FSfQCmoIymf\nveuFW1lePXE6o44sgfW8BN+wH6XDcbdCNtSt+75UBueegA715Xshmwmcy53OSn4mbks9GB/uqbzD\nfeZWOGjMXLdai5mTiOKuXXGrIR2wsOCl9F0mjJdmX9KRXVf229PB5y9zU9tM5kqdXCmU6D5p1/i0\nKD5um3JxL0b4oGzSRudst2gq5KFFx1CSGTKQzzcOoShBdo2Gm0DJJsjdJq1NZ++hpaXleRhXySu4\nw9rh9w5pcVwiuulPmTye5fmnoFS+YBtKVaNvH8LywojbSHNFiRbrJQi2WVxGZE5ueekWLS79LYe9\nRt12qOtDxLwxLM/Sksxl9bjfyo9eYXkxM1Bae2kLpAsldfz7xcTiPnXwRzlvTw/mRup+pJRSA1fD\niaGpCscw7nkuacjdA3mc3wiMxfpsLsmh8oFTeZgDFt86iOWNGogxW0ncV8r3cTmMNR3PfBiYBTvi\nJNZwkbtuRYZhHo11HajFnQ1cUlR3FqXt7kn4XmHxXN577lNIo8Y/i31Cl86l7vx2uIiMWgHpjJE4\nnXkN5242v70Fl6yhKSgTLyviZfjUaYpKsH2GczcQN7I2XNsJWYX+Hhs5Eu+jErHmSi4fttI5rpkT\npyishcsXrmav3TXpXi1eM3OmFm9d+w+WR1eUGS/hPbue5e5PVAr8xV8+1+LkcO4SsuOZL7WYzptd\nXTgvJ7O5fOWp+ZA5fbb6Qy2++e4pLG/V+09ocW8v1uNHn+N7WXqt3/0ce/CVUyazvPLTmBOiiIOm\n4Se+R534LHeuMjduQzDmpo7j+226DhWVY0zHDg5jebuIbIXOva9v2cLyXn8ErRZiQ3EvDdDJE+jz\nAN23hC+B7NuYw91nqJyjLhOS6agJ81he0QnIcOtOETez9AiWZ+2M71FJHJBsPfj4OfANZFyjpiVr\ncbnOiSjSKkzdKDpqsNZQx12llKrYhWeG1hbsNxPu5hKd7g7MFeHLsKZd/OgkyztfgHPR9D3+rqmL\n71FNtfi8FOJEmVOAZ4TnP/6Yveeh22/X4psehEvlAJ1jMX3WdbbDWhUSw5+JWspx33cY8d2pO65S\n3O2KOr9SVzyllHKP/++tI8xF3Xmsacd2nWev2VjhnhgUhbYQtbVc4pz6ANau9nrsVdqq+DrrMwgy\n7r4+PPv5p0eyvMxtH2hx2EQ46uV8gHERupjLoK0cIEss24P9iP4Zgj4Tj30Un5359u8sz3MQnlFy\nfsOcFD6K37OZB/Ba6TGDFpfUcul0pE4Oq0cqZwRBEARBEARBEARBEPoR+XFGEARBEARBEARBEASh\nH5EfZwRBEARBEARBEARBEPqR6/acUaQfQ6+uF0POLvTeiJoN3ZhjELdQ8/BDLwHDMfSSaSrgNsQF\nxN51zNOzcIAO1iwvyQI6xMYS6Neyt0Mj67iHW8WGzETfi3+uelqLS/fyfgG/Efu9FW8u1eIKYlOq\nFO8zU0d02O4DuRaw9hy0pB7E2rVn9yWW5zM+TN1ITFXQXTpH834lVDfpGol+E1066ztH0segLQix\nQzC/3tWHcU16TfhsfU+R0n04p3knECfMQn8CaztuJ5f/C/SFroNwrp11PVioNXnQrdAh9nZw+94K\n0u/AejJ0g8Yr3Nb52g7o7qldbO0Jbh8XdDPvr2JOas6S/g46zaRrHHqV1JIeCF46a8i6LHyvrZuP\naPGSNdwOuLMWulj6p7wHcQt4e3v0HKivgqV68tKHcTy1+9h7AqZD30ktYX2TuYWwpT3u+74+9GbJ\n3byT5dEDjJwD2+DCX4+wNHtf9PgIm4G+KB1t/Fp7DeX9HMzN7L/hXJ9+W3eMNtDIpkzGfWCv6710\n/DvcB7ZEA1x3mvcAib8b+ln/UvRnqdFZOAaNwPkoP4vrmPTAYi2+/NlP7D1Up0vnQGp7rpRSvq6Y\nH06eQj+V7uN8PUkIwlwcSOwmM788w/LCxuI+tXGz12J976DCCvTB4aajfx59nwGKA+ljEjEXvUoK\nt3D9csVV9COInYtrrbeUN03EtbK0gd144iCuc6YWrEMeQ38DYwXm1kjSK0EppZydcXzGTqxJbW18\nvaO9jLY/g3GQWVLC8oI80W9n6iz0s3n/qa9Z3qxU7An8JqFfh7WLLcvb8TZ6qSQvVmaH9hCwI/ee\nUkqVbsF4pLMt7TuilFKBM6O1mO5pmgv5/iYwHOtGQz6uqVsc78U21Br9zUq24Jo6kDnA0tqSvWdI\nNMbCAGKLXdPEe7+4XMSYC5+Ha3/8n/tZXtpq3DG0RxazPVfcDtgpHD3R6NytlFLG3BvX/4lavd42\ndgV77Y03H9LixkzcV6XFvO/B/L/Cgnnv3z/R4tTlvB/Gv55An5lXN8Pu+qFZ3Jrb1IneS3O91mjx\njqf+qcVB3vy6XyLH9PAX6Inz1ep/srzlabivjKXogxI8bCLLK2zepcV/XQcL72PfHGd502PQzOnR\nbz7T4ivbvmB5r9zxjha/vZvvF8yBhS3GdNU+btleR8Yg7TNDbeeVUqqxFftcW2uMwTnDeR802lOp\nxYCeLI46++KmAoxbd7LvY3b3Ubw3o40L5sqz7xwj/53vW9pIr0cbT6xjHXW8f+K1XXiWcSH9BNc9\nzPshzUzBvPHZZ+hns2Akb37Y3cJ7spiT9jas/R58OVYew9GHxb0XM6pJ1y+yqwl7Qtq/h15PpZSy\nI/8OSMGerWgn77NlYYVx1dlNbM7JPfrzp/9i76nIwt6BzruFX2WwPJ8J6LlCj48+bynF+wfGe2If\n6qp7Xrz81VktdvfCWKTW1kopVUX6mPhxt2ezYE3GsJcz33uOuGe0FtO+ZR4WvM9ORwOeIcp3opec\nSxy/XxyH47t1d+M+rzr9G8uj/eyyvt+qxXEPoK+iYSvvjzN4+SotbhuIz3YJ5cdQcRjHd+Vj2HFX\n69ZP1xascdQGu/oc33eHBeFZd8dxfJ6Lbv2cfB+fs/VI5YwgCIIgCIIgCIIgCEI/Ij/OCIIgCIIg\nCIIgCIIg9CPXlTW5xqP0snI3L3V280S5E7V59IjnsoCcrbCxo1ZrRbm8FCh2JMqDG3IgFxlgyX8/\nKiOWjaE3QWbh6Y4ysG6dfOXr12EluGAxSv3DZ45meZOJ5WP+J7AYdEnwYnl3PYKy8fozKBXO+oSX\n4DsTide+f0HSNf7ecSyvt5tb6pob92SUWZUROz6llBq0GmWPxjxcR7cYLkMq2giLT+9RxEJzL/88\n79HclvN/aCA2zkopNfgRnIOWUpSAUztqw/YL7D20pNw1EqVpxgJeNm1LpAXGHNiXtZfpytQSUVZ4\n/jNIRSJ11mixK1GG31qGctSeTn7d9Pbw5sRUhRLC2hwufbByQkm+B7nWWd/wMj8fYjG/6g6UwRZ+\nx2V28cRi19ERZYd5O3ipob0vyqpt3FAKefrX17TYbxI/lxRaFtnexL9T/RnIs+y9IVn0HR3K8ko2\nw7aOSplsvexZngMpWbawwDhqzOUSFbcYXm5ubrqIHTmds5RSqrwG90HcWJTUF3xzkeVNfgT2qg5e\nON7Wan4Oa7NQrtnVgpJjaveslFLHXv5Oi8OnYB4u3Ic56+TZq+w9E2anaXFyENaCg59zqVbqKMjV\nwon8NXtLJsv77TzG6iO3wwo09SEuSqL2pvlf4j3+vvw72frwElJzUrofa6HeqrSrEeeZ2nVSyY9S\nSuVVYj70+B3rRMSd3DPawQGWkp1NkEZdvcJL/yc+iBLZ7K8hB3KJw9rVeJGXfHs+gPPc0YhychsH\nLk2j8pWEwTiegYO4hbCxHHPj6YOYU1auXcDyindDJpr/G+YQR1sua5qzdqa6kXgkYq6k5fRKKeUY\nCgmBDbGzrb/E1zG6P/Ej64apgduf0rJsKqei85dSStl4YdzaOGJe90xD2fiGl7g18OTpuBe9hmH9\nTddZbnc2Ym2tv0BkdTMHKg5kByHBWDPCFnFL9MsfnNLi4MkYF4053DI0aukN8EH///ni39jb/ePR\nu9hr4WMxfv7x7v1avGQlt4VuKYYM+qfjkP2MfpLbTr/1K+Ts/77zUS3u1cmM165eosUX/vWDFlPZ\n6j/+dT97D907/PQYJFOLXl/K8gz7D2uxI5GUZ7z3Hctbv2mbFn9xGOfol9VvsbzpCueoPP9XLW7J\n47K8t3bytd/cdJH5xzHKnb3mao81jkrmyoi8QymlUtLwPPDwa7DefffFh1gelSLRPeXvbx5keUMW\nY9/nE4e9YnMRzk3jZb7mRizAuj34LtyX9t58rVdE9kMtf69t4vNBPlkn/Ew4RzEBXEby5YEDWjwu\nAVL+nl6+R3UI0R2HGbG3wzzZbeLPYJZ9WEPaSontcg+/dzJ3Yq9H75e2Dr4mBbhjjGQexhoy/QFu\nPU8luaFESkhlSJ0NfO6nkqnS37BW6cdldyvm9AYiqYubwedT64v4Hj3k2bS7nUvM6PrnRZ6xin7g\neyVmBX0DZE0NZ7E2pC5JY6+V7YDMjj5X7v32KMvzd8P66eGE5zFb3TOS0Qgp17U9kL17pvDxXf07\nZJ/XsvBs0PcTnrn1suiybEgJqeq98jjfO3mlYZ3c/APmgBWvLGF5ZTvx3UOH8+cQiqkav3NE+aOd\nyYR7xrM82irkvyGVM4IgCIIgCIIgCIIgCP2I/DgjCIIgCIIgCIIgCILQj1xX1tRBZD7Rd6fy10g3\n5uINKEfq0HU4piVDTp6Igxq4rMmCOBB0t6FcrHjjFZYXeSscJ+rOobzp+BU4G4xO4mVlw6NRql92\nHpKpvl7uoFFL5DWe/ihh8xkZwvLKSAf13y+hDNFBV5a9/ElYTDiFndBiC53bgl66dSNx17lDVByG\nLKm9DKXYTqG8hM8/HWXLplqUbUUu4yXLpTvhcuGWhLI31wTemZxKmexIB3P38CgtrnfPZ+8xXkUJ\naYcR468ln5fgDrwdZfTUeaToFy7zqTpo0OKYKSiJtXbizh3UdcstgbhuXOIygfxPicziuZuVOXEd\niOvWpXPEMV6GNMfGHWWcHV28bLK9DDKimtO4D9wS+bWxs8N4P/4yHBwSV/PO/0VEDhU4C/dYwDRc\nQydPXv5X14wSVJ/R+DtdLZ0sL2whcStyxGdcfGMTyysgZb9zV8Cto6nMwPJcAzB+m6ow5vUypp6O\nG+dmoBSXFlTU8HEb4IF7zpgHaUDymjtYXmU2Skiz3jukxR5p/iyvvQL387YdmOuWPjaH5bk4Qkrh\nGIhSSxORGOrHEpWXDSCd6y1t+dy2ZQOOb2RejBbH3cwlEl5eKNG3sEQNqq0DlwNVnsZ8G7tyjBbn\nfHaM5bnp5htzMvgRSIjCCrnMhboL2pyGtM7amc8pabb4d8AM3C/OHjEsr+gEJAn0s0fMG8rymvMg\n7UxaBSmEyQRHpbZSvjabTDj2vm64Z3U0c5no5Q2Ql4aPhJTJkbiHKKVUiB9ka+67UA5OJYpKKfXN\nYUgzFo4apcXxD3BXlYPrIM8Ke9f8dk2GTSih9x7J5bj2RBp7bWu2+iNo2XxrJSRuFtZ8axWUDnlo\n4Q84n95j+fyYvRGOIAFJKO1uzMRaU6GT0nkkI88jCPfVpXe5w5pTLLmXSGV8xlbuQjLkFsjdOpux\n1tRlVLA8Z1/IGetOYo20suRzAB1b5mb+dMgen/v3V+y10O8gxXn64we0uIU45Sil1OYPMM6efOR2\nLXZz525N7696TItXf/q8Fq+YtJzl5Z3B+tLdg+9OpRSnfjzN3rP4bTg0fXf471qcdjKZ5UWkT9Ni\nS0vMLy6hXA7zwkjstY+++K4Wm3TykJpTkF7S+/mFr35geT/eDbmIhwffB5iDohOQGoQOC2OvWRCX\nuiriNOhN9mJKKVV0DtKH+2fM0OLPvtrB8tYEQq7QTfYdyUv4nHrtV8xhYQvwTEGPp8Ggc53dAKnf\nACusY31dXF7kHIN70USes9yS+XeyIfsb/wisaSGpfN4II5KdtFW4Pvq2A/T8Jd6kzIpzHFxT9U5a\nZUfJ9Z2GNY5+d6WUsiR7iewyzClpUVEsz5W4zjaewnNCpe77uqdiTxQ0CueMylYNOimZHZFnnT+D\nuT/Ag7vCBsTi+SZ6Ava/tAWBUkr5TcGa2UOkTFTSq5RSvtG4vlb2WD88UniLCY8kvs8zNzWNmB+z\nvuBypZSReE6isq6Ken4fjBiG+8UhGHvKvh5+H1xZj70Ale6WbuHnhroQWhCNUgB5Lm2v5mOJagfd\nI3ANWor4c2AtccK9+41lWkx/h1BKqR7idGZFJMd+wxNY3rF12LOVkfNC3TaV+t/nQo9UzgiCIAiC\nIAiCIAiCIPQj8uOMIAiCIAiCIAiCIAhCPyI/zgiCIAiCIAiCIAiCIPQjA/r6dD6AhOrqXVqc89FJ\n9pr/dGgAz34NnaWNFddaR4yA1ivmJvQ6yNm2leVFzoTFdU8PrM062rnOuf4KNJhFe9H7xUBsZGc8\nNJW95/CH0LWNuBXWYK3FXHvsGAYdIrUTy84vYXmDUvDdvUZA29uq0zJbEds/ar+a8ji3CC3YhH40\nw+59Qpmb0oKftbhdp/F0ILrx5mJo2W3duRVxI+n3Un4BGr3Y+Yksz9aD9FlwgnbTmMvtNZ2I5tPD\nF9bNTU2wMKe9XpRSquAArrd3EPSf4Yv4MVCrUisr/J3Odt5L4dzb6FNBrfACddpSH9KPwI3Yy7eW\n8uvdSewgE2/mVpl/lnNfwwJT30+jrRy9JHyHh+F4mttZXmMWriEd34FzY1kevW6OHtC7XnlnL8uL\nWgk9PO2x0FSE89xaws9RG/l30n3oI9FYze28qQ7UdxR609g68O9edR49qdyI3WVHI//uTn7Qcvf1\nETvDDn4/ZLwDK9Xpr76qzA29jvb+Tuw1Oh95Extc/xhu6UrtBy0s0PNiz3O8H48vsTM8W0B6L+ks\nt6cPQd+oFmLXSe8Jbxdu+5d0U5IW+5D5sLubn8/as+htRP0MN3yyk+Xd+Sz6RFUdRu8A/ynciv3q\nd+jX4RGA7xe9lPdBqLti0OLY8dxi98+SdQB9mCx0/cIar5Bz24ulldorKqXUD8cw99CeaBOIJbZS\nStVfQL+WkJmY546t49a2o55CT4j2avQacgvGvd1cy/X4GWRNT3scNo+VR7jVpDexZy4kfaZcE7xY\nnlM4eiY1ETvlt9/hvU+opemsmaQ/Qhbv31NIxulDX3+tzM2OtWu12H8Q1/G3FeFeDCb9Jjp180pv\nN3Tjganon9Payvul1WdivrWwgvbceLWG5VmSXgOOIejDVHsC91F1XSN7z7hnZmtxRxOO28mTW523\nGbGPyfnkrPojKhvx+aMexLhoK+NzeQfpSUX7IdG1XSml2ioxHgem3/2Hf/f/hoPPPqvFI565j722\n+fFXtDhhAnolbPpuP8u7f/2dWmzjgGNvqeDjsb0avSR8huDzNj/Fe90cuoxeRv7E8vfeF27T4vrz\nvA9TXzfmCmfST+PFZz9heW7ElnbpJFybtLUPsrzcXRu1uJfYFdP9mVJKrXsGc9mzr+LaWLvYsbyO\nesxfcRNWKHOTffBzLT7y3XH2mrMdjsVQg/tF3+di4iD0WwqIxr6F9opQSinPEYFa3Efm6DMb+T0x\n7Xn0DWzMxVhoysX+pqmQ93/qJL3ZXLyxt3bV9fVzDMB6evEL9B8qreN71FHp2GPt2obzEuzF595n\nP4B1+Mv3Y+8Zl8Z7tZRfwbi7+fXXlTnJP/PtH75m54m+drRXoSWZN5TivWponxHXeF1vQGLVPYD0\nqLP343uqg+/CGjnSD2PCzhd9Lp3C+XzV3YZreHIn9hvU2lsppYaRZ0m6LljY8GdganlP58muZt7/\nyZr0Mcn9Cn83ainv62lNPiMgdK4yN3Rd9A7lPf/OnEEvmFhi5+6TGsjy7Lxxfq1ID89uXW/Jq1sw\nFkrI2J++iu+DaJ/WTtLvltpn157iz4t2ZCxEzEJ/QlM7n3tNtdiz0mf4FgNfZ+l+bsc+7J3Sk5JY\nmt909MGp2o+9lH0Q30PXZqOP3H971pDKGUEQBEEQBEEQBEEQhH5EfpwRBEEQBEEQBEEQBEHoR64r\na/rsbpQ5jliQxl6jZWpdzShVolZhSnGLXCciG3IMdGV5LSUoIfKOR/l21ue8fNvCDiVjgdNRDk7L\n3Ip/vMzeEzwfFp9uISiTz3zrV5bnOxllwLTcKmEhLyuzdcd3pyVmlna8nI2WTJbtRZmzx2BujXbx\nS5Q1znvrLWVuaLlhUzYvo3aJQXmk70Bc4+Ijh1ketWh2DkGpbuUxA8tzIFa8zqS82c6Jf+faLFhu\n95gwZpqyUdpm48lLcKltqUsUSn8rD/IyfBsvXB9LW1yTwoN5LK+N2EraEjle/M1cJmXMwjnrI2Xs\neuu2yGWDtTgkdqEyJ4UXvtdivZV21tZM/N1kSBBsyT2qlFL5+2EN6eWDa3PsArerp5bwM1ana3FL\nIS8jtvdD2W5nE+QwfiNwX3a2NrP3ULkRtYd1DOPW7XRM9HYQO1LddyrahjJL1yB8J325rGs8xvml\nH2GlN+zBMSyPyqmSlzykzE3eScgzSnfksteCb4IEpfYUZAxJq25neT09OIdNtbCBdHQPYXmF249o\nsedQyKSubeY2hVF3onS6bB/mqfgF8/HfLx1i73H0x31OrUVrdeX6u7/HMUxdhHNNrYqVUurEV5B2\nznoJdoZHX+ZSrdibYFto54XS2XOfnmB54WmYy1NuN+91pCX4VQcN/PgegP1uw2WUwvd2cjthKvfz\nn4Q16dj6QyyPSiypFXKX0cTyOioxF72/C3Lkv7+ENbzxYhV7D5Xrps3GGGjJ5/f5qQuwEw0h5fRR\n46NZnjUpX1ZEWlpxgM/P7gkoUaeWlBd3ZbK8xPGQjgxZ9BdlboouwS6YrhNKKVW6FeuTpSOxR9et\nSXTsB4yFRWz91VL1RzRcxLgwVnCpELUJdY/C9e4gsrjBa7iteMVllFhXHTBocbduL2ZLjt0pAvMt\n3b8pxdfWc9+d0eKwOF667p4EqWjDJYwtvY2u7/gwLTb3unj0H89rsZUjn/M37YV08MVNkH1UXOH2\nsHSPGjYSVtUmE5/Lsr/ap8V0HxAybyDLe+L217T480PbtPj8e5AohS3k9qs+AVhn9z8LW21HJz7e\nvMcSiS9ZC610690/73lfi+ekYV9X38r3LAv/jf1m0YUftbjqkIHlDV0DK3IbG75Wm4NruZjnKw5w\n+aXfBMzlZbuwhzt6is8XE8ZjDnvuXayz05K5Hbkfkfsm35qKz/v6d5ZHWzSkLUCejRuuid42+dJv\nOCYPIkHbn8mPtakda/jUwYPVH/HLSdzb1KLexZ6Pi+VLpmtx1llImBNGxbA8Ox+smQOnmldiuO/p\np7U4cjHfQ9cch4V3TSEkr6ZOPveMfgIS7rYq7B1zfsxgea6e2HvS5zb9uHWKxFj1SoYMp4vYQJfv\n4RJUnzG4xy5+C6lbWDLfX9mS/Ycij9EWttwyuf4U5hEqwbILdGZ5vqPx+a1laFVA5aNK8fV51OPP\nKnNTcBbPi9XHeEsPKhXqI3LJC4f4M7ePK57vo2ZgHc/fmc3ycspxbkYMxP7XeSCX7TmT9co9FLKh\nvA14Tu1q4HuiwWuWaHHlFdxH9NorpVQesTR3INK1Xt1PI27kGcUxhPyWoZMrFW3AuaD3echQPn76\niJP2f9ujSuWMIAiCIAiCIAiCIAhCPyI/zgiCIAiCIAiCIAiCIPQjVtd7MWEQyq1t3f64nLdsH8ro\nPAbxruSeKSglox2oI24dxPKoxIGWp6b9hZf71NejJLXqpEGLqdQjfDkvEzyxHqVPQ25Fnt615OpX\nKEUeMR2lkJkbL7C8oGg4O0SQssg84lqllFJ+k1Bu5zEE72Hl30opUxcvPzY3HbUoibYlZY1KKVWx\nC9eOlskaL/NzQ9/H3Hx0JV11pyELcYlAefS1A7wTvlsCSqKNOZANUbcKUzmXxBhOoTw+sgMl9R21\nvOyPdmJvOIOyOf8YX5ZXW4DyyojZkL7pJQPBs1Bud+1XlLsHzeIlo9aOtupGQcuWm4u4Q0BgHMZW\nxjGUDZbp3Ax2n4ecZyop9W1q404yNw+HNMNEpFth00azvJzvUeZNu+k3FuCcV5LxpZRSftNQkug/\nCXHRj7zsN5J0qO9qwfWndtHDAAAgAElEQVSgY08ppTyiUf54eD++X4jOzWAYcT+KHIl5rea0rsO7\nN5dNmRt6/HmV3A3EuAHXIWY6xuOlT75jeb4TwrS4PgOfYe/PHdHsiHTIVIPya1MLH999vaivpFKm\n2hLMZ9RJQCmlLn+I12i5p8/oUJY38y7edf9/KNxylf171EqMLeqmlbgsleWV70BZe9SKFLz/8Uks\nr7eby4jMSQfp7n+ukJfg2/2CuWfPAUhCJqXxNcmelDTXX8bal7Z8OMuj6+LPGw9ocVEVlyhNTkQZ\n+ZKxY7W4m5Tw2njxNXxIIKQV1A2htJjP/XOfmKXFV77BPVZwmMtEi2sx/ibNhwuTjYNuvavC+aso\nNmhxeBCXvvZ29aobCXVTvLaZl1uHzB+oT1dKKVW6I4f9O3Y5xp1hF0qn63XrZ9g83M+0tD1qAd8H\nGYjk0JLccx7DcY91dPDPpufJPRnnsL2SSy4Uuc9PbIMrYvpfuBvc0Q8gRUyagOMuOcNL3G2I809D\nMdaa+OUpLO/ke9izhaw3r6wp9TE4B1XmHmOvjSvDMbW3Y++w7nHugPS3dyHZmZGE+WpXJpe59LRh\nXrqcb9DiZc/8g+V1E6mG0QjJeswduC+LfzvH3mM7C/dV6HTsK5557D2Wt27sai129ofMbMAALqWg\nzkV2dlhzfj1yhOXV3IXzd++n67X4Hw/NYnmJ92BduBGypqwvcD4c3fgaXHkY1+7QCchbgj25k0yX\nEXv7oVFwKaIybaWUop0cTMS91NOJS23tiMSh/jT2NJl5Bi0eFMHXu0HpZN4g0s7yw7xNwJxh2GOd\nzsM8evO4ESxvSDieIabMwWs1F7mLbV0u9tAXi3C+4oZwxzanEO5MZE4sLPD/+u08+HOGBZGNBg2D\nvKPuIt8D1RFXO1sPjAOfBL42uMTg2tedwR6upZbPea3k3/6j8cxg4wjZTdhC7kx2bTvWgkHzsQ/V\nSwepVLnsN1zDfN2+bvhNmA/brkGuVJHJZZOdRL7kkYrnZq/rOCHdCLI3oqUHbf2glFKDiOuYYQ++\n87gVY1kelXs3XMD58NM9gzkSJzY7f3wve91zajuRDzblYk6l93xJGV8XLT4mjnVkjexp5c/bCYtx\njQcQSS6V4inFne7ovsqYw/fdoWTvQH832aVz2KTSrxTeueA/x/+//5MgCIIgCIIgCIIgCILw/wr5\ncUYQBEEQBEEQBEEQBKEfkR9nBEEQBEEQBEEQBEEQ+pHr9pxpJ3pMva6K2mKX1sH+2N8rjOWd+QC6\n3VFroc+2suH6zrIK2Mq6BkGTWFG4i+XVZ0CTGDkD9nFdXdAXt9RyrdiYx/B3j7y+X4stLfhvU6Nu\nQn+DK/uh/Y4aEsbyuurRs8HRETpG52iuwT/3JfoyjH8Gx3r6XwdY3tQXuDWmuWki184tkfcEoj/P\nNV5BH4PQBVxzb2UP/e21HdBkeqT4s7zuJmjx7ByIvrCvmOX1dkG/3VqIHirdRA+ot+SMGIVeIdTC\n0CGE973xJJZ51HrdKYRrpd2HQMfqRGyYM9ZzrTn9Wy7EzratgvfEcY30VjeKpjzcY95pQey1kl/Q\nv2P8/eO1OH8D7+PyySbYVR4nVowPzeL6ct906JQdiF12TTa33LYivZOofWDNEfQm8JkYxt5j4wL9\nd90FaG6dIrgW2lSHuefEB+hZkDCVj0u3RIyxyY7o1+Eaz8d5DbGmLs4gFsL38T461cd5XwVzM4Do\n0MfcyvuL+KSgt1FNBubDqNu5Dj3/O/S26GnD/WLMrWN5rtHo+WRNzns06eejlFLObugrQftZMNtH\nF64Btrcl194G99jFz3nfrTFP36TF59/Yo8Upj01lec3lmHuKd6H/QkUG7wnk6oZ1o6cTc8jxtw6x\nvAZiGXvPp/OUOekklo0Ln5vLXsv/6qIWUxvsmireJ8rJCH25NbFs1Vtu0+t22324T1sK+edVF6Hn\nALWApBp3r+F83qDzF/28hNm8D4q1M47Bhti5hk2OYnmJ7phTaK+ckjLeHyfYD/PkoPnoxVNzlN97\nvmN4PwdzY+MCvXttdSN7zeEs5ovmLNxX7kP5elewGX1OekwYj8EzuM24rTvv9/M/XP3xIvu3M5mX\n7Yltqb0v5uGLb+5k74m5A/3DSo5hLfBLj2B5hT9j/k4ZCXvTkx/z9S46GuPkwgG8Jz4hjOXRudye\n9PXo7eG9gkKi+DkzJ+fegK192CI+btMeQh+EJ255Tov/+toqlvfV8+hNcN907NMa6k6yvGGPr9Hi\nvlff1uK5k3i/K7qvfGMFLLzHxGOevVbL99P2/rjWVqRH0wsvcLvjiFHo/fL5fY9ocWI87y1SSvrN\nzVj3qBZnFHJb+5gA7JXq6rDODo2MZHm2trznh7kJI3397P24xXA5sc9On44188vveA+HUH+s+ePI\nuXYJ5nuLsPnotbXxiZ+0ONqPf8eo+RhPtHeEw3l8np2uN8buLw5p8d4M9MdJ1Z3PwGDMgZ7O+L67\nTvBeRHQsff/1bi3u6uHrxDDSY2fVYxgjGVu5BXXmWZzLFR+bt/+TC7E7pvscpZTyGopx1lqONSlM\n19ur9jTmXdqfr+wS3wdYkn29G+lzSq2zlVLKIxFzj50d5jUbG9I75co29h5rV/xdpyD0BbF25L1p\nSnfjOSh0IcaU/RE+Jmh/EtojxTOQHyvdT3c3I6+no5vlddTzHpHmptpo1GJbK/4TAd3rxSxEn7us\nDXycRc/GdS0lVuWXrvIefWnjcY8d3I2+pKPq+LhwCMUzHutfNBv9uQbs4s+LtM+MLem35zGN34v1\npCeOO3k+jliYxvOy8AxrR2zUW4r53uHsF1g3aD/ZybrebuW7uYW7HqmcEQRBEARBEARBEARB6Efk\nxxlBEARBEARBEARBEIR+5LqyppomlJ952HMLrDpiUTwwBWVCljb8IxPmJmkxlSSFjedl7TH3oVTr\n9KsoMxu6lksu7HxRim1hgTKwzk6UiTp5hbD3FG5D6XHyQtiaucVy6UP1KZRVh0WhDC/yFi59aChC\naVZHB8rJHUNcWZ6dNazX+vpQhjj66QUsb/ffv9Xixe+MUeam24iyOrdYLr1puIiScxtiXdfTyUuT\nG66i3NCF2CZTS3WllAomcqg2I8ZIRx23u6bl+9S+zGt0sBbXn+FWc7SczSkcJYEtOmvpOjLOHAJQ\nDtfbxUtBjdkYM/Xn8Z6AkXz8+I5EyTC1b3cO52WJpXshRfG5fYYyJ1SqpS8ZLczFtQkk5fR+w7iM\nYcv3/9birT/B2rGa3OdKKeVbgrLG3M2XtTh4OJcZmMpwL1J5zZbDJ7T4pg5eGngqC+do+m3jtNiY\nwW3wHEg5aQkpAQ88zyUSDT24blYumA+onEMppUJuwbjMP4/SbiudRXQRkTwNVebn0HpIGkev5Pf6\nT2u/0OJFb9yjxc0VOrnHxDAttiT3hF4GeOUTWDm7+uN8tpdzu0lDFa6xhSU+I/JOyJ8asvn16SSy\nxCELZmqxzygupSv4ETKnuDsx95qM3Oa9lFgZO8XgvoqcGcfyzm5E6WveKyh1Hr6AX61unV2iObEl\nduudTdxqklp8jp+LY689Wcry6JzXYMC5CIzgc0qnERKq5jzkUTtqpZSKXwh5UEcj5lrDfpTOug7i\n613AGNwTV8i49B0fxvKobbqTK7576UFeohx/J2TBFnZY+1Jv5dfG1hOfQW3cmxr5d6o6atDiQH5I\nZqHgG0iK3F25zLqtFHNbfTPul6ghASxv/zrIrkfdjfu5u42PPxsiXRh4xxwtDm7MZXlWRC5YdYLM\nU8T2u7eXr822ROpHFG3/S3YblA7pg6ka55rKzJRSykQsXac8lq7FVEqglFLXtuGedQjC98v5jku1\nAkfdOHlacQXmpWgnbpmsyDL5xtY3tdjRkcvxlj6JxMfvQ57bem7pnL5umha7xJH73JjA8tydcT0S\n1uA9793zhhbf+tBs9h4HImEr31OA/66TbPf2Yr4ZnITvMUA39w9PhaynrQ2fN27uMJbnMxLXxskJ\nEgFqcauUUj09dP9mfjvmTiL3cPDnsiZrN3IsA3Ct7lg8neXR/Xfdccy3LWV8L1BBrLmplImukUop\n1U7uHyqRMJzC+xOXcNt4J3Lenll6qxZfzeOyfnovHc1CC4VZ6VzC3N1M9u5EcuGos8T+9XUidST7\nwyoiUVFKKQ+dXbg58RuPfXL2B6fZa1S2FzQD46ytis9RfhPwGdXHcM58w/kc5TGYSNDInNdUwPcV\nlpa4Hp2dkKcW7NynxWFTR7H3tIZiv0UlSS5eulYPzlQiiIMImMbnl+52rAU9JjyD6CVxjRnY23bW\n4HmYyp2UUqqr0aRuJDEReG7Qz/mdZG9R/BvOE5VSK6VUNbHSXrFunRZvfOcVlmcke5pkYhtfWcEl\n+v42mN+8R+P5jB6fvsVG+X7sT+i9093SyfKofLjFAIlSs24suQ3EZ9B9d1NWDcu7Woq5Z8YkzLfN\nuudUYw0f+3qkckYQBEEQBEEQBEEQBKEfkR9nBEEQBEEQBEEQBEEQ+pHrypoCA1FKpnc/KT6FkjOL\nSpTRUccCpZT69QBKom9bg1LO6gLeCd8zDJ2fw2dDxtCQb2B5tOwor3q7FsfMgmtGc/Nl9h6f0Sjd\npB3ATbW8vJ8SvgjHU3s1h70WmAw5hpUVyk6Nlrz78ri/LdPizHe24Ds0827bbg68fNbc+E5BuZiF\nNZchqR6UozkSCVB7DT83VP7hHouSQh8fXlpaUQpJWifpOO4/mTtHWJKy96BZKHPM/BjlkI62vEzZ\nKwUl5bSDeVtpky4vUIurfsc4DZ6czPJyv0f5dTcpFXep5OX1VE5g44Gu355DeBmdtTMvPzQnXeRc\nmmr58Y1ePUGL26tx3ex8eAkrdd8ZGYNz7jOYf49rZ1GuGHcL7oMG0tVcKaWcoiDBaLqKMkTqTFBY\nxt9DObP9ghaP0jkSFfyMeziNfJ7v5DCWV/IbZAF2pGS3Io/Ln1qJxGfCM9PUHzF0xYg/fM0ceLng\nHnPQXZ/Jd0/Q4qoLcEnJ25nN8nyCUVIfthDd7ou3XGV5w59apMXlZyAH0ku5trwDaca0hZBm1BI3\nrdCJY9l7fIdAsmM4AElM1NSbWZ7LUly7egO+By0TV0qpxIfwvt/X/ajFIY7chYS62bR3ojzVW+eo\nd+AfWBuSuKHSn8YrFfOLMY+7rly7CKfAzlrM8366+W/Hmzjnaak4lw0X+f1y9TwkCSkzIV3yGaOT\n7m7A/eJHXI66ujFP0hJtpZQq2IC1eeAaXN/Nf93A8sYsgkOKqZK45IVxGUDBt3BsCCfOObbufH07\nvx4y407iZjBwXhLLc9C5tpgbzzRcR70EiMpuEyeGabExn1/v4csxX3Q0oOS7Rydr8vBA6Xx7O+R4\nVro1zlSP46D3qTEbpdORtyay9xh+uaTFUXdhjaNueEop1VmH8dhWDLlDWTUvIQ/0wfxSew7H6hSm\nczskZeS2rpAPOOmkeXTtMjcj7sJ5ba3g+4Bjn8B9iEr071zPHZD++cSnWnxzGmS4nx3grpq+G7H/\nCJqCa1B5jjvJRK2E1OXoy3BIpHOXXxKXw/y89l0tTpmMeydiEl+rqKypqgRjMSCer+Guydij5X19\nXIs37jnK8v4ScYcWn379Yy1e/O+/sbyaEtyzIbHmdflRSqncfVgbAgr4s4ZjKOaZQ79gzjpyla93\nz7+I69rbSxzrOvj4azoBuUPSUkguL317luWdPYi/NT0F91VQPJGYW/L/v502FXNYbzfWuB8+O8Ly\nhs/G5yWFYC53jvJgeZ1kTmm9hnu2pYg7xDS1kzxyb8+4hzuJddTz9gLmpJnIcyOW8rm8k0hxir7D\nfBV+O88r3Yn9XOA0SPSbi7kkpPEq5IwOgdhTOQbzNamtFu/r68ac7k0k/319/Jm19gxkKcGTMR9Y\nWPC52obI7YLiIe2uLNrL8op/wjh1Jg6a+jYL1kSWbx+Itc/el+8T9W0czE12Pvb/seHB7DXaFuPy\nbuxRIxP5fsRtENqgbB24Xotfe/M7lrf2oSVaXHMJe5+kxXx+rDpk0GL6G8PPL2zV4vRlfI/aasKY\nCxk7QYuzvtvK8nzHhWlx8Y9kHzWV79m6yP4p93vsdaKXDGZ5NxPZuzEHa2t5AX8mofPIf0MqZwRB\nEARBEARBEARBEPoR+XFGEARBEARBEARBEAShH5EfZwRBEARBEARBEARBEPqR6/ac8R4LHZm+z0UN\nsWib/RKsIfM/P8/ylq2dp8XUdnnP2/tYno3VIS32d4NNXNA4rvsKmIQeBnkfQyNakwj73qY8rqGm\nGkyPVGhzPWOiWR615bKwhL7QZxDXRXZ341xYWkJf5ujnyfKufvEr8hzw2UNWcAtdvZ7S3FQfRt+V\n7jbedyBsMfTN1HosZj63Ojfsh/WyqQG6+GtNP7M8F2+iEy1BTxFTFe9hQ/s2UNvu4hpo6/3duXa9\n4Ev0iHEbAk1jva7vg/8kjBlrV1zH4p1cU+weDv0n7UtUc5xbF7vEeWkx1QAb8/k462q6cdr69hqM\nOWtHa/ZapxHHVLkH1zBqFddtNubg3MYtg+b5xIfHWJ6NFcbqpn9jDPu6cj3vKNKLwmdSGPIs0CeE\n6qSVUirrKPo3HcuGznyC2wSWFzYLfTiac3F9L27k80vshFgcN7nW+p4zkaQ3Sy/pV1RJ7HqV0tkW\ncimpWbAkVqDGAj5+Lm2BjnXgZHz/qKmxLM+G9Hc49MoeLQ4N92N5tbnQz9J+UmW7eG8sqnm3J/aO\nxqsYL1c+3cbeQz17fSfietcUn2Bph9cf1OLYpDAcTwgfS5e2k55cRCusv6fi56HXg0cc7tnKU9yS\nOO3O4epGcW07xnBh1rU/zHMmttpWDtZ/mHfxYp4Wz31pHnuNWuR6kt5QR17fz/IqG9GDINQLNrql\ndRhjpZv4eFv8xkotbirB95jxBO8j5hWMviqFR37TYn1PmGvn8Bmt5aT/BxnzSinl5g4NffhS3GT6\n/j2030lwjDI7zWT+ttXZmip7zIHGXOR5JPqyNBPpK9ROLHupraxSSl38+iPyWpgWV+vWGo9kokMn\nlri015n+ngifj7ncwgJ51i78eruRvoFN2Xht1JpxLM9AdPftZVjre9p5b4aiDBx7wizMr2U6i/WY\npUPUjSIsGfdLdRnv9TD1b+gDYWOHtX7n375neY89t1yLN70PS+LH71rA8p595TMt/iAFPVlidD0H\n3rznQy1++H30QflpyWta/PuUlew9kb4YV7Mn4jvlbNnO8mhfu8kv3KvFW574N8ubNgfnPGgI+o6k\n/eVhlnfn2ClavHzCBC02HN/F8kJG8N4l5ibtQeyJaX8lpZRqKcT+mPbncrDhvdP6SJ+Z+NXoRdRt\n4tbDZXuw/tH+HYPvSGN5De9jz2Uy4Z4Ln8yfByhO5HnlwpvoM/P+p39leaW7MOdHT8F3qjvF+xc5\nhuNZyCkc++G+Hm5dnHIN8w3tLZi5idvax6bHqxsFPSZ9b5uKnTjnniPR76XqmIHlsXmuFef8wNe8\nV9Lomala7B2LsV5+5hTLs3HH59EeL51kDqW2yEopFTYV82FNbqYWu4Xz7xSYOlqLcw+gT5u+r6dH\nCvZlraRXUGA6t9zOyzynxSFzYdttquPP3vb+N7YX25Cx+Nuu8dzCvOhb9Asadif2BU353Hb63A/o\n8VjegHvsxXdWs7xG0mcm8cGRWnzijYMsL3IsztX5DXiOGxqP/+6ZxPtudRlxja9+tVmLXeK9WN6O\n17CnSU3GXvv3b/heNv0JPBM7++IaHHyX9yabtAZzpZUznkmcqvl1zLqAdZLPPP9BKmcEQRAEQRAE\nQRAEQRD6EflxRhAEQRAEQRAEQRAEoR+5rqzJKw4lPnuf56WgQZ4o2S7ZmqXFflMjWZ41sYM88+Hv\nWpw8jJfq97TCejJkHsqqLn7AS4v8hqMsL57Yf3a1o2SovZJLaFzicKz23ihf7uripVjUTrPoG5Rv\nDX6UWwdmfQupB7VGcwpxY3nhi1H+aGWFv3v0ZS4RGPsML2U3N6ELcT67daXJDVdgSec7GvKG1qYC\nltfdhvdV7EM5lqUdL+GzTMe/m4m8zJpIMZTiZe82rig9TH8AJWHU9lwppYxElkMt82KXc/kOtRBt\nr8BYoHInpZQq3gArOGMWzoO+bJxKC1xiUObXcKmC5fmM4nZy5sSCHIPevtExEDKBmPtRIFd9kpfM\n25GxX0cse1MWpbK8CiKNivQjlpwV/Pt2E7vYkj2QlUTPSdDizMNZ7D221pB3pCfh/rCw4uOobDfK\nYG2I7frw+7gksIxYaRubINmLnMR1ENS6mcqJbEkZrVL/227Y3Dg64O+ZdGWOsaNRokmvcWsJl4b1\nBfKS5v+htLia/dt/Kj6PlnynPMitZPP3/aLFWZtRxuvhgtLNynouvYyfijmFyluqT3GZD7VKpqW6\nXc281LyJSEcmrp6hxaf/xUtGg4g01jmcSi64dbG+tNiceA6DJJPKZJVSyp7Yo+cSiW/NyVKW50ek\nu4GBmFMOvMzlBOMenazFhcSCNH4cH9+V205rMS3VHz0D93becS5nqzoHWaFrFEp9e7u5xeflb37Q\n4s46lHbXtfPvlHQn5p56Mgdb66zbrT2wFtSQOerc3kyWRy2FbwS+RHpUf5HPbbQEvi4TEklqwaqU\nUi3FKFMvOFukxVdO5LG80Xdj3qJznaPOnrqbjOP6MziHvhPCtPj4l8fpW1SwJ9ZqOzKfMYmm4pas\n1MKWfgellOomss+AmZApX9uSzfIGzsQ8X3UE0mlHdy4Ro7a3oQnKrHz9wCNaPOO52ey1S+/gPBXX\nQjI3au5Qlhc2EjK+nnext/Mewa/1j8sgXcj6DnKj47/zcbvisVu0uJBIsdc+ehvi5z5g70mOwN5k\n/4s4hvlvvsTy1t/5gBbfOQp74RF3jGR5+d9A3hEwDXPrhpe2sLzHH8ExNV/FObK04zLM6gLML+FJ\nfB9lDgw/4Bw2Gvn+3S8Gki+v4bgm3qP5fquHjNuinyARdh/C5b5hc7BfrLti0OLTn/FnjYkPTsQx\nkTFcdx7Soz7dUkxl9MGkBQOVMf3nfXhjSwGeQ2zc+T7ZikjY6V6lnuzflFKqsxvf/fJxyG5TZ3NJ\n4fXktX+WLiPWdNdoLh2x9UH7BxOR6FvZ80dQDyLdtXXDfDU4JkKXh2tam491Ub9XaivHHsGDjAPf\nKMjeSk5xiXCLI+bDZnJtXEL5d8rdDDlM2Cysfb+/wu+xoCEYs1Tq3FHfxvL6yB7VRF7r6+lleY0X\niWSfd58wC7aeuFadjVzKVduM8xlGJDs+w7nlNn0+SyHHf20rX0N2X8T8eFcUzk1IEv+8jL2Q2oZ4\n4ToEzMI+6Me1G9l76sixtnfgmW5YHm9n4uGEceaejDESY+LPylTO3tuB/cHIW4exvPwfyD7tHoyL\n0t+49N7HxUVdD6mcEQRBEARBEARBEARB6EfkxxlBEARBEARBEARBEIR+5LqypsqzKDUc9+QU9pqd\nA0oNqy9DulC8mcsYHEln6YQ5KKX1SAhgeYbNKAVy9kSp0sTnueSirw8lUoYjcCrxGYqyt656XjLf\nbovyJt9k1NWWn7jE8nzS4P5RtAPlV8WHeKdwO+LsUHcSJY76Ur6GKyg9dE9AuZSjHS9drM+GjMRr\nDHdOMDe27lzGYU1Knw3f43pHreBSIVpa50Q6yDdmcFecaztQ+uWehDFCS6qVUqrqGMrZ/cbhvJdu\nQ2m4RxofI1SSRWULHQ28PNBrKMoIqRyISjuUUsp3chg+ow6fEXLLQJZHS/u6WlAeZ6WTXdk4Oagb\nRSMpraelkUpxeZGRlN/mni9iecNXoJSzNQ/lmrZefExE3QX3DxciUwlv5GXeLrQMkfz3HCKNiQjk\nJcXB8+BMQGWEJT9dZXnGViJTbIDUqGA9lx8EEEevpnZcJ38P3gm/7gzuU4dglBM6BPLSw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oi88InSTmqDfPnSNvQ0bUIqy4R4aMpbxuIfXziMVn+yRxGww5Sjqb8Hlnv+S2CIF9ROuL\n33FOXi4s7b64CutsdMotytIoEXbk7n35upcegAzQoy+e1Y5+dZDy+k9DLb60D8/Zw+/lfX5jNva5\n4WJeukXwuGhrhvwy+haswX4RsJfv6Kik9xQdxZ6o8gD2YlJ2pJRSY5fgM359+jsc6yyeO6e3QrLu\n7y6s2K24Csz75+1GnL0Gx9DTzpLC6CX8+WZo5oyGhoaGhoaGhoaGhoaGhobGNYT+ckZDQ0NDQ0ND\nQ0NDQ0NDQ0PjGuIvZU1nPgCtL/mJafRa9ipB1xGSF7PbRFgy6NdWtpAQ9H1wGOX98NjXRjznKdBq\nfYPY/en7TXB/8XEDffvmR0Fx7GXNNKOST0Gdku4z/W/izuiXfgZdzKM/KGaluRWUN+Rh0KCsrEGL\nLLtURnkBqaBPlR8APdVmEtNlXXtfPaqhUkplfAu6akltLb0WPwjOHBkHIIuImxBHeZm7IdmKaUF3\n86Y2vt/pm0Fn9A/Aee0+cY7y8ipwTZ//GBRU6Ryz8b0t9J45z8404p5u0APdYpm+vWEZHKmmdAvn\noUqWIUm3l/YaODikLGC6mezyPtgRdD07D6brZ6zAdQ5/07ISp+ipuB+e/QPota1//9mIU5+APDD3\nd5avBE+GXKH/XSlGnLeKXTh6jwIN+kzGMiM2u6nUXxTz4gpojXZuuC7Wjlxiis9j/uYL6mbCo+Mo\nT9IGrW3xd63s+fPkvfEYgDkrneaUUipwBs7dXty3dne+h+01LepqIvswOtfbmsaPlIl09aCmRl3P\ndH0pQ5JOSR3dLIkJE45P7bUY35OemswHJdRBUg7m1gfzqpfpa3wpZWrKQU2x93KkvA4xr1qKQC93\nCmCqbq0YS531qCnpa9k1pP8tomYLWVPYAnbdqjrKLhWWhHSoCx41kF7r6UFttBZjtfwYd/SX8B8X\nYcT16SwV+vH11UYc6Qcascd+ltB6u0ISNOAe1J6a4rNG3FLK8kUvIeOV89TVlcdb+ZENRlwhnMMi\nZjC1vrMZUrI+t0Ie5+DFciX5m5CUVlWdZrpx3RlIo/xfnKksjfBFkBq0tubRa33G34jjqoAEwd6e\nHTvqyiEPLduF/UPCg+zX1yHGdOHvoHlve+Ezypv3/rtGnJv5KOIteE/GxlfoPUOWgiqesRwS3/CZ\nAyivIgdyB9dAyCFlbVBKqUur1uDvngUdfOgs3i+tW4F1NjoAa5KzA9e141nYVyQufFBZEiEzMQar\nz7GksvIoxpOjkF1JqaBSStUdw5iOFhKntjKWSPhEYEz3zANFXcrGlWJ5W/ZOSMbaq3CdW4N4LtZW\nY6xnbUKtmPL645SXuweSfSmtCpvOc7FLyEb3CpnGwHlcrzziIVuQKt6Mr05QnkuM2KOyIZ9F4CP2\nNLt+Y7nH+AV4VpA7+4xlvL/xCcd6VZ2NvYVHBDswnvkZrRbG/x17nfJjXFNd+0JG9MP7vxvx3OmY\nbyHTWNJWdxnSivydGPcxjrzHl+5Ncs0YfCc/F3V3YJz9+Bkk62P7skxq18twtZ34EmplremZRDoC\nBb87T1kSZz7GZ4ePYk34hdWojYFRWMd6maSrjsKF9dgm1LJem7h1gXTJCpwOiVKrac6GTULNKjmE\nvYRTINakNtNzwchn4e6VsRwyutYKztv2b6wLsv1G4mReP7tbcX+lK5t0o1JKqZw0tF1Iuh7PIPY+\nXF+ke9nVQOA4PBO2lLIksKcLe8xL3+N5x9PUfsQnGbVpfDIknL1MG8nsn3FPkp+Ee2J7K7fLkI5/\n/eZCCnzk7feMuLmB9+6+oqZsOY5jHd+f788v72NepQ6HZOq4aczZCZlrgDinrhZ2+9r4PNzcUh8Z\nb8SF4nlHKaXsxZqk2HxTKaWZMxoaGhoaGhoaGhoaGhoaGhrXFPrLGQ0NDQ0NDQ0NDQ0NDQ0NDY1r\nCP3ljIaGhoaGhoaGhoaGhoaGhsY1RK8rsmmDCRkHvjNiV5PlWVsV9HdNwvr0wna2UAvyRM8Ye3/o\n0jpruVeJjbDUDJuL/gHZ37Luy7m3hxHbin4Rv3233YjnL55A73HwR3+DLV9CJz0oLpryqqpwHu3C\nmtusDUwvhpZ58nXQn4aOZTFudRY0rF69YWNaeYEtvA9+DyvtO7/6Slkae16E1aZnEvcrubwHx2Iv\nNHWDlo6khJgqZwAAIABJREFUvK+e/N6IXYSmfPY9kyivch+0gfEPwHquvZGtGNtEb48D/95vxInj\n+6k/Q62w7/UfCx1xzXHWmm8+Ar10lD96BAR6cv+iC4XQeNoIe+pgLx7r41+EBrWtFv2LzDbRpbvR\nTyRlyZN/chb/Nxx673UjdunN55G5B+PMzRH61PglKZSXvRy639B56GFjthJ094busqcHvX0urVxP\nedLiuUv0myg7JCyE3Vkv2/ce9K6qK4Atn6Mf9yCpOQ+tdOnePCOOvZP7AVk7oFeGtFZuNllpu/eG\nzjn9U8y3mHt4zsq+FwNm3a8sjQub/4V46wV6LToFOu2MYxhL1lY8zmR/Ea8BOC9Z55TiXjDO4ejh\nY+vGPSFkf55g0ZtH9vepu8h9t6Tlc+Up9GzwMPXPas6HZjlsIep63UW2PSw8gTETOQa1svp4CeU1\nC2vL+BvRP6FkC9uIdwmdd+qrrypLYv8rLxux3/gIek32O3MRFsyle3MpT14/O3esLye3c4+dcQ9y\nL6b/IP8XXmfj7h9ixNnfY55XV2IeJN8/gt4jbT2dAqD1l30OlFLKyRXWtiXH0a9B2ocqpZS1PdaP\nugzc3+421mTLuV6xH73YvIewvfAFYRk65733lKXR0oK/3dTE17O9DvubL5/C2vf31d9Q3rG3PjHi\nhmasaZmlvCYNjse88h2F6xmUxD0mLq1Efx9H0RchYlyqEV+5wr2lOjuxtuauQ78Oez/uA9B7wgwj\nri5Gv45d7+2gvNRHsH9a8eIqI7az4X5fss+C7DkQHsv38cRx9FB54LvvlCVRkr/OiM3r8amP0WMn\nchKuv2c89w1qr0MvGDlWbV1535f/M+q193CcY3s19zoIn4K5mP4N9puBk7HfdA3ifZi1NdbJ3I1Y\nn9xifSjP3gtr9RXR67HiIFtuuwjLZDthZf/+kzx+k6PQX8JP9Hkb+jT3mNz6EizeF3/OtvOWQEXF\nH0ZcdYbPxS8Z964uG3s2n1juu7LmaaytZXWYE9Pm8V42cCzO+dLnsE5fdegQ5T3y3E1GfN0NfzPi\npFj095k3ZAi9J3ocetCEjUW9/ff9/6S8xR/cbMRXRA+k6vNcN2pPYh/Ufynmb00uP0Mc/RrHnl2O\nffJ9XzxAeRWnsOfqO/luZUmcWY1aKPusKKWUz2DMl8Zc7EuOrT9FeXKvYyXi3lFsTy3XivIdWFtd\nTP0n+y3EPSxKwxiztsN+37y36WrCXjZwPPYi9VncD06uf0c+QG+ahBtMvUx/Qd83Rzs858YsTqK8\nxuxqI/ZNQROS7B/OUp7vSKwfMcNuVZbG90uXGvHUV+bTa7JOXfo3+kf2tPGatPMseoxOHoHrEXPb\nUNNfw30o2YP6mnMoh7Li56B/WvSo64w4++AvRtxtsqo+ux7XbdzzU4x4x6vcy1Q+z/a/Hveko4G/\no0jbgL2ZXAs7uvjvRiWil2lnPfZYsbePp7y8TVirkxc/pszQzBkNDQ0NDQ0NDQ0NDQ0NDQ2Nawj9\n5YyGhoaGhoaGhoaGhoaGhobGNcRfWmk35YEaWHWUbS5D50AW4RwCSrS0eVVKqaBZoPk5CEqm2aJL\nWhkrYQ9r68UU/NCpkFxUp+UZcXwIaGCSEqaUUl1toB0teAl2XZKGrZRSVitBxQqcCgpqj4nmPdAT\nVOQWYd2W/u12ysvLAUVx4HzQm2yd2ZJ41us3qKuJ0DmgYZb+wXQxKWUK7gfqoJSVKKXU8nWgDy/7\nGyieUsaklFJ2vqC9nX4PNMJGk+W2lRgnE56Gte/hj0AP3HTyJL3nxY8gM5FSuqJSlkj4e0D6FiIk\nStvPsZ33/FTQTrcfhXxuWBLb8tak4xyPrgQNNn5Qb8rzGfJf/NAshOBpoPa2VrBd4OjnYD2/9lnQ\n0BNdWeYSOhfjwCkAlHl3D5b2dHWJa3sM9oihM9leXUqH3IXEySUc119a1yulVNE+0OnrzoBO6jWE\naauukZBu2dlhjO585w/KG3o9aMV150Hn9R0ZRnmFW0Gtl7bBbSbrbLeoq2tr31aFvxfobZKKlkNK\nESmkARGL2BJXylH2CElCn8QIynMR17BqP+jgbv2YKh+2EPRwKVd18RT2sx1MW1VCDVtYDTqupFQr\npdSoBeL+CCmT37BQyvPsB3mWvSdqSPHBPMpLvBe02Awhee3oZBp19Hy2S7QkbD0hd/COZ5vW1lpc\nCztn3N/AVP4MScE98NEeI65r4fFYcwZ1+NIRUNJHC8moUkpVnoR1uPdQjJ1+A2Grmrma5StRC2D3\nLOnKrV281ltZsbzjP7Bx4P/vaMSxe8Vh/pXsZxtxazvUBDm2ay/x2Imby+Pe0ujsxB6kIZcp69L6\n9dkf3zHiwnNMiQ6chjXAOROSV/9YP8rzG8Xj5D/IWLuV/u0aA1q+nAetrZB6ZH57nN4j73f0AlCn\nP7/nLcqbKWQwzbmo8bKGKqWUvQf2XPPvwtocPJzXicpLaUYsZYSefVk2dOYUWxRbEvlrIUdz78cy\nO7nHkPvSkl1sRes7FOt2Rx3mpVnWZOOKdahYSJj7P8DStOw1WDMHPwzr8Nyj2ENV17HM0cYJnz3g\nhruMuDBtI+UFhON+1NRgr2TryfLhoEGokxeW4e/ePJOp9SdPQx7TexrW945mlgUnzUhUVxOrn/rR\niCsa+NkgORJy374LcRxlp1nuMeExSOzXvgEJdv1Fntsnd2HcLngb0h6v5EDKqz6COrh01iwjnv33\n2UZ87ku2/Y4Yh+vbUI2xOX4Gyzm+XAp52ZMrINls8mD5vxJS7a4uYb9u6kaROAfXZZi/q3jF1LWC\nH88sig7xDHelm/9u5VGsT9J+PKmxnfL2bkZLAmkVLyWBSil1bjlqYFAsJILuJllTZyckVPbC8t7J\nEzWqMa+W3mMvZP4t5bjmNUdZYl24E3Vk6MNjjPjgB7spb+j9o43Yzg01pXgr18X2Klw/V7EP7arn\na9Tdys+jlsb0V2804i0v/kSv+brhWX/wU9hbXPyU9xYPfvWQEWd8g3rYbprbdZewJ7QSLQqCevMa\ncnkDJE89HTgmL2GXbWvvQe+5Ucj2Tq74wIhTbuT1rrUEx3RRSNASbue2ELKdQJ87INU699VRypNS\n/rgHMO/LTqRRXlspW7OboZkzGhoaGhoaGhoaGhoaGhoaGtcQ+ssZDQ0NDQ0NDQ0NDQ0NDQ0NjWuI\nv5Q12fuC3mXrylKcmjOQ7DRm1Kg/Q3Mh6JFl20ADi76TO1pnHwSNOGA4KGwR1zG1OVvISsoLQFcc\n8/RE/P8hU7f3oaDQ+wh++eWtTNnyHg56qzw/r4HcWT97OahPTiGgOjVXMU1pyO2gu5I7iYmSeOpH\nUPSu+5hpp5ZA2XZQcI+kpdNrtqLrdFw0pAB2HkyTXXr99UZc1Qiq3/hHuRN+xRFIgBzE+Gkydd9O\nuBUU6dJdeG3k43AnCfyRKYppK9HZvfc4yHwGzueu51UHIeGIvCXBiGvf5fvjFofPjy0EhbKiuJry\nAt1BXXd3wjn5m6jqpTtxHpEJyqKwcQTtuaW4kV7L34qu6f0iISfw9h5NeVVnfjDihkycY2s8y6T2\nfLbHiGe8CqeqsoN8D0vEvU55GhTHo9/i/VPfeIbe09ODTviX6tBpXToLKcUuTPYBuObDxjE92Ltv\nhBFL14z8tSylOJiOcX/7W6Btdpo6stt5u6qrCUlfb2tjump7E8anuzPOufww17Nmca3C/UDlby5k\nyqh7X0grgmeDStxtkii1lGE82QknJ2tbxFIipZRSnYKOnFsBeVp3Tw/lyXtSeQGylYozTBFOejzV\niG1s4BoSLlxWlFLKygbd/QNTI4zYPH6s7K3V1YLPcKwnOb+yxETOTUfhThg6iyWBHfUYd5KSPtSH\nHXZsXbDuSlcn10CuPVlCkusejbqWUwTpg5QxKaVUXR7GlbxPsRNu4c8+BKmkXxLuh5NTJOWVFcEd\np6kgz4jdolm+V/S7cBoREubqw0WU5xrP8jtL47en4C6SMmMgvTbp1UeNuKMD+wyzs90fb0GWNP/t\n+5DnwDLNr++DFLi2GfP8CUG3Vkqph6bCxWXJgql4jzvkbXFL2MGrqwt7rNYmXMOnV35HeY9OhRzj\nifeX4AXTfqRoM+5PaxHWBgdfdrTa9Dlk3OPmYa9TYapXM56boa4WQmagrjUVsCQkeBjWQlshJ3Bz\n4b2sm79Y3wNQN7a8sIzyUoTTSIhwCm2TknylVLCQYBSlbTJiz1hQ9T09ed9UXghZQFkepLs+vXkj\nkXMYrkm9xNxxDnWnvKos7KedQiFFMLs/2Z7HntwpEHn739tFecPuHaWuJpIHoK44ijYJSinlHIZz\nk3Lst+/9gvLuexouLonhqI/eJsm0YyHk3q2NWId8kjgvZz8cAKubMA98gnAtkh7lR6iaIlGHA+EK\n1d3KUroGIV+tLcO+1rtPLOWd+AEycM+TkMF0muTijZewn4u4Gc9Meb9zawDvFHZSsyRyL6L2JF3P\nrprSvSnzX5Au2Xpy24qpd6K2nV+H56zzK05QnnSHk2uIR6RZPorXpNtSTQue79orWUp88gj2jmNv\nwTxtbeZr7hWFdVa2gQjwYHmNlIkW/wFpsqytSimqw5k/YP6GzeAx4Why5bQ0So+gzidMZnm4lK6l\nfYQ6Nehp3jNICZ58ri7ekkl5p05irbEWjrmjruN9fuwtcFtqrMJzSO5qOCgl3H0Tvef4V5Aju/fH\nXtg8d75ZBodEF+F2G3iIpVVZZbjHzpswF6sb+XnM0x/1SrZNsDG1M5HX5b9BM2c0NDQ0NDQ0NDQ0\nNDQ0NDQ0riH0lzMaGhoaGhoaGhoaGhoaGhoa1xD6yxkNDQ0NDQ0NDQ0NDQ0NDQ2Na4i/7DnjFAh9\nZ+1ZtlY+I+wxWzvQRyLUhzWt/sPY0tb4vPQK/g9hGddYhP4sWUJTppRSjvbQbR3NhH7NewV0XkHT\n2XZN9q8oz4O21yeZ9Zc1F3COLfmiV05aKecJ/emQSdCVlmawFWjGLzj2iCnQRmdu5n4YjnasRbM0\nrEWfi0UvzqPXOuqhIWwrx3md/uUU5dWKcx6dDB3ilR7Wq0t7ZL9x0H9Gj+9DeXI8OYfj3nV3oh9G\nzF3cl8g/B72NPGMijHj/62spr7gGeT8v2W/Edy2ZTXnZe6EpvlgEvezMydyboVX05AgbLCxit2VR\nXkc1a88tieOf4Dx8fFjTKq2M41ygY+zu5uORul9Hod2uu8BzMXkSNMuZX6OnRvAs1r56D4RGu3AH\nbI1lP6Dy7D18DMJCOGQqxoSDA1srF+w9ZMRd9agvW5exTWFSRIQR+47BvWk2WbfLMZH3E+alcyRf\ny4K16E0T+CqPF0vAPQ49Yno6ufeLt9C8S0vrujS+Pw5BuHe+c3G/t73JNr+b38Qcvm4utNxdTR2U\n5yQ0/YVCSxswRuj2B7DN6M8frjbiIE/0o+kXzvU+7RA+L1jY2qc8cyPl2dujr1f6hp/x/z7c4+OK\n0GW3CuvxwIlsa1/wK3TTUdxO5H9GUy7620Qu4BqV8c1hI3YTPVMacrgvm19ivBFXZ2Ady1x1jvK8\n+2C8yL4ZDSXc12PYc7CN7OnB2K/KxbysTue+B7kbsA6Fi/Wpo4N7d7jH4Dxkn5myywcpr/4yNP19\nZsN6trn5MuUNfBT3/uIK2PyGX9eP8s4Im9qEucrikL2SOn7nngZrVuw04md+/MyIl7/MPWLGL0Cv\nlcYKaOErKvg+Otnj3o2ZDz19Re4hynvqtTuMuKsF8zR3B8aISwTr9oMHoS/Crpe/MuLS2t8pL8wX\nY6nqCPqyJdx2B+U1ZGL+uQhL13+9xD36XlsL++PsA78ZcfTohZRXnM51yZLI+A7ju+/9bJF66mOM\nz+iZmG+ecVzLdr3yvRH3X4Ri0WPqn2Vli54IZ4V9auhQ7nPhFIyeKQ0ZmBPhiQvw/w1sA+3pj79b\ncg69m2ov7KO8oFE4Dzc3vKe+nsdvcznqjavoQfXN31dR3ti+fY24uxXjLWkh17XGXFG/uA2FRdB7\nMc7FvKe88CnqgLSwnZ7MxygthuXcLt3K/cj6paL/15EP0JNr6+nTlJckLLxvf+sGHM+vK404ZFI8\nvSd3JXrEuPdFPxvzmnvHA3OMeNlT6AV4x+u8Lsr74NUX42zN336kvIp6PK+MXyP69flw78grXTym\nLYkRD8FO+vAnpnHrhzHoHIOa0lnP+zTZL1Ku9V5e3IfI1gP1NPmOB4y46PIGyqtpgGW97Nu45t/o\nlxLh50fv6ezGvsxW9AlJfIx7fWV9j72x3yicn08y9y6qS8fzbGAqnhePf7yf8oLiUZfCR+Nem+9Z\n0XrsUUMfVxZHg7C3NqMgH8+4SYswNgsO7aW8wl3Ya4x5CbbavazZcnvObMzFvW/jNe9ErtHlZ9GD\np3QH1tnwRdgznPl4Bb0n/t5JRnzhs21GHH0H9yh95M3b1H9DzSl+7ncT/WhcRb9SfrJVyikMY7WX\noL9UH+Weei69uY+jGZo5o6GhoaGhoaGhoaGhoaGhoXENob+c0dDQ0NDQ0NDQ0NDQ0NDQ0LiG+EtZ\nU+05UJicI5iCM8QLVmn+Q0DV2vf6Jso7/wlou7mVoEtNuC+V8lobQW/L/fWCEUebrLTzfwNdXdJO\nw+aDXthSytZW9HdK8Jprb7b47KjFMfiMhMwiwmRT2FwEy9oeIdMwU++cI/G+muOgOMZMY1tVn4Qo\ndTXhNQgUsTaT3feaT0E57hJ0PilBUEqp2GBIwPrcCSvBtI92Ut7ZfNDtrxOypO72TsqTFPhiYaUd\nczOu4ZEvmPI9/BnYqVVfAG1uw0m2C7xnwTQjHjUd47SliMdFzETIdPzTQVPramQKaoewXtu6DsdU\n38zX8o5nmM5tSQxaCup6Ux7TdIclwvItaDhseaUFrFJKbf4JVNMJk1OM2Hsw0zA76tni+T9oNlmV\nHlwDWueQSbD8bBGWzl792Ya+uRivdbdjvFVUFVJej7B7dumDsThlSCrlecaDknruQ9DY5VhWSqnH\n/3GrEa/6cKMRX5c6k/KqLrA00dKoOY06UHy2mF6zE/asUs6Tf4nzAv0xVv1HQEZkY8320bc/Ai1I\nUxbGjLWzLeU1pKEu93sAMo3iHZDtNbrzmOvVCxaVJbV4bfAolqbYVOCzw+eg7llbs1yp4PRmIw4a\nC6p9xQmWDuatSjPi9CxYuU8bE0F5nSYauSXhlYgxXXORx23v20CZtbfH2Mz4ieV4decxzqQkq6Wd\n5150AuZ2VwtqqLRkV0qpK1fwWnMjZC+Slt1jslB3csZ480vENW9pYflTQADmSH095DpB8RMpz8oW\n59irl42I+VjrK3APe18PiU9NBku1fEJ4DbI0Jg9ErSyp4fG98O3bjbizE6/d/eWrlPfF3c8ZsZ0N\nznnK3UyBH3MPKP/1gjae9h3LUZa+A/vPoyXrjfj3r0H5njTsGXqPlRXu8fS3Xjbiz+5cSnl3f/6Y\nEbu4YO1rb2cau5UtfrOTshx7W76P53742oidIyAPPbfya8prLcS6G8Hbuf8Zjl6oIwXrWC4elAzJ\np5QknfuQJRfSllfKOsOC2Uq1WtDce4Tk4ko3yw56WaM2hk2HXCdjL6QoEcOn8nvEHKkS9PeYW0ZQ\nXskB7H8rXTBPGzKqKa+7CfXA2gWfHR/MUv6GVkif5dps58FymMxduC4DZimLo+IIavn2X3jfd+en\n0G5c+gb7VWllrJRSNsIiXUq9b3iY1/heNhjfo57FnvKD4espr08Q9kVNBZANXTwImWb8fN7zDXte\n1JRL2Bv//gvLPh68BVKPx0Zjb1ewh6UuzXn4uz1dqN/NpnXittevN+JKk3xCYpOwP35o+c1/mvd/\nwaVvsA8feANbaV/pxnw5tQo1LzyC94dNjbAejh4q1sVc3ntGX49r1tmJa2Rlw3yD8GQM1vJ83A9/\nYXd9+DLLbgf3xt9tyMQ4Mlsh27pDWlWbhvW8+ATvCXyjIAu+8DvWz6FCBqaUUiVivyX3xg1ZPLcD\nJ3PbDkvDzgtz3y2O25Q4R+F7gJPiPibOTqS8MS89YsR11cIOPobbFzSVQn4YNxLtEMz7t4o92Btk\nC0tr9ws4Pr9xEfSe2hzUx5JKyDLtTOtEVQFeKxDfUXi6sGW5bD/iJuS+zXk8NmV9kdbhXY38DFxw\nMM+IB8xR/x80c0ZDQ0NDQ0NDQ0NDQ0NDQ0PjGkJ/OaOhoaGhoaGhoaGhoaGhoaFxDfGXsqaQyaA6\nlx8xUZ2HgxZbdRaylAEL2RrDow+ooU1vglJndn/yEI4QXkmguq34x6+UJzuoL3kRVL6yPejK3f9W\n7njeUIdOz24hkPjU57FcwFnIl/Z9h475kvaqlFIp94NSl/sjnF/8x0VQXnMh6HbeQ0AnPf0bOyHZ\nb4SMa/4H45Wlkb4ONPKccpZt3PT3+Ua899M9Rjz0pqGUJ6nOa/6G7vKDRvelvHk3Q95SdRrXt/Ey\nU/OCpoCaF3MTKHE1Ylwk32tyTaoEvfzUL6BQzhrEFMrvft9uxBMGgEft4+pKeee34LrEj4PkwsYk\n+7i4Gfdn8gzIPi4czqA8dYVdBiyJy4Iyam/P9MrmFlCTKw6DUhmxkO/N/EemG/Heb0CfnZjCVOcD\nKyAPyhGuB9WNLAuT3ctLV+PeTByCGlB9ljueB4zA/L30KZxt+j40mvIuCXcqp3DZ/bwX5WV+jbnU\nbynuTdUJpvaeWY28WYtAJ+1uY6ph//t43FsazuGg0w4YyB3pu4XsxDkI59x/NmsB/AZirNbl5hmx\nt2l8F+1GXS6rA/Wy35AYyvMcjOOQDglufUAbdwlhV6tREyHf2bIBNPSLx1mGNHg+pI3eMVgzKnOP\nU15IIupeczM+w9Gfa28vsTa4FIOCmvsDu59E3ZKgrhbOCwczTx+WsnY0QALpGgnZY/T1JnnCIdSe\ntmpQuUcKmr1SSjWXYV7ZCWlGxaECyqt1Rd3sboPUNmA05lvh7+n0HrleSQlWRdZRyuvshAyw8hjq\ni3RBVIpdrHwiUJMdHdnBy84OlPz6MhyTkz/TiG3F8V0NOEVhTHu2M426vQnrVcEW1F7vQVwrx8+E\nQ1DxKdScnV+zjEFKJHal4d4//9OnlDf3LMZxvZAULf0KUqY/nn+L3lPfgvETPwxz+6b3bqe8C19s\nxfEsQT3I+YllwS2VGLeuwsnNLHUecDM+vzQDa27BH+wmlVGKNWCUsiyCp8Mro7ORnV/shTSnQMjh\npSRTKaUGPDrZiLN+xNoXcT3X3eqzkKT2T0JdK93CNa9atAPwH45rKaWqe1/+mN6T+DD2lKHCFfHS\npyyH9J+I+ezTF/c6fuJIyss5A0chKWc0z1npjOQ3BJKDwk0s9eg3n2ULloaUMs15bBq91tIoao7Y\nm+3YyZLAu+ZCujxtMtZxN1P7AllvO5uxd1o4kq+hnxtq++avdxlxSjRkL2/e9CC9Z6LYb24/BwmL\nnP9KKWVri2M69Q4ctP71xx+U99SDeJY5KZ4bJl3Hx1p1HHvtPdtwXfoE8h6DR75lkfgojknuy5RS\n6konpH9Sft1S00J5G0WLgomd2Jsl3s5ObO0tqM8drYilfFEppZqahAzwGK7R5WLEm3btovfcNA77\nQ1tX7LXTlvN4C4jzF3mQOEVO5P3VgdVYTwePw/joauW9Z00OzqMiC3ub4EEsBWqr5HYKloankFJL\nR0yllLq4C5Kg0Y9hz+YbwpW9pQXP47YOmEedbfwMERwHeWd9OlzzcpafobyAKZhzxT/jOgWNx/+b\nW3ZIqdmQJdh/+UYNpryMtXD4CrIVUliTRK4xA/KnbW/D/SnUm+WVbrH4LiNkJmp53UV2XW04kKv+\nCpo5o6GhoaGhoaGhoaGhoaGhoXENob+c0dDQ0NDQ0NDQ0NDQ0NDQ0LiG0F/OaGhoaGhoaGhoaGho\naGhoaFxD/GXPmcyvjxixnTdb66V9BJ2etK31M2mya05Cb9xnKuyuS/aw3iruTmjUPQOhb31ieRLl\nubriMxoboYst3gSbwqrCI/Sejnpoke09oHcv35VHef4ToOdNGATd4OVznNdeC52qleiBYdYC1lyC\nxuz0fmgfZa8Opf5/219Lw15YfLo4ONBr7cImup/QqzcL60ClFPVTiQ/BPfYbwf0EgqJgXXfu6xeM\neNAj3FOkvRZaU9mbRwl79C7RO0EppTa/D838tIcnGXHJZtZ8D43BeUx4CZrdkkOsYxw6/h4jzj8s\ntL6m1jFxE9HjY813sDSdOJh12I5+3DPBkqhpajLi4bezBV9nM/ol1J5D74kN4noppdTCf8wz4oEj\nMI++f4X7OsUInbKXsJP7YT1bTd67aJERj03qb8RS477jV7bFvGscdMk94l63NzRQXv9H0R+nsRQ2\nej4RrD2+VLXGiOsz0aNh40/c86G1A9coKhFjVvaAUUqpRmlTHq4sjq5m6Iydg91Nr+IY972JHg4+\nbtzXxK03NK4VB9B7JGxEBOVJHbTTYfTDiJ7Pfa0yf4XFZOFG9ADxEb2IHJxZM39gB8aMhxN6Kbz4\n5ZeU944tLEOl/aBHSB/Ky90Pi9Tudsz7jppWyss+mWfEQ++GjrhsF68nJTvQIy2U/9T/jMhJqC/V\nB//ctlRazzv4cO8cFzHuyvfkGbGpHYaqOoTPl5bykTO4H1fBDthVxsyEdWxpGnpoxNzKfW+qzuOa\nlV9G/6euVq67reVYw3tPxLyszOV11sEX55i7G+PXzo3XHLlO+qZA4122P4/yAlMj1dVE7HysVRnr\nNtJrZz7H9WgQPV1cz5VQXthIHKPsDTBm+m2Ud/rd1UZ833vojbH1+fcp78lP7/6vx1p0AH2OJr76\nGL1mawt70zvHYl1cWss9WC7nondHsguOL3wBz7HibegZ4z8W5zfQNC6KL0F339mAfVXyE2yxPlQc\nn6VRJvpqyb4WSikVOhvrdvy96Ctz5n2+1wfeWGfEo5/HGpmzlnsvnT+GHnMRfujR1NTG1zlLWL1+\n9Man3WriAAAgAElEQVQbRvz44sVGPH7BMHpP+YE8I/YbJvZU1lwQirdirxOSjDpeVrKB8uSeqv4s\n9qG9b+f99OZX8D7foZiLTqG85ph7s1kai17Gdbd3495pZz9A352gMRiPt/xjEeXJPidWdvjdOXsF\n9yPrdz/6XDSUoAZOvJH7Zth5oG71dGJPU38R/UAW9J9A73EV/W1uSsUGws7NnvLK07AXHfzMLUbs\nHMLXXa4TjbsxT/f+xrV3vtjb1f26x4jN/ZVGTk1WVwvyucgpjM9D2s17tKFWHM/mXqaL56J2eCZh\nH9og+m8ppVTMdNTuXr3QF8bRkXvsnP4ZvZ1O7EZ/0LlDsY8c268fvUeus3Ze2NuYn9si5qB3Sflx\n1AbnYD73EfNSjNg1AvdT9hhUSqnohehH0yz2of6jIiivtaJJXU0c+w5jy9xvNWEWevnViGeNsl0r\nKM9XPBfaiX1o3qo0yut1K+aplR36BfV7ZDLl9fSgxg66GWPayQm9S6sK9tF7wiejxmavRQ/Lng5e\nxzwTMWY8w7C3a6lnS3T5rOB4UTzrmeaYnOuyD465X6a07f5v0MwZDQ0NDQ0NDQ0NDQ0NDQ0NjWsI\n/eWMhoaGhoaGhoaGhoaGhoaGxjXEX8qa+t4H+l/OhoP02sAn5hpx5UVYDQcMYFvjhkrQvdqqQfFp\naGUqbS8rfE9UVw76mbMXy2b+ePZZI056HLROt76wr7rSzboUr+goI+7qgnyip4tpsJ6REUbcLize\nkkJZfuAUANplcTXstZKDmIYd5ILPa90FWmjv8Wy1FjyCpRqWRu8FkJwkBJrspD8DhS1qHqyXv3rl\nZ8p7fiXouZd/Ap3ZzS+W8traQPsOHYJ7l7/mAuV11oCmFiFsbyUNrDGb7bdHCEpm0TpI2nzH8BgJ\nmQs6c+kRUFpdo9hSMWfXZiOWlPzooYsp79iXbxvxjY/PNuLv/vkb5U1rB00x7GWm3P6vGP006LNm\nWmPZ7jwjLsyHfZyZkijp6g++9okR3z99OuVJGVy5sGBe8eKLlGdljTlbXgEaZuSQCCMOLGfZ0Im3\nQe/3EFbNdiYb6PIzGC+u4aDFF59l6uIqIcmZPRzzaMGDbMcpZXouUfg81zCm3DeXsrzK0ijYAxpv\nv0j+23mrcc7BgahnfqkRlFd5FHTL9mrUUc+BAZTnKMZ00mM3G3H2Fpa7BU2AHaGTB6QZh97EvXJ3\nY+ng6Cmo8xlH8NonTzyh/gyOHrBo7OhgSqeURfiPAB089+dzlJe8GPf4vLC2TLiTa2jBLxfVVYNY\nXuIfHEcvnfsQsseQ6ajzpz/YT3lJj0HmGThRXH9fHhNOgcKGsgnXqKWO5TUxM+YYsb29rxFbO8J6\ntrWuht7jLNa1ovWQsyXcexPlXbkCGvDFn34xYp+hbPHpEowxKym8B/7F5x4/GOfbJeQSribL26Kt\nqFeBS5TF0doKSUO/RTfTa/a+a41YymV2/3KY8rLXoN5OvRv7kd+e+oTyUmZBTlK8CXui3iN7U96x\nzw4YceqLC424pAB1w9raid7z77sfMOKHbsO+bN2mA5TXLwSyldZWyCHtnfwozy0eNX/HO5D7Jo1n\n+v+v/4QkZva9kFM1lfLcdvQVez0P3h/+r3ARNbTiINvL27lgfG/7+w9GPPhmrhUtxaj56cv2GPH2\no6cp7/oHZ+A9RXhP2VGujY62mHPrf4NV+oktkLLUn+dr1OceWD9Xn4eU0S3eh/Lqz0GiVJ6JPbln\nOGs37T2xd5LyiSPvsTV3UztqipM/1uoz3x6jvN6pvGe1NJy9IKE1j++Bj2Pvc+SfWLuOZfF1txbP\nEDOmQvbZy2SvnPEjWjJIKcWAW1mKeOwN3Dspi45eBPmJtGtXSqk60crgq4+wP3z49Vsp7/JaPOPU\nCctf3xFcU4t+xz7XXowrM8oOQPp9/yd3GHHa5yx/KjiBOZJ0o7IoHLww33yHsoQjbRlkmbVCoj/7\ndpaFScmrjPvPu4vyqquxplhZQdZUfH4H5XW3YH1JFlL+7dswvgM8eI8aLCyui7dhjAWO5+e782Kt\n7+jCGtn3TrZq9kvB+9obYCWd/j3XFzc/7IEL8zAm3PpwDTj/PezGIxMtfBOVUs2iJox9miWqdk5i\nz7ALEqXI+SmUV3oQ+y9H8bxsrqk3DMW8DxmD5zsnJ77WDQ2QLNq5Y84dfmO5Efe7h+v6gdexf024\nA8fn5MfW1x8uwVp9w51T8HdMc9tWyJVa8lD/HUP52eXI12jlIOurjRVzYaytuS6ZoZkzGhoaGhoa\nGhoaGhoaGhoaGtcQ+ssZDQ0NDQ0NDQ0NDQ0NDQ0NjWuIv5Q1Fe0DbdzJ1EW8uxv0LI8YdDsuv3iK\n8tqrIA9quIiO2yEx3FV73T9+N+JFb0MSUl/M3bxLayGf2H7bB0b84Lu3G7GDt8kZwwVynaYm0Lc9\nEv0pz9Exwoizt4HW3HOFZVJjRoCiPOUf6LSe9tFmyvMaAocTSXX1HcyUrez1e4x40K3cTd8S2Pdv\nSEGmvsASlpgbISk6vwJ0uUc+Yx55r14YKnZeoHtdWs7OB64xoIwd/wOShFmvzaW8+myMhSOf4Pik\nM5JTEI+5qqOg+7ongIpdtJPHSO/rQDv1S4GkramEO763lYNe2XsCur+3tHCX7oj5+Lzc1ZBJ3XDH\nFMorPJyvrhYOvAMqbougyimlVMoCUMUljfOtX9ZQ3t/8cc0emoXzzSkvp7wsQdFc8uyfy7NcwkAH\ntfoJNF1538Y8ybRV6ah0pQfzqrGggvJsXUBVdffD/C34jZ2lbr4N0suWQiFJMnVQ7yVcL3zjQc/f\n9vfllBc/Vsj0LMvAV0oplfggHHOumGSV3imoF3VncU8qhJuPUkrZesJFImwe5oukeyqlVGMuZCzh\n/UBL9xtxifJahJSrPgu004gJ6ITvPyiO3nPhY1B6/ziLOXHHBHaCCluEe9dQjHlVvD6D8uT9cRTS\ny7JCnrPd20HR7zMHcs0ME0W4q4evrSWRuwNym/o0HrcOnrgH7YKWPeTpWZTX0wOph50bjrU+j+ei\ndzTuWy9v1OCqDJZtufm0iH/hNxcbR7yn5A+WAUQtAo1YroWbnv2A8ia8hBpw/hjOfYZwblBKqfIj\nqMNNOVinR9zJLlFSSuDoJZ3HeO/gaJLgWhoODpDD/vwISzbHLE014hIhQ4oPZjfK0Cni/ggpl1lS\n+uE7PxnxlIEDjTjI5EgYNxnU+0tfQLLpHA35ztF/fkTvSUqCpKVL1P8XV/2b8jo7UQ9+ePifRhwd\nwHLIuLtAy0+agDkWMZndbEL2QXJh7YBxdmwZS7/ixuD4AhYqiyJzO/ZzSUvYAamhAC5jVoJS3lLE\nTpQN6ZAAfbsd6+wr3zxCea8K+vt9d0FGWCL2pEqxLEA6twxbCGp9YIpp7ggZb1M2Pq+Xya1p2Atw\n6qqugLRj8/PfUN6AyVjjWsXxBMfxvvvsNuxZCjZAptB7HMuY7L1ZamRpNBTnGfEV035724dwfht7\nE2pJHzuW2W34EnmFlyD7DIpk2V7sYuxJbGxQY16/4R7KmzML4/2rZUKiFAn55UufsEvNj/sw51z/\nDQfCg99wW4gJT8GNRkr5l73I7QTmTIe7ZWIY1uDay6a9bDGex468v8eIx7/EcqqMn7erq4XmUsyj\n0u2mPfks1LWqo8VG7NmP7035Iciugkah9jQ351CejQ3mlbU1am3wgGjKK974lRFH3oR9/JhK8Vxa\nw20C2quwNvsMDhL/30J5XkJGHjgGf7fL5N6mFPYsmd9hn9J7Po9fKZFzy4KUyex2GDk6Sl1NJI/F\nccmxqZRSJ1dCKiulQlZWfIxlR7DX8x2ImjNpBLuFXVyHZ8R+tqjRVYdYnuYWh+vhFIQ5O+BByBdL\nTQ7QPiGYp5XieArTWOo3YzTOo5do1fBXNe9wOta+VH9+ZvcWLRpshHSp34z+lHdiLe93zNDMGQ0N\nDQ0NDQ0NDQ0NDQ0NDY1rCP3ljIaGhoaGhoaGhoaGhoaGhsY1xF/KmrpaQbk9uy2NXnNcDxlDUASo\naSEz2b2nKQ+d/z2SQJ2WLhRKKTUpAa8dfAt03sihLAGKCkDegnefNOLcnXAV8I1geuu5H7814uh5\ncNcIHzOW8krT9xqxdP/48WWWUnh9tAmfcSOoSil/W0p5uYcg+Ul4dKR4pZvyfIeEqKuJQE9Qos0O\nVTZOkI8k3w/K6IlP2Olh2NPIC0oFha/yBDsktFeD+hcbBgp49nKWHcQtAbXUxQEU/fx9oC/6xzDl\nMWEJXEQqckBN8xrIVF3pMDHnzduN2C2EZR9S/pZ/FA5UEcNmUF5nC2iKx45DEjLaj7uyZ5WVqauF\n9k7Q1eMG8JxwCYW8SMphPv/pecqrFLKw4dNxD0PWX6a8+AfgHNFSBrpsey07rNk6Yg77DMcYll3N\nbZ2Y3m/tAEp51LD5OO66E5SX9zsof51NoARXV9RR3rq9oNBHB2IcWF1midnkJ0EjzliJ+uJoZ0d5\n6fsgYUi0MAVfKaUKN4CGHzA2gl7L2o2/3dGNGhFs6i4ftRC16eS7cK+QrmxKKeWdgPmXcRDyrW7h\nkKMU1wBJoW0uxL3K+pFp2VJ2dltqqhEfuMiSqaTlGDP97gJ91M6H52JbCajF0r0i8TZ2Aag6gjGc\nsxF/K2Iy0/Abs9iZyJIY/MQYI7aysje9ChprYxHqQeEOprC2lYMu3Pd2OMBducLSI1tb1O6ODpyT\nb+xAyquvRA1tssM46hYOcpELmX7bVof7+8dySEtvfp8lrUeEK490REn7mJ3Tkp+cZ8TFtpDIdtQx\nzdslAue07SWsraMfYucrdRWlaUoplbEWMuTUh1mOd/YbuHmkPIr73VjAEpauZri4WPXg3o/422TK\n8/rUxYiHv4h9S2Eay4LdwyC1OrURcsHQduzF8ivZ6Se0HZTvLlE3aioOUV6XcCFJvRn7kR3fs5tW\npFjDPftjv1V6isfwlH/cYMSOjnBYO7KCaeN957ALjiVhK5wFzU5nWT/CISZ2FOqDVyLvF04I+fXd\ncyFV/uH51ZT30GPXGfG+tRgfsUFBlBc7D/IJuR+KGIvPzty0gd7TkIZ7mvA4JODt7aWUl3MQcnv/\ngZBGxQ1jOUfOPshKoidiT27jxI4/twzEnPWNQ03Z84/vKC8oVkjfWKVoEZRsQd3rc+dIeu3697Cv\nrs1DbStam055U26EA17wSNS6ygucVynWqNqzqNGTByZSXpBYU57xEQ6eQr64dOpU+RaVuRLPELOn\n4EI5+PE+aOPrmPfDpuC6R/nxnjd0BqRMBetx3GHT2Z3L0R/1pearo0Zceek85R07DPnckPuURSHH\nVk05SwfzfsI4TpqHe5O/huW5XfWop0dP4Rol3svPdNK5sFk4pymTJG7Ao2jjcP5D1PuASZAG2Zkk\nYu1lWJul7Kp0F0ur/Eeh5lUcx37TJ4nrgXx+CBiD91xYdYby6prxd6Uz1/BwlpPKdgBXA91Caiv3\nhkopVSuOsXy/kESW8X2U0tjqU0JiOJndCbO/gMRN1oCianbq3bEO+9zJiZins/8JmWduMTt7loo2\nFsl34Jmms7GD8mpK8Exx6BTOY87DPLele1gf8awRNJlrb88GPE+1FWDNlXNUKaUmPDVJ/RU0c0ZD\nQ0NDQ0NDQ0NDQ0NDQ0PjGkJ/OaOhoaGhoaGhoaGhoaGhoaFxDaG/nNHQ0NDQ0NDQ0NDQ0NDQ0NC4\nhvjLnjNtZegDMM6kj9r7LqyuMi/DpqqulLWGlQ3QA8bEoC9FxT7uVVIoNGZjHkw14uqTxZSX9CS0\nnzk70FskbvqNRlyet5Pe4yn62TTVwG6r4jBbJvsMglYw83voAd0cuT+CRzL0t1LHnXd4E+Wd/g0a\n7ZGBqUbcVsnWbQVboKMNe/PPrYv/rwgZGYG/9StrA7PzoAdMGA/bW3cnthE7/T76C0jdYbPJ1tlK\nWBin3p9qxH4xQyivsQ7a17YOaAAPZ+BaTBA2ZEopVXRqN/6u6IfRUc29UBLH4DzOvo970v8R7itQ\nth9jIXwKNJIFJ7ZQ3qlV6J8wKAFa3/JTPDbtTMdrSfRPhRWhVwJbn55dBo1xn9mwwSvdwRrZsHn4\njOxvMb4TH59GeU2VGBN7vthjxJOe4j4K0qrUuy/64NTnoS9IQy5bA1cdwLwPHQStZ8leHpdkU9gX\nvaHKd+dR3v1vwsq+WVhpu0Zy/4GOeuh+w+dhfPhWhFJememaWRo2oldLfSbras02p/+BlS2Pq6Yy\nXNOhf0MfBGmVq5RShVuhN2/KRK+M0AXxlFch7olLFK6btMd1i/eh90gb8PNb0I9s2gLWR/eIXhlt\nov+Ce7wv5dn7oN74DUPfjZzvWZft1g/vixuOe1d9guei38hwdbXQWIBxW2EajxE3ot+EexiOz9GP\n9cZ16bDgrriMflxOAWwf3d6Oe52/BXUocJzJTlPI0K3t0DeorQq66+IN3FuquQF1MzEc1+v8h39Q\nXsLdqN1N+Th3p2B3yqvKRE+E8oMYU84BfO5Vh1EfPITl9Il/cV+jQXdxnwFLI/M45vqAG26n11yf\nw70r2IF+WM6hfM5Fog+Bn+h9lraTe7YFTcT9kn1mtn/KexW511j8+edGXF2NvjAXnvia3mPvir5H\nG3bjGgad4znQLfYq0dNnGrHHGu4lE9AP172pAf06dn7I9qbT+mAuNpZg/g2ez3ap51fheJNveVRZ\nEo2tGMNV57jPWPTNGLdVZ/OMOG8V909MEvuFkClYP8MXcQ+vhmzU68GD0Qtk086jlBech7HTIWpe\nSwp6KjRnc+8i176orxeXoTeGzzDuRyjXsdp87JUCx3Mvh1bRw0vuUfes4j5Eg5LRj0b2RBjzwvWU\nd+Ez3hNZGjF3oD/L/tfX02vjhB102U4xZx/j3oAf3PmWEd8RjnWs0bTO9lmI3j8Ve9cY8d4LvAcZ\n4IpeYBVtGFvZh3EMj3/EtvYXm9Drp6UFcXM5r82Xl2GPNdYH554ynXuJNRWi3sbeNBHHc4HH8IVl\n6K8UNiLCiDsauN/Xgtfnq6sFW2fUIdn7SimlRj2UasTVp3HuTsG83vlfF2HEaZ+in6DZUl7uiWTf\nr6BU7nm659VV+Gw/jInjP6FnlOz1ohT3kJLrXfxNsyivrQ3rWPVp7IWLt2dSnv+oCCO290J9D4zx\np7yBY5DX04nrt+vjXZSXOCpOXU1IO+m6dO5vNu0feD4t3o1nOGtH/irhw4eWGfH1c9FLrtL0zJ1X\ngX3QC198YcQ/vvoy5XnmoDb5e6DHZt4erEn+4yP4RMRlcwnCWhU0mffTbvIe+6OfTb2pF1FLAZ4v\nAqPQiyjrW+6n2mfJICNu/xpr69nlxymv91j0tArh8q2U0swZDQ0NDQ0NDQ0NDQ0NDQ0NjWsK/eWM\nhoaGhoaGhoaGhoaGhoaGxjXEX8qavAeD3lW0OYNeC/UDTairE9R1JxOFOdAXdq5dzaBX7jvGFm9z\nl4Jq6Cw+O+cy22Nlb4WllrRua2jA59VerKD3BI0CPbUmA1KWriaW5Gx4CxKY8YtBz2/dytZbxQfz\njDhh4HAjvvAD04OlhbW1PahU6b8zJTG4H1uvWRo7fwGV1d6Gb3mwN2x6t69BXm9/ptzFzQDdt9cO\n0HPdTPKnQY/BzjDzX6CDuz7AMoYr3eDhh8Tj/IOFvC29pITe43YC1s3b9uGzk6OY4p80BbRsSeWu\nTsujvIqzoCI2Z4Me59afjzVpIWjaLaWwlvYZGkx5Tjsd1NVC8DhIUbo7WcblHQCan5xj0hJPKaUq\nhfSjrQ1j//jbbOvp6oM5PFBQvl19+To7hIJyfeqzfyMvGuO+4hjLTfo9hPlSdBoytarTbBnqKOrI\nhZWgpg5+5lbKO/PxT0YcMguSM49gpreWncHcbK8F1bzBRF10MNUvS8OtD+Zb5UGmeBblQ8IybCnm\nUWM+U+A7G3HvLn4NunnUjQmU11YKum5NE2juHau49l4R9pPluaCxxkwDfVZatCul1JUuvCdUUEZ9\nU5iG7+gO+mf5acheGjOZ5h0+D/WlbB9o40Ez2DK0VVi7S9lV0QWuFd4pPDctiXZhqegxkOtkzTlY\nszZmgvYr5WJKKXWlG7KwoMGwC8/dxvbU3kmYzxHTUdeq0lmi5CQkCVVnQcEv3ZNnxNnlfA8nPgCZ\n57nvUU+HPcWW1o15GH9kHW3Lv+101KIuyTHl3p/tYZsLIEltuiCkVTcPpryCXyAziBigLI7pr99r\nxJe3/kyvSUm3HEs1Z8oob8WePUb8xXPfGrFnAs+x7nbcx9B+oMcnpRRRnrSUb2yEpMjeHjLPCJPd\n7rq9oP/f/fgCI/YZyJLNhnzUuqYmXNsBqSxz/OWJd41YytIfXf4Z5ZVkbMffigSVOzeX5U9Hd8IS\nPPkWZVHYCimxuaZUH8baE3kzamPESJbxNjbiXmWvxDww205XZKE2hgyB9PLud/ik8ldj3nulQOrW\n1Y51p6SMpTbxQnrvPRNrV30Gr08SrsEYEy1V/HmV5ZizHgMwXmb9jaVArv6QI7e3i9pVzhIxpwiW\n81kan9zziRFPG5tCr2X8jHF27CzqXkBuJOXd+BCkeo6+kEv2njeW8uzssF71X4q5WPtaA+XVFWGf\nu+N32MNLKf+mjV/Se07+8zsj9hmN+Rc4OIny5LNBaynmfNhUtvOuPIe1sCoDc3b3N7xOSAtux0BI\nhcy2y+2iRiteqv9nlO7Ds1XfhXweXS1YN5pzUf9bG3kvm74fkqCYFNzf3JVcTwOnQAci21HItU8p\npcb9/QZ89nJISKVVtZ0tz3N5xcrE+uka5UV5dZdQDzrrMSak5FsppYo2YcxGLMSzaEtwI+Wlr8Ae\n1cEO8vex9/P4dQnivYSlcfYQ9mkzX5tLr2V9j7XG1gPPO669+dqE+WKOfbridyOeMIAX8sxS7Pv3\nnF1pxLXneK+ydMJN4jXUqSYhD20ySUXjlkAGuONlfPaQu0dQno2zGAvuOKeAMVxfGrJQY2vP4his\nrXkf1NmCtd4vFdLimN7elLfxVcibE/gyK6U0c0ZDQ0NDQ0NDQ0NDQ0NDQ0PjmkJ/OaOhoaGhoaGh\noaGhoaGhoaFxDfGXsqaqI6Dcxtw2lF5b/9xqI57wCOhD5XvzKK+jFt3Ce9rRgfpSEdN55wiZy+YX\n8dkTn5vKxyTcm2ImonN0UxOoYx5xLEupywbVzdoONFjXGKYZDbUD9VUed+TEGMqztsdla8gBlbbf\nLexS8MfHoPfG2oKqOf7lJZTXVM/dvS0NLxdQ3gfNZXpljaD+9ggqenhyGOUdWgVHgqnPTcd7urgr\ne9n+PCOOF65bDYUsW+kR1D+/kfhbKXWgBwZMYhlNxT7cxwThLuLj50F5zcWgp7qJeyyp9kop5Z8E\nOmQv0f390PoTlCclIQsfAS342I/s0jD01qvnLtJYDKmelIQppVTsHalGnL8Fx27v50x5/sNxndsr\nIM0IncXd34+9v9eIfcTfam9n6Uj+DsjgnMMgOQsYgfkSPJrH28UvQVF2i8e9Gfrs7ZR35QokWY05\nkDJK9xCllBLmYORK0VyXS3lNQprh1RdjpyDjEuXF3suUR0ujaCPkob16sQNB7Ghct642nEtnHTsu\nOAuXHElZL9rGdcTeH5LDEF/ElelMGd1/Cddg8cNwqLD3xnvqypnyLWnBvkOE7OM8yz66haOBi3DQ\nsvNiB7yibbguoVMhszj53m7Ki54L+ZOUvQy8k9enioOoFZGs9vqf0ZABemvk9UzTvfyFcM1YiPPo\n6eA62dWK8d3aDGejAOHsoBRL8Ap34rOdI5jafGkZ5n1XD2rrmbw8I44LMslnRT0ceAecbVor2b1C\nOqRJOZaNsx3lOQWhBtS34LgT+0dTXksR6Nt9b8Wa2VbVQnl9H2Z5laXR0QHJSGM6y0KaanEN+i8W\nTpC7l1Pec6/cacQF+1E3Sw8wvd5T7EkOffWMEc968z7KqyvCXGxpgawi/TO4i3R2sVxVypyklGnX\nq+weOfpxyNiKhTQ5fOogyrMTdHX/QRjDRRe3Ul6akHGPfxmf4TuY9RJjHf5ym/k/ITwRa5pLBO8D\nfBaClt5ai/trbc21p6kIrzWWQ2pgZ5KAh43G5xUdyMMLpn2FUzjqc0sRPk9KQcPjWXZp54lr3pgr\nJEkmVzvpMFqXjdpqllL4BWNtjZtyuxFveuZ5youeAqcS92g4RuX/zNL7gCn/xU7Egpg6EuOn7x3s\nilN5GY59jsexz3/uPnZK+njja0ac+wscVPzHcD3rCsBaJiVOk157ivIKTmK8L132phHb2mKcleWz\nhE86abp7YW04IeROSim1+B3IbRxdMF8K9/Ke0i0a91HKKobP5GcNWb/qL0Juc3DPWcpLCMN8sbRU\nVI7VLiHtUEqpDiH7aWvCfibhUXZ3vPAJZDO5J1FDQ6LZoVSOd7mWesSx5LPkCM4/cCLG8M5XMD7m\n3MourqWHsB77pGDNtHHi9c5B7K9bRbsDz8G8zraUYLzVXsTeqyGNnZCklEnC0Zel9s2lmLPKT1kc\nfUJQm46+w/uvfjdiP+8aivtdc5Gf5weIcTZ2Jp59z+/h/fbzb91txOVCQtbVxOPHOQw1Nf4m7FF/\nffIDIx40lTd6W1/83oj7DMa9by7hvaxnPC6idAHrZcXcFfnc0JiDGt1RxdI8ic4GMe6ruQ716x1m\nTido5oyGhoaGhoaGhoaGhoaGhobGNYT+ckZDQ0NDQ0NDQ0NDQ0NDQ0PjGkJ/OaOhoaGhoaGhoaGh\noaGhoaFxDfGXYmDHEGjIj779B73m7Qq7toZMaLePH7tIebZCt+vhDI2emyPrfqWuPbcC/TXSPj9C\nee6hUleMng2dndBcFm1km1FnoQHOPYxeFI988AHlvXQ39G8pMwYasV8ya+ZPvrvNiKPnwxqtubCe\n8sKFnVi56IHQk8KacRtne3U14Si0jNLyTSmlTuXgesy6a4IRt5azPi5H3JPPHoFl6J3PL6K8oHsP\nuw0AACAASURBVFT0zcj5FT0S7L35fkudaP/rFxvx3tw9RtzyC/cgcPbB+Jn6+qNG3N7OPTR69RIW\nuweg1Y8cx/2LDr72LyOOXQwtZd8zrJlfewyfsflLaIzHTGXr10Zp5Wbh9jPnvz9pxDbCPlQppcJG\nQUMZNRO2e8VHj1OenT3Go2MgtOvNxTxuWztgexg0FWO/u53Hbc7BbCMedB96tbTWQBPrE8pWyCGz\nYRPa0QDtsdRxK6WUPR3rAfVncImFJjvtZ+iIk5bwDQibDoG1jQ00vMGz+Phyf8Vn+C2d/Kd/9/8K\nr2RYq7abentc3Iu6FXQe1oRNbdxzpkP0oHEMQh22ceJy7i16KkmL5w5Tz4oof9i4Wgv72JYijCvf\nOLaM9krEv2WtqDnGfYkGPXWXEZ96/xsct+hRpJRSXUKbe+RtzLEBi7kfRtVx9MiSNoXSvlYppRyD\nr54leuR1GEtmq9LGVuiPrYTFYumubMobcP9CIy6/iHkq+7YopVTGD+i3MOBhzLGirdxfKOZmrFeF\nv2INHj8WvQkcA/maOAVhXbwieofJsaKUUlHChvjEJ5iLHsJWWymlitZj/HqKtf7SZ7soL24p+gyU\nH0XvE2nFrZRSmRs3G/HMdyzff6Y2E70FBtzP69i5z9H3rjhtjxFH3sC69pMf7jfi/rdhrIY4sj3r\nmTWoK2Mewbm8ftOzlLdgxhgjTr57qRGX1KD/xfD7RtN7XFair0JHI+bi5FduorxfnlpmxGMXjzTi\nmowcytvwDSxnbxb2n8UbMihvyJOpRtzdjb/bXMqa/jO/Ywz3tXBJDZmK/UbeL9wnxVX0ZbIR96NX\nL7437uHQ/nvFYOx7D+LeES5BWJM8+6H+mfd9u76DzXF5PV5bdC/2H/mXiuk93W2iJouaEjiUG4O0\nl6HnWnco5m/NKe7pZy96jDU3Y471N/VF9AzH+i730HY+vF878SP2QDHDb1WWRmYWelZEt/O1kT0e\nneyxV06IiKC8Hx5fYcSTb0CNWf3G75R3/d/nG3HRWYwZW1feh9edxlgITMCaW3J+vRE3ZLF9u1sf\nzBcbe8yX5KdupLwjb+BYC6tx3UfdNpLyCn5Fjw7fMRinKz5YR3nRAejJMmYiasgNM66nvKZCthu2\nJEq2YpwVF3M/lSA/XJfYO1End/5jM+UNvwf3LUz0aXMJYSv3vW+id+G4F2ChXrDhHOXVi94gch81\naSyOoc30rNMjerbZCmtla2seH7IHZp/bsDab64uVLfJqz+NZ5XIu92kZuRifUSP2ORWHCyjPK5H7\n71gaDmLvVHuC9wJ5a7C36Hs/+tS5hPL+3cEN1032t3R3cqK8mpPYL3a3oAYGTGQba48o9FI79ynW\n5mZxT5996V/0noemTTNi32F4f7up9wv1lhF9IDub+FlZKfytkClYd3q6ud+X7CtUeCjPiIeOmkl5\nJ8uOqb+CZs5oaGhoaGhoaGhoaGhoaGhoXEPoL2c0NDQ0NDQ0NDQ0NDQ0NDQ0riF6XTH7CwsUZa8x\nYrNFaksB6Jpp50GL7RMYSHm/HoY1Wkc3aGqLhg+nvFUHDxrxrZNhbWampzsFg/advglU9smvPWzE\nJef203s2fwGafEEl6HaHLrGt19RBoLo5C/qklGMppVSDoK7HCDphZhlfo6EDIOGQ1px1LSzXieoP\nylXKPWznZwmc/vljI7ayY0nM1tW4VgEeoKatP86SmIx8UPN+W/+eERdvZnp971sSjVjaZVcdYwpf\nxGxc64ZCUNucAiDTUKaRaWWDe5K3FlRp/zGRpjxQ08r347gdA3gs2fuAYmcnaHjShlcppex9cf+l\nJe6m1fsob959U4w4/v+xd57xVVVL/1+k997rSW+kQugldBCUIiIqdsR+bZerXgv2a7liL9gVEUVQ\negdDCx0CSShpnPTee/+/eD7P/s3sR3nx9/DJm/m+Gt1zTnZZa9bah/nNTL5XmZLMdR9pNk2d1UPT\nm6mcTymlSvZCWjHuhQc1u62Np6vXnIXUzcIBkriOMp6uXncO6XsBs5Dm5xmL1ty5a3grvrDFf96q\nurWSp2XbuOHZGNcj9XiIOf89uYukpJrbWvylXx+R0UU9iLbLuV/xcR69DJIBDw/TSylK8jfA3sjj\nT00FUnA9vZGSbxvoyPzai0mr+Bi0P6Wp9kop1U7awtadQJqsMZ9LjwyhiNk2RCblHIXvrtzPW5MX\nFOD7EibHanaXrh2yx0giESSdw7t17cEtyTjrbUMbxWNreWvRUH/E28hlkBUWb+L30nMMaRk6lKd2\n/13Krvyu2fp10YLIwmiavBVJj1ZKKfdAtJcsOQnZT0Mm/76oOyBTbCxGXGq6yNPGu+uwJlm6QpLg\nFAF5nNK1brdywvl11uG56eV2xUeMmu3qhfXXYxSXf5aStHaKewrfE9iQFu1UFuYWx/16O5BW7Bc8\n70+/++9QdPFXzd76Fm87veB1SB9oC0y/qKnML+NVtPNt68L5Bk8IZX6hk7E2FB/D89bLAFMex7rR\n0WHU7KpMyFlyt3HpeNwiSNpcwvBMzMx4a1Y6fgrXIaYevnSJ+cUE4Du8nLjMjkJbV9NnvEcnY0sZ\nifXA1Pubi3sh1XKJ4X1lz32IPWX07ZDzuARwKSu9T11dWIfaKmuZn503rtf4G+QTXuOCmV/5LswD\n1yTE5PpT+G7faXx85PwE2dvQO7A30rckpnuiXiIrpO3PlVLK2YBn2NkE2UzNSS4ZCpiCVulVJ7CP\nd43l9/LQSjzTxR99pEzN5UPfafbur/iegb5TOHpifTKz5Gs8bZXc04K5uO2TPcwvmcihvtoHCd+/\nnr2d+ZUcwpqXT/b2N746X7P3v8nLPSz59FPNNmb/otmHPj3A/KJT8PyPH8rSbCqDU0qpqQlEUlqA\n/dvIuCjml/AI4tXeFV9r9sTnb2R+/f1Ydz09eSz7u/z62GOanbwklR0bYoa1p+h3ItUawVvK05hS\nfRhynkvn+f5j1G2QrbcZsW/KOsJLWvi6Yh8VfhNKUBz/Bu+lYx+dyD5j5445W7AOfi5DdX2ryVzs\nIpJcMws+Lmkbcd+JBs22sOb7uroco2ZbkzW8u4nvlRqzUGJi5MNPK1NTXQ0JbV83ly7XnsZ7nD2R\nVdJ3PaWUco/A+Ozvx/UX/HqU+fW24vttvPGe1V7E3zVq6zEvRj6Zptk1J1Ge4cjmU+wzc1+eq9mn\nPoQc2zCWvy/6jsO5NpK9sV6qVXMK1x4wAWUwyo5kMj+vEdh7Hn0L8cVM15o7+QH8BhIYzuepUpI5\nIwiCIAiCIAiCIAiCMKjIjzOCIAiCIAiCIAiCIAiDyFW7Ndm5olp9s30dOxZ+K6qKV76BFOu9WVnM\nL5TIfqL98H01zTxtiab87DiOzjS3PjCb+dHU1XGJSCft6UHVdJdwnh59w1Oo2vzh099r9qQE3nlh\nQgxSPO18kXJWdJmngg6figr69kFIfYroimV+FzfhXjjYIO109EO820Jvpy511cRQOc+pdTz1K4mk\neJ4uJGmtDlwCNHooUgKffRQyqTvS0phfxX58hyVJmw+dx7vnmJkhba+/F+litHOQXpri4A/JQG8r\n7llnLU/D7yGdX2iXqL0/H2F+vkTG1U/UfQcv8LTxJddBZmfpjGuakBDH/Ap2ID08ZrIyKbRCu5Uj\nT2Eu24s06vAF+MM9PTwtu+4oxnHRUaTjNp7j3a6olKmFyBcdDTzNzy0ec7H2KNILqVTBd1oY+0zB\nL0hrjLsT6cFmflxuV34Uafe0a1xrHu+OYG6PMbFmd7pmT09MZH5jnkGbkPwfMAf850QwvyP/2arZ\nc981vazpxMeQEY4gXWuUUqr9uzOaXWBEeuXEubrzIPKUNiM6Y3VWtjI3mjJaWYKxEBLJU4ldkxEv\nC7ci5djKFePMWMjlF+eNRs2OKIcs038Wv5+l2yCZo5If+xA+lhpzqtWfERHOpTMlxfAL70EqrYUj\nl3CYW/PxZEo6SLzp1XUsqiHysaC5kHMM6Cr6W1kh3XoIeZ7uw3mHGHNzxGErIpMKmcXHTsFmSCwd\nw/DdbWT+6uWQVGraWYFrcknm8jgne8iQvMZjzS3ZwlPI/clcL9yOWKhTUykLMmcbLxB5ls6PSt/8\nuHLEJKx+EV0fFi+/gR3b+Dyka4tXPqzZZRe4RGL400s0+7ZxkM8t6eKp8v5jsdegae5bjvCODYmP\nosPSpW8g77AkspXRT3M5Qj/pvlZ+CGuXtTvvjOEea9Ds9ccgB3p0+WLmR9c4KkdwDuVp/S4uWNNP\nvgepy41vLWF+Tk5J6lrRXg7pZmW6kR3zHY7Y4eyPsXnp+53Mz5qk01NJgucIHnusrHD99B7pux1m\nX4QEw5dIVcc/f5tmF2xOZ58Jm47UeiPpPDf0sTTm19mAeG8ZTLpRWXGJRPVZyM29kvHdHsO4Vrzi\nCPyonLRHF9dG3MvLEJiak2sxD/zd3NixmLsg86IdI73CRzK/4/9BN8DGNsSzhKAg5uc9BbKGjh24\nzi2ruZxq8gRI4bpLESvfvR9dYeamcvnOhsfRRXT0U1i3Z748n/nteBElI8bPRdebwkNcGhr9MI61\n/BfSmT/O8K5EUV2Qi0dOxbpjZsY7Bx18DbLq+e+ZVtbk54P1pWI7v47QO7AfqybSrYhoHhtOfYY9\nevgkyA9dCnhpCbrHH0LmbEMbfxeY+HCaZpdswN5m7COIz61FjfQjyswSewd/0g2uOZ+/A9MSEW2k\nK1R5MZccR07B/DMnnXOzPuDyT9rpMeV+zDfaqVUppQKSeFwyNbteQjcyfVfloFQsxOa2uJZK3V6g\n4DfEsMApiL3OcXwN8YzDWM35CHHZ/wYuPe3ZgHXt44ch2wsjnUZ7+/keq7cD8SzxXsQK41r+G0V3\nPe57+j7swZd++hTz6+s0anZzGSTCmTv491nuxrkGh5D3Nl3H4lYix1O8IbRSSjJnBEEQBEEQBEEQ\nBEEQBhX5cUYQBEEQBEEQBEEQBGEQkR9nBEEQBEEQBEEQBEEQBpGr1pypz4MetUfX+nRgAFrNYKJD\n8w3jenXa7q4iF7Ut2knbSaWUWjgK+mWXMGhOaf0LpZTK/pi0NguDxrGzAvUWnOI82WfW/wCd+MJJ\nqJWjr3vw1Asfa/Yb/7hHszt07cRoO+WcX9FGa/TTvNBIijd0oMc/Q62JAx9ybeuER9LUtST9B7QR\ni9C1Oj9HWmRPnYLWtG5JOr+10D2Gj4WGsDGL6ysdSL0Dn+GowdNQyFvhdRGdny1pnx2SAP17UxPX\n8nW0GTWb1jIp38HbedPaI8PD4Dcsktc/+XwbNI5zR0Dbu/QeXn9gwzpoQx9ceadmN17i1+7jyTX+\npqStFDrdjI28hXd4CjTUvb3Q4F/4iLeUD12Cugdl5J6F3MJrL21fsUWzR86D3tstkY8Jqv8+moFW\nkbRWiY2u7oHXeOi/s75AK9v+rj7m50NajVq5QKtpmDKB+fX2Qi+84i60tzNuyGF+V349p9l+MyDw\nHBjgGvyunmtb/8nSAiH35Ge8BlLCYmjcfUhL6qZcXjsoeDp07nk/4b531XYwP0sX1EWIvh41o1yi\neHysPY06KQ4OuNesvbyuRti9yxdqNq27cvEbro/2JS2tab2w/O/OMj8XErMdw0lcr+Ya8jAytrqb\nsSZZe/BxRltDK14G529j44Z75DyV150qP4taTv1kfjiF85O4tPMnzbb3dyK2rhZPOWr21Gei7o/T\n7GjmZx+Etpb2vvi+ziqsi/R5KqWUK6ljZTsFtW2ajbyuEx1HK//9nWbfMHw486P33D0Uz7DpPI+T\n+QdRj2Dcs2gxbW3N48ul1TvUtSSc1MMr38rXENqCdcfz32p27ER+3799+RnNfub2mzQ79bFHmV/Z\nJVyL21DskSbHxzO/E29+p9m9fYiJoaQFa/XJYkXJ3IbYFp2K2PbH77wNfTypvfHkK3do9ndv/8b8\nbiN1/kLSUK/v/bv+yfxCvdF+PHIE4vVvz6xhfrOfR4z2N/DaG38XW1+M28CZMeyYmRliwoXPt2u2\nBakXoxSvLePkgVoHeb/zNsk9jahZEbEEdQMtLHi78dADRs32IPuoXS9iHDW1t9OPqMQUxAdP0qI+\n+4N05kfXrhpSm8Y5gddyCJ+G1vMXN6Cls+eoQOZXewoxxTkC++4m3d7GP43vEUwNrclY29LCj3mh\nDlfhJozpb1/iNSHGRWNuWpijHojPZN461z4AsfJ6EsNe+Pxz5kdrozz1yTLNNrfBGl6yhbehn//S\nCs2+sP1LzbZy4/UmaC2PwDTUXWm5wNf6PS9j3E761zTN7v+Y71uqjiOm2pJ6maUH+XocMorfC1My\nhLzr+elaxbeWIAbQ1tXGtdnMj9ZfsyPrIo2FSinlFI6xWvIb5uXCl+Yxv6bLuJ+hd+E+7/4P9v6T\n/8Hf2/K+Qd0RW0/UujGQVtxK8dqK7SUYKy51vD6ORzJq/DUXoW5Nta5t+ugHEVPMbUl9Td21ewzn\nNQNNjcEbscQh0pUdo3vPcysR/yPuTmF+ZbuwnmasQz2pmc/OYn6H31iv2SP+mabZzQW8vo//TMS9\nuYEk3pJpYG7Lf87orMHe0YHU57L24HPx+CGMwTs/wHu/mRlfJzpJfbNe8hvFuAf4O0kX2QfRWoPN\nufyaPJP+pNAMQTJnBEEQBEEQBEEQBEEQBhH5cUYQBEEQBEEQBEEQBGEQuaqsqXIfpCjllTzdzomk\nxrunIO2waB2XE3iMQYpmPEmpLPyFS1YCpiPFh0qZ2kp46lfkHUif8gxGe6ycH5G62Xyet2W94+kF\nmk1bu37/6Qbmt+JmtML0HI3z1rdppe2zQ8m5VmUUMT+avu1shxTbfp2UQt+20NQkRCPFUC/jSA2H\n1MdzNNKeaZtVpZQaOh+t8DI3QJLQQ9p4KqXU5vdPavaCCZWaHXwzTwmksibarvPcr2jJ6RTlobsS\n+JWSVMb+fn5NT755t2b3tUOm8tGrPN36+Tfuwzeb47svbuRj89b7r9PsvG+R8mhYxCUNZpZXnU5/\nC9rubcqLPDW8tQJyweKdOL/hT9/N/AYGcC+OFULy1PQBn2Mjb0Sqr1MI0vfaK7i0pfYYxghN0805\niXGf5MpTCM/sQgvIIA88X1s3LkupP1MBv7lIV2+u4i37rJzx/U0kFbK3icsm84uQvt1L2jBGLuUt\niUOTDepa4umH9ErPcbzFJ5VL9rbiHBuyeDzzSCFprSQN2GcqT1ke6MO8qNyLFve1B7ksIngx5qY7\nka6tfvpnzU4M5r2MrYg0gMrY3MJ5u+bDvyGlNT4C5+cYxtuldlUjFbQxByn1+nTZDpIebmmP2Osa\nw9P6T39ApJyjlEmhksCWcj4nXL2QcltH2mpbOXNJ0RkiRRk+H9foG8NTrB0dMWf7OnFNV/amMz/v\nURhLFlaQejiGYRzR8fU/34fYXboT62KjrmVoSR3+O8Adz9drqA/z80jGPsDGCXP7yibeLtrfHfei\ntxPStOJt+5ifSzyXSJuaCf9CK1kq7VNKqZGzsBdIX/GOZtt48pT1x756UrNpSn53dwPzy/gc8XbK\ni1hPnP2dmZ85aTdP16TQYbdodvbmVewz899Zrtnt7Yi97sN4W/aWQsjVbL0xRkZF8rall3Zjba07\nhvty20sLmd+RTyCpjJiHa8rOyGV++d9gv+D/imllTebWWHOzPzzMjlHpfPQi7F88Irj8qa4A0pRL\nuyEjibplJvOrysZ1ODnh+xobuXwsdBHiaf1prDuTnkdKv/E3vsegbeQbyf7VLYVL/Y6tOabZaY+i\nVfO5b08yv+YcSGpiH4B08NDrvzK/SSuWajYdO226tf7g62gtv+A9nsZvCuJvg3y69mgJO9ZYjH11\nzSXsdW79x/XMzzsJ+7HKM7i/hTv5nqGWSHRTF+Dv7rtnNfNruoh1qHwv2ctH493HaxxfF1tbMXdO\nb0XJgwMXLjC/N37GnC0h7XsDb4xlfp2rMYbPfpKh2f26tsFDzPHv7JmrMRYCY3gMaC0hz/VmZVLa\n6iAjqcngz9ApGutB8e+4R/XNXMLmSPaRp7/HvApN4ve5g8h1O1qwhrQYedy1JrL6XvIuMP4e7Psc\nffl3xz+OOTcwgPuf9cEB5hd5D9ZtK7LPdfDnMkf6ztXfgzWYyruUUqpsJ/YVhoWIIcHJfJ9IW0Rf\nC9o7cD87z1eyY16j8N+2XlgLW4t5O/L6Iqw1056AHK98D2+xHjYNa097Od5DLHXv3NZkn+8UjrFE\n19zVT/D3u6lzsfHLWo+5mPYCX8dKCnBN65b/qNkJIQbmF/sI9gvVmVgz+kjrdaWUcovD+OluxXtb\n0wUuFe1u5/dMj2TOCIIgCIIgCIIgCIIgDCLy44wgCIIgCIIgCIIgCMIgclUdRtR96Dbkft7IjtWR\ndE2PVKTZ51zhKfMBzUh1i70F1bJ90wzMj6Z/2oci9f+P344xvwUvoJNOXSnSpc1IBfXoh7lUobsN\nKXDhpKPLzDbeCaSRVNAf2IhUyIoGnioX64S0WJpW25TN05bSXkDeYEMh0rnsfHjam5kuLcrUlJYi\nTdbPi8sObPyQ3rzvI6SVT7x3PPPrqMQ9TFqIe9h8icvdxj2IlNfSTbiHm17ZzPyGj8A9zN2JNMce\nUpk8NZp3lWnIRkqrXQjSwWsuVDG/Te+jM8Y5o1Gz63RdADatQjeGhcuRIqvvTBNpgd8wrRwh59j/\n0X7mN3EZT1M0Jf6TkOZYtI13uinKRApp6kPoRtbTw8dtXT5SayPHoztEwKQk5mdujnTFI2+s1eyU\nx/i88iNSRDfaqeUCxhutcK6UUrGJkNg5ku4QzhFcwubiiTHW0owU5T6dNKO7GWmD5Tswx9yG8XTw\n6bcjDb32DGJNZxO/RwEzTNzaR0dfJ86/ZDNPt/aZZNBsKum7dJh3krH4CmnQwYuRyk0lXkop1VmD\nORv9AMZFd2sr86Mp9V1NSGkdl4gUa3N7S/oJ1dNG5TawvScYmN8oItOgMiuHUN4FwDEI/33w7b2a\nHVDDz9XcGrGyjUiKLHTn558SoK4VPpMgz+r5nXfr8CVz4uy3SMv2s+DSkbF343kYN5E07xNcsmLl\njmc6/P7HNLvUahPzc3QmUooy3qHjf7F2cWD/TVPAabcrD929vHfxfzR74hjsCa6z4utWbzvkuXmb\nITEJu5V3dao9B5lC7pc4V4+RvAuFZzy/Z6Zmz+tYJ6jEUimlWsZBnk27NdLUeKWUqj6DOewaCxnW\nun9+w/yoHKz8jwLN7q7jXTAT75ij2TW5SMW+vB+dfix1HSx3Pf+hZtMuNSfzeQq5FekUZ/CCDHD2\na7czv9WPf6bZU1++V7M/vPdl5hfpB8nElX3YOyRO5tIMKp8zNbmb8ZyG3j6MHeuoRuxoK0IKuY0b\nl5/7RGMuGkh3pfp63u2wmXR+qQnA9daeLWd+vS2QQlBpnqUl1jvPMVyqUE++I+CGKM22duExfeZY\nSDDqc7Dv8Y3kEkAqBc58F90XA4fr/m4x5JW2nthTdeu6s0bpOmGZmu5GrOMdVXxfTiXwPkkYcz9/\ntJX5Odhg3Zh8HbpvTnrpYeZ3YS2kXXT/7hmZyPzy1kPikPjQaM3+7LHvNPuFdXyeb1qOORLqC9nn\npCemMD8qwXZNgF/W11ye5hOHY97jDJqd/QWX0nWQtTAoFnHUOZbvof2v4f7G2YA13Hu8gR27shZ7\nuJhHIDcx//k88wu8AR23eoi0O2f1GebX24qYHLoQeyAjKXeglFJRSxETin/D/teGdHmz8eDzt3Qr\nYjotaRG+hI8PK9LZsppI/BMen8r8moogDaXSFseZfA9Er729GvtmfRe68++jW2tosjI57mQv3lnB\n5yLtTkYll/p10T0E693l1XhfSXh0DPO7+CnGcTcpkeEzjO8FaFfglOW3ara5OZ7BPR/zDoldHfhM\n+GzIbnN+4OVMwkejtIdHNt5daPcxPcbd2JNH3MhLdpiZ0ZiN2NXXwdfBIjIe/R7nXcaUkswZQRAE\nQRAEQRAEQRCEQUV+nBEEQRAEQRAEQRAEQRhE5McZQRAEQRAEQRAEQRCEQeSqNWd2vAht1qxXbmLH\nXONQY4LWHJh0N2+zZ0taT/qFQ/dVlred+fkMg045by306vOemcP88r6HDtszFfpTWs+geDtvU1hw\n2qjZ3i5og30ij9dysLKE1j4uEG2/PRwdmV/xEbQYH/0MauC4JfD6FXlr0XrN3AZa8M6aduZH9f7e\n16B7aEAA9OVDLIawY+a2GAKRvtAQXlrPtaDxd6VqdsVeaOZrSuuZ3/Ej2Zo95U4yFvKvML9u0jo3\nYhq0lum/oF3g0VW8NWbcFGgvC4/j+/p0bQXrSU2N+SOgPW7t4u2V7a1RPyZ3Ha73+pd4i8Z8UuPD\nMQZayunzZzC/gtXQb4enKpPS2wvtZ+AsrkENnAXNo6Wly5/aSill6YDr9ZuIez4wwLWQtM5M6HTU\nfaBtAJXimv6qP4yaTds62gXyuVNwCn4zb8fc6evjGvf9Kz7W7OSHoFO18+KtZ/v7EHtcEzF5qnWt\n4GlrPscQaH2NP/JxbheKe+ZzhzI5NA74z+Fa1eqDRs0uL0A9ARc73mbcMRq1CwbI0D/4xm7mF5WG\n2gWuQdBl1xVxXXtrIeJWD2kzfvYS5vmwRF7/Y9sq6Pvv+vBBzW6rrWB+jqE4Vzp+rmzm2nB7J1xj\nCqlR0tPC52zVcejDnRMQ1zpzuDbaLYm3eTYltp6oGRa+lNe56G6CxtjFHmtfexlvV28fiHGc8Hia\nZtN290opVZ+N+9nTg+9ovMjrm9l7o+5UWxnqD3gnJWh2cyWPwYGjUUOqsxPzJXMlr6X140srNNs5\nkawlZnwtqSDt2oPmIUbV5fC2qq6kBbcZqVtjpauvca2JH4cY2Kmrc/HD419odrgPztdDp4XfsQL1\nPNK80zSb7h+UUip5OVphF+5CvRI7A68/V30R+nza+jpgarxmF2/PZJ+xs0Js80rAnmjY2fP0BwAA\nIABJREFUI2OZn4Ut9jeFaxH33r/3beZ3z6uLNdu4O12z732P16bJJ/WCig7j2Z8r4jVdxkRFqWtF\n0gOoBaJIy1qllPJIRN2phsuIp9ZOfE2qLz+l2dWkjXNvSzfzs/bA+OwgtbCCJ/FabGZmWGdrr2Dv\nkPPJTs1uaubjLfH+kZqd9QVvPU/xH4WaMUNILTzagl0ppU68k67ZY55FPYOGAh4Dekk9oPzvcB98\nZ4QxP1r35VpQcwC1KgPm8LWm+hDGU14+4hRto6uUUmOGYY2LnDdbs89/+RPz8xiNceESinnaWMbX\npDH/Rm2Lrx9+R7OnJSCm0pislFLjlqO2TOZHRzT71OcZzC/hVqwbtm7YjxzL5W3op9tjXdySjlgz\n5zn+XvT5Uz9otpsD6qncvfAR5mfchhqe/gZlUvymYsz0dfM9ecD1eKaXPiF1Rnr4etdehVorB75G\nzScnW742xIxH7aXMH/h+hkLrzFSUoGaURyP2FZ4jeaz2nmTQbEc/xH5zcx436Hqa+vRtml1yjL+3\n1B5CTLENQrzv7+X76aY8nF/BLtS98Ynme5noe3kNN1Njb8Ae2D6Yv0MM9CPG0pigXxfzd+P8xz+3\nSLNba3lN2l5SYzRgEupRduvijXMc6uDQ9xUzMxvNtrHhdQapX2M11rvg+bwm2v7XEJenv4w6seff\n5/vp6nO4pqR/YG2tOcnfNSzssNZYkPfrsjK+Z4uedPV1UTJnBEEQBEEQBEEQBEEQBhH5cUYQBEEQ\nBEEQBEEQBGEQuaqsaeg4Kn3o+ku/rnpIVBqzeFtj/zsWaPbpVZAq2AXwFDF7T7R8o23STn95lPm5\nE4mR3zikF554Gyl/lY2N7DMzX0SKY/p/kKp0672zmJ8DaQV3jqTKBc3m6Ue5GyHd2f/Kes22IzIZ\npZRy80NKGJUzWDpxP306rqmx9iHp9UU8DdPaC6mhzvF4BuG6VD/afrjciHZjtbr21Atema/ZtNXt\nuDk8Fc/WD8+xbDvkZbYkRXvso7w1dW8HUiA9ziI98NBFno66lKRlV6UbNdttuB/z623DOOskMqtu\nXTqzbTAkCAVHkb6tf2xUVmNq2svx3Kg8SSneXtrCjrQeduUnaE7azbdVoZXjJV2bwvHP36nZNjZI\nV7y4aTXzo+3rYx7Es6o6jedZls7TqOPnox1h5spfNDvyAa4Di5iD1MP2Sowx+xjeCrT8NM7dwh5j\nJ+nJmczv+Ntou5lCUhK9p4VyvzVIuR12DWRNVaW47w5GN3bMKQapm95paNdcsoGPb9ehGGd1ZF4m\n38LnWFsJkcGUowViewmPATQeuSbguwNLcK6eJI1YKaVGdmHMVZ5G6rCVsw3zcwrBNWW+hxaQYfPj\nmF8jaTFZcwRpwK7JPKXX/wakR9O02rIjRubno2vpbUp6O7EWlu7gaeg2Xoi17iQeNF+uY34eibif\nJ97Zo9kBqTzutpdh7HcOw7P2TOUpvLVZaJts7Y5UeOMupNN7DOepx+c+QkvZmPsnabb3CP7d3mNw\nriVbMY7qSBq2UkoZZmO/ULYH59PbzPcOuVsxXhxtMF48R/Nrr9gDWd2Yp7hExxR0ViJWuqfyezOS\nxNS2ZoyzU+8eYH7hpF2uoz9sC+dC5rf9ObSnHiALx9QVC5hfwTpIWoYtgyShvx/30CGUp4Zv2YQ0\neo/KSs1ObeCp4bb+WHNPnUWKNpVtKaVU7hrIpgImIz5W/MGvKfXZ+zWbyuIcV/IWx7Q1tKkZ6IV8\nwtqVSx92v4Q94fhH0jSb3kullKoishka/yp28+ul6f4OgdgrXv55J/OzJlJ+GzIXSyoR48Y8OJ59\nhkqBkx+HTEofX+i8rz6KcWBhz2VNUfMhg6NSprwN2czPi7R/LzQivkT4jWJ+Q8z4XDc1VCJ56kcu\n6wqNQ1yY8zrkI9kf7GF+sfdizT+/ap1mB+rGn5kF9uL9/ZBT03bPSill64v7dtOLkIY15SGW0zb2\nSnFJfXxyOL7Lj7/vuIRgH/PMjS9oNm13r5RS3d3Y896wAjLwmuNcKjp/HvZfmzdDDpS5chvzc47g\new5TUnUY88h/BpemGcm9NSNjPWgafzbmROY6+cE0/H8bPr43vokYM+VmrA22Pg7ML4+UK/ALgSSX\nynP2vc3lK76umNsJj+G5HfkPj2tRc7CHOb8Ra6kj2ccppVRLJ8ZYex5iT1MhLwlB1wUXR1xHwTku\nEw2Yee3aoSulVFcd1g2/NN3cITKifeTd1/98NfOzNMdzzHoP9y32H5OZ37DlkOeVHcGzMszk8ce4\nC/vyhpIczd7/ISTY89+6n32Gyktpq29bZy/mN/l5xA17e0jzhv9rMfO7+AOuo3wf1oawWxKYX80x\nzE0LB7yTpC7lbcTbK5rV1ZDMGUEQBEEQBEEQBEEQhEFEfpwRBEEQBEEQBEEQBEEYRIYMDPy1puby\nwW812yWKpwJlf4h06cY2VJ4f+8xU5kfTgKNvQvqPPr2SMvJpSGP6+rhsJuNNpJCW1iG98DoiY6jS\nSSmcopFmVrwP6dY+KTyV+eIhpJAOX4IuP3Y+PCVx75u7NDs8BN/hPTmE+TVfRioo7YKilzVdyMDf\nXfLpp8rUlOQi/ez7535hx+KDkF4ZfzsqyOsr4VNJzJoX8X13vMlTv9pIV5Izv6Cbg5cz77JjT9I8\ns8/imUQG4X6G35PCPrP2aaSqxpNuGHEPjGR+vaR7GJU+6FN/t6/Ec0wIMeCAbkqE3ZOM7ybSqswv\njzO/zm7IoRZ9yNNd/y6lBb9pdlcjT1evP4uOLj5EDlN9hKdDFmci3S5qFmRDzZd4yjJNwTVMQ7ps\n6RHecaCzhnec+F9oinLQLN7Nho4rc3OkbnZ2lvFz3XThTz9DpVRKKeWWiJR8e19X8hkr5tfZiBRS\nC1sc62njKe5730Wq9D1ffKFMTf4JSMNohzmllHIIRNp8/reQa9U38hgYPAIyk7LTkBMkLB3B/K6Q\nTlQ0tTZkGk+LpZ1quqph2wVCOqhfJaj0lEI7zymlVJsRElPPsYg1FbsKmJ/vdMgnWgrwrIpPcglH\nCuncVbIRci//2TyNuo/IrkISeIz6u5Rd+R3nsOUSOxYwB2nAdP416OZY4hO00xukGZ1NXP7USeJX\nbQbmL/07SvEuUbSLC03hP/Y57yIRPQH3zD0Zkk8qk/yf08P5NeXiOmx9+bpYshXrmM9EIoPTjR26\nlliStF9zWz5n20oxdmKn3adMzQ8PosvYsOuT2LFWMm5jSVe5U2+tYX5jXnhMs8tykB7fpZMUZf4O\nqdDUFZBIfPuPr5lfBJEYdZD1ZOF7r2n24VfeZZ8Z9e9lmn3+S5zfsIceZn611ema7eyKNa0oYxfz\nCx4zTbOr8hHzfSK4FCfjNUi1osha7e7H1+Ojr3+u2ZNfe02ZkryjP/zlsbqTWFOqijBuaXcrpZSK\nXYa4eeC/6EIX5M9lypYupNsh6Uzj5M3jaU0uJBy+sYhXVCLc1sY7hbY2YA9EpUzNxVyiH7IA67az\nAfFU33GxeAc6R9LOcPq9Z/kWLpvSvjuJX7vPGOwrvLxm6d3/NpWVRDKwh98baw/IxDoqsBZaunAJ\nbchU7FVKDqNT0rltvCOjK+lm5EpLD+j2h15jcH+PfgapUOxkyDfbrvDn09eO53COdCgtq+cSFg8n\nrK2hXni3yi7hcqXFyxF7rEk3OyrLUUopZ1/E8n0vfYdr8HRlfq1NWN9nvvWWMiUnVqHrW28r78JE\nJZWtl3EvLF35eOwna48LkQX367o/OUeia+qlb/CeEbmEx/EOsp8p2Y1xFXEb/GgXO6WUUuTVh8rH\n9HubjgpI2JxjPNVfYe+HZ53/DfZ11p78+7qqUFrBNgBj1F0nR7b3xbV7eU3/y7/7/8vhN17RbDpe\nlFKqk3TXomU8+Nsi93Mi3UYjl3Hp/aYXN2r2gjfR1cnKiscfc3PM9YOv4neJ0JnYB1GpkVJKRd6J\nNY7KRve9w2VsU5/GXsy4Dr9LtDfyrsop/0R5lJ5udDgt2XaZ+RVnY08evxDj7Nga/r5I1/f7v+b7\nAKUkc0YQBEEQBEEQBEEQBGFQkR9nBEEQBEEQBEEQBEEQBhH5cUYQBEEQBEEQBEEQBGEQuWor7e4m\n1CnoqOZ1D9zioQmzvAA9b9GGHOY37DHolOtz0OaRttpSSil38n2Fm6BzLjhrZH7TXrpFs3t6oM+n\nGm+fSbz2i6MfNHu0vkZeBq97YE/aep5cDX1Y0jyuYxy9BG2+XCKh1W+r5LpS42nUHIi/Gfq3i+u5\nBjZxZry6lpz+HPdz0RNz2LG+Tmhkqw/ifHOyuH4veSLaxk0ai/ux7XXeqi8pFdrXlMXQF7bptNMD\nREI6/i60juwjNV2Mv/K6RIHu0Frmk5ahVl/zVtCe41GPpr0E7cr8poQxP9q2+0olWsCnLuR1UmjL\nyvZSzAMbS65VdXd3UteKU5+gXkTEtGh2jLba62vD/aPPVimlwifh2XilkJoVOsGouRXCAm3BTNvC\nKaXUqQ1oeRntjznmdx1aSJb9cY59hra7PrX5rGZPfDiN+RlzoNuk9YrsA/k93kXaaS548ybNHhjg\ntWSKfkUNm8D5MZpdrmtV6ubAWzGaGltPfH+TrhXxlZ8QF7ynIIZ5tHP9dv1p1BiicbRfV8PGcwI0\n8+5Ey122n89tzyRfze5twd+ibZzdR3Dd84X1eK4py0ZrNq13opRSTmFo3dlBahQ5RnAtfMVOxOKK\nGsRR2npdKd4SsbQI7RubvuFtCameN+Q909acoXr/Zl07RBa/ziBuDL2RX0flMdJOOgT3wsyaL8nm\n1ni+obdgDWnMrWR+F39HnYvQNMy/KwdwX6PH89oYLkQnb+kA/bi1GZ/neT9gnleVYsya69ZwDy8X\n9WfYB/L/31oMvbYdqVtD9xtKcZ34tWDa86idsWXFZnZsxhOou1Keib1A/OO8pl5nJ+aiIrWxwsbz\nFtl5OxBHL69CPYw737+N+e15ebtmD52ION/VheftMcyXfebi+g2anZ2NuW2o4W2/HZzw/LNXr8X/\nD+Vzcf1Tb2p24hSs+/3d6cwv4fE0zT72FurWTH6Z14qLvo/XGTAlRaTmU1cvX+8SH8A+ze481pDm\nC7q20GT9ix6Ne9RubGJu/jNwrCEH+wXaWlkppdquYHxnfPOOZo9bhr1wQ1YV+4wr2f+GLsR5n3+f\n10doL0O8aSvBnHeO5jUhy8+j3k7TMdTamPo8rxcTuBA1bDqqEO+7m/j62VFD4hz/Uybhw2Wo7zb/\nhgnsWN4ePONYEkddInhdCjpH3Ek9yVmj+X7uk2WotXLfk3dqdn8Pr7XVSOprGcKxzw+Zihhw8FVe\nKyLhPtQvCrPFPjnrk2PML/4RrJnfPYk6UQuW8Roi2z7G/saMxJfJN/O2vNUZe//Ur6mxlfnF3Mrf\nZUxJVwXW95DbeXvhy6TWSlsXxtbQubxWnAVpmd1wAeu7fQDf99Haj919eG4XfuDvAu6heGcImIZ1\n8fKP2Hs6evA9nyOpZ1OfjXMIXTSU+dl4ohZS+VbMsYC5vB5cZx1ql1g4Ym31nRzK/HpacV9qjmL/\na+vNa7tVHsHf8ppv+pozDuFYD3qz+N4zjozvPW8j5s/4N48rhd+hxpoveR/I172rWZN3qOIteN+z\ncOR1p2h9O/re5hGPffKxX06wz4S0YQ/YUYl5EBbkx/zOfor3YwfyG4CNDa+HVLQTtY28Sf1EWmNT\nKaXCx+F6rZzxfSE+PF61tfO6dHokc0YQBEEQBEEQBEEQBGEQkR9nBEEQBEEQBEEQBEEQBpGrttIW\nBEEQBEEQBEEQBEEQri2SOSMIgiAIgiAIgiAIgjCIyI8zgiAIgiAIgiAIgiAIg4j8OCMIgiAIgiAI\ngiAIgjCIyI8zgiAIgiAIgiAIgiAIg4j8OCMIgiAIgiAIgiAIgjCIyI8zgiAIgiAIgiAIgiAIg4j8\nOCMIgiAIgiAIgiAIgjCIyI8zgiAIgiAIgiAIgiAIg4j8OCMIgiAIgiAIgiAIgjCIyI8zgiAIgiAI\ngiAIgiAIg4j8OCMIgiAIgiAIgiAIgjCIyI8zgiAIgiAIgiAIgiAIg4j8OCMIgiAIgiAIgiAIgjCI\nyI8zgiAIgiAIgiAIgiAIg4j8OCMIgiAIgiAIgiAIgjCIyI8zgiAIgiAIgiAIgiAIg4j8OCMIgiAI\ngiAIgiAIgjCIWFztoDHrZ82283Zjx+qySzS7t61bs3N2X2B+QQYfzR7o7tNs/+ujmJ+Nu51mt5Y2\nabaDvzPzK1idqdnWnviM35QwzW6+Us8+k/X7Oc1OWJiMv+lmy/ws7K00u/ZkqWbbBfBz6G3H9Vo5\n22h2X1cf8xvo69dst5hAze5pb2V+PeT+BccuUqamunq3Zpub27FjDflXNNu46aJmRy8dzr9kYEAz\nK/YVarZLvDdzK956WbN90wya3V7SzPysXHHfbH0dNdsx2FWzq44Usc+Y21pqdt3xMs0OmMvHEr3v\n1m643pwvTzA/72R/nIOPg2b7JCUyv+yPt2q24ZahOB9rS+ZXl1mu2UPnPKBMycX9X2t2TTq/L1EP\nj9TsMysPanZRTQ3zm/bQZM2u2I1nuP3YKeY3OjLyT8+horGR/few8bGa7Zbkq9n1mRWaHTCTf9f6\np9drtr8bYkp+ZSXzm710imbT2HDo84PM73I57vmDK+/U7OwvjjM/MzP8Dp3y1HTN/nX5D8yvrx9j\n55Hvv1em5qv77tPs+ORwduzUCcy/4akxmh1wHb+HjRerNburvkOzh5gPYX6153FPS+rqNHv0zSOY\nn5UL4qAFmWP9vYhnpZsus8/U1iNGByUhtrmn+DG/jW9vw98djbnTWNzA/AImhGj2jh8PaPaCf81h\nfsb1WF8GSExyifJgfj0NnZo96rFnlSnZ99xzmh3zAL+XQ8gjOPpuumabm/F/BxnxxATNrjmFWNbf\n1cv86Jp0aAPG9HX/uo75tZfheVSkGzXbIcBJsx0j3dlnLm/N0eyMy3i+D799B/M789UxzQ5KDtJs\nGjOVUso1FmtBRTriy/p1+5nf/W8u0ezO2jbN/vqt9czvhtG4t+OeX6FMzeZ//lOzUx4aw45tfHkT\n/vZsrIUusV7Mb/0b8JuzbKpmWzlZMz9rV8Sw+vOIj5ZONsyvrxvPv82IeGvjjXvd29LFPtNZ3a7Z\nTdVYZ0Nm8nXxwuYsfEcf5vb4f01lfnte36HZwxekaLZzOJ9jO9/YrtmTH8HaUr4zn/k112O/M+ut\nt5QpOfjii5p9pbqaHQsPRizym40YemnNWebnGYVn6pGKPUHtiVLmR8KNGmKO+Zx3upD5jbh7tGZ3\nVOLah5ghODiF8f108QbEtYAb8NyOrTrC/AzROD8Hg4tmO0d5Mr/cb89odk0zxkTyohTmV7gFa07I\nHKw5mb+eYX6hCZj3qUv/qUzN5YPfanb1fiM7VlGPtWLcUxhnVvYuzK/8MO5hy8VazXZL5WvSoZ8R\nz6Y9in1Gb0cP8+usQWxyjUNs2/kfzI8Zy2ewz+z5L/ba3i44v+b2duYXPTZCs6+cMGq2vTWPG8Ez\nMG7PbsAzSbgunvm15OOdx3uiAd/ny2N+4drTmm3qdfFS+jea3V7Wwg+SyWPpjGuszChmblF3YXwe\n/OAPzQ418Gd4IQ974NHzEZ+v/MFjT/Qi7OUv/IJ3x7ApuK/ZO7LZZ2KnYR64DcX7a0X6FeZn7YbY\nXXAAf7els5P5hZFzp+8mBcUVzM/WCmv90OvxfMv28/gSMB37xui0e5SpWbkE6/P85bPZsR6y9jiR\n9eDc+4eZ35kruFcpIdjblTfwfd+EZdgH2ZE17spPWczPzoB3cO+xwZpt/AV+DmGu7DNNOYgBGw5m\n4G/GxDC/2AUYI23F2Ed5jvBnfpUHjJrdQtZmv6mhzK8hE/vuxgp8X9KjfI9xeRXeu9JefVXpkcwZ\nQRAEQRAEQRAEQRCEQUR+nBEEQRAEQRAEQRAEQRhEripruvQj0j87e3jK37h/36DZxk1Iz5n+8q3M\nr7cX6T+luy5p9vnvTjK/qOvjNJtKXg69tZf5hSQghd7WH3KYKz8jvcltuC/7DE3h9YxDOqFxOz+H\nwBlIu++sQRqiXpLjTtKdSki6v40nlwx5jAzQ7DPv4jpsSPqaUko1tCL1NXil6WVNxVtxbzxHB7Fj\n7pFI8apyRCpa2Y485hdGpBBd1UgDrNzNU+6Snpyp2eUZ8PMYEcD8Sn5HOm03kSCYWeD3QktdajiV\nbUQ/jPNpvMzlOzStrLUS6ZUDimMfiFQ5jxikEtflX1R/xblVkBZEzI1jx8ztLPXuJiNvO84paelI\ndmyASHECxho02/6ULrWeSLzcUjFH/nHPMuZnXIfxknsRaacRPj7Mr7cFcrzP/v2jZl8/IlWz01/f\nzT4zcgLSNb3GYCx6bctlfuZW5ppdsRdjrLuXyz5aSQqpuTU+E70kmfnZuNtrds15fJ+DLZc2TnmW\npymbmtgopGRmn+UpuDMfgbzg5PcYZz6TedpkVQYkpfZEWtJeyeWS54vx7CJ98bwr/zAyP2sirQic\nF63ZXfWIgSWVfI7FTULcKDhSoNk2nvbMz46kadP57OjGJTEWjjiWNg1pyvr44jvJoNl7fzik2TNm\nc+lX+jc4NkqZFioXdN5bwI71EYkqlciV1XOprd0nSK0PnoN7vmPVPuYXF4C4ef3z12t2awmXGB5d\nB8nmmFtxxT1NmB+bv9jDPrPkzZs1228n5vYQC/5vNn7hOGbpiLXr58+2Mz+alh1LztvLmcuCO+sw\nrqxcMPbmTRrN/Ky9+FgyNWVE6pdqxbdC1z2BONDdiHvYUlDH/MaNRDwzt8Z30LRnpZSy8cR4H+jH\nSmRG4pxSSu37HrLNxEjM+94mjCuXZC4l9pmItPEqIhPY/jWXk8X6Y99S04J1saWIp5o72uCZuERC\nLpP9OZeKhpOYUkTkhu66/VfhLp6+b0o8xpH9YLETO0bXl+Z8PDfD9Ajml7Xp/J9+t7UH3891VCC+\nUvlTd027zg/3tr0Ue0enaMgAGnKq2Gd8puFZ1xKZY4A/lyu1luH7crMh7XDez881+QHMJW8yZu18\n+T0KmIC/SyX6VuZ8XHZV82s0NbZE0hD1EI8DHtm4H5Z2OP/eXr7eHdsK2c+MJ6ZpNn32Sik1Zj72\nJ61FmKf2AfzeGA8gttefxBiOCcWY6+/ju8rZr+K9qI7IF81t+N7Qikh7vI2QPlD5hlJKuQ81aPbA\nekiSaAxRio+t3R/iXUMvk/Jz43I6U3L+N8iGfN24xMT/BqzP/b1YF/sH+HXUncU9i0rB2Dx3nMuq\nrSwQa43peE5xS3SyvXV4B+kn63HhPuw3DZFcMtXfA79mMnfKc8qZn28k4nA9eYej56aUUja+WMcc\niJzRrZ3/XVoehD5fW2e+R73WjE7B3m77e7vYsRvfuFGzzc0xtpKeGM/8QgqIRPJnjNu4lDDmR+Xy\ndkFEgh3Fx2nDabzT0Xuzfg/kVIl5BvaZ1Juwj3xkOqRaPeS9RSmlXKOwv6HvEEW/5DC/iPvwfR3V\niPG/vbWV+U29EfGrqhjSKipfV0qpuhad9E+HZM4IgiAIgiAIgiAIgiAMIvLjjCAIgiAIgiAIgiAI\nwiAiP84IgiAIgiAIgiAIgiAMIletORNz5zDNdvGPZsfOf/ibZtuHoGVcWz3X5bWQtta5J1A/IHYy\n/77LW6DvChqBugyR47k+OP8w6jT4t0HzF3c/Wq6e/e8G9plpL6GOy5Ut0E1X5vD2vb5p0G7Tujf+\nU3jrrbMroeXuJTpG1wBek8MlDNrU5CehCa04yOsUmOde9TH8bfyJxtpcV++mowXtIsPvwfPOfO8Q\n8zM3h27S/3pS32EIb9/bVot7WkH0792k5a9SSjU2QaNpuBm1fno7UVOEts5WSikLW9ynDtLmsK2I\n6/v9SevhRqLt7m7iLUgdgjBuay9C+6hvg+qVhvFofgK6QXs/rlEu203q9ExWJiV6Pmob/Pzyb+zY\nxLFoBWfMxfwb/xQ/id9XbNTsQA+MR7fjfM7uOwsN/pTkBM3W10fwmYT50rUFbQ8/3Ir2yTEBvNZQ\nLWnr6XgScznYk2vrz/wCnWpQMGnRq2vF98IatIPc8txazZ75Im8BuOrR7zR7wR1onzl22Tjmt/0V\n6EeXfjlXmRpbomu3M/JxlvMzNNv+XmiBuXclr9sz7Z9oBd5RhXlUeYXXhfElrTyH3oG5/fPrG5lf\nXCDiVOH7iG0j70DtEkMo10fTFs9HLqGWmKcb18yn3QMt8pXNqJtEW3srpVTaRMwxRWJqXzuvdUbr\neniTWiZHvs9gfkmpf94O3hSkPZim2Zk/8Lpl0bPQXj6E3KNL27je+GQB1oBoLzybMeN4i1Sv8QbN\nrs8itbQK+Dxo7kB87axGbMxOxz2f+8B09plLX6JWXHsXYuOZE5eY38QlYzWbtrWfPZVX8zl7GjG0\nhZyP/pq+eOMXzR4XjX1A9GxewyvjF6zVw3h3b5Mw92U6v3ntg5Y8jM/ju85pdsrYWOZHW6M2nMPz\n8Z5gYH7t5Yh7ZcewLhbX1jK/lKQ/H7chi3APC1ZnsmMeiZg7mX9gH+Vix+uQRN6MWD7UBXUMLHT1\nMFIfQUzsJq1T9fW+zheh5snIGJw3jQ1KKeWsOw9TQltSd+hqbtWdxpxzH4YaMQU/8RozEaNRByH3\nKOZl6t18fNuT/ULDeTxr53jeXj1/L+ZBOGnZS2un0XpgSik171nUkzq6H+NtWDJvh25ti3s7ZgHm\nZX9PH/M78xnioW8E1s/eNh5PmzKxP7IhNXbCJvJ9d1ftta05Q+tt1BaXsGO0lXDmb6iD2dPHr5nW\nmak+iu+ouszr+4xYDr+8r1H7i8bN//FLw/edwPc1Z2Odbc7la26rJfZITuFYw+k8Ukqpg58e0Oyo\nWMxfp0jerr5kN+r/TV2Bd5yedt6u+fxnuI4Fb96i2Z0NfG/c18XnsCkJH4l51HzjC9zJAAAgAElE\nQVSJx7U2Unupqw5rQ9DUcO5H9vLuw8ieQ1dzZtTtmJutBXjHrNPV9Qi5EfG69iSO0XqgZuY8R6Gb\n1GnLWo9YG3MdX5+qD2EOR0eivlVfG7/Hx/cj3rifRJ1UumYrpdT429Fq+co2rMEh1/F35aKdqJcT\nnaZMjg9pDZ1E6u8opVThWlxLdRnWyKjr+LpIW8/Hz8W6c15X3yvlVtRxqSPvVv3dfG77kXfOL17C\nPv/WxZjL/bpz/eC1NZq9eCxiZfBN/Dle/Bix0ikGc5a+o+o5R2rmLnp5PjvWkI14EzGN1HCs5fHF\nkBKsroZkzgiCIAiCIAiCIAiCIAwi8uOMIAiCIAiCIAiCIAjCIHJVPY2jD0nV6uNtn6zckRZblglp\nzP9tf4zff6LGIlXSg6SZKsVTAKlkpbOKpxYl3ooUcNcwAz7vhNSp0Ft5G+jyQ0gNtPVFWpmfNb/8\nTx/5VrMnxEDK1FnBz6GhDednRmQ9zrqUxIY8pL21k/aKzlHcT3/PTE3taTyflnyeDm8gKV6XP0Ea\neeQticyvOgvp0s7hOP+yvbwdsFMknqP/BMhe9OmUnkHwo63R6s9CYtOUx9vP+k7G9xX8QlJ/H+Nt\n3Kh0yyUGKcf2HnzMUQkebSPZeKGa+ZWexHN0dcP4qT7O029pW1hT4xqJc7/rA57jX3EYab8GM/id\n/JBL05xIerl/Avx6GniK7LBQpDXmleAeeTpxGdf9Ex/+03NNjCDt6qv5vaRSiB37MN7SHuMSrH3P\n/qTZox9Amv3F9bxtXf5PRzU79Xq0z646bGR+dD7npyM+xN2UxPySxnAJo6mhMp04nVzy4Ea0Q469\nCfMv0I6ntVbsw/Muy0N6/bBlY5jf1rcgL7MmMoZQb96K19wMMbqcyMYq9+Dv+M7k6cc0zf3eJxbg\n/+vki1Qt4uSFuWND5G1KKbXzU7T/nDBvhGZbOHCJBI2VTqQNul5y4RDKW3makpLfkXIcOiqEHaPt\npWeOQ8quh6Mj85uwCO0Wv1iONvQPfXwP/2NENXpxP/5ujE4WHFKOOEdjcKIt5DD1p7h8cfRzyzQ7\n47VVmn39i9czP9r6NPNzzDdrSy6HSYhDWrutH1rjdpTz9fORtxC/jnyOGHVxG29d6alrwW1yyNjs\n1rXXdIzAPRzvjmdloZPalm1HLLE2w8Mq25rL/OxDIYlxsMO4HTGNr7O9rTiPkhykeRe/iTboIbFc\nKlpxGOcw9k7EgMqdXD5t54P4nfclZKMe4/j39XUipbyLtD0Pv47Hq7ABjEEqY7B05PuZmBsT1LVi\n3zu4L76ufM6H34yx39MKWUlrJ1/v8vYi1X7kDKwHBeuymB+VoMXFY43Uy329/CG12vvzEc3u7Maz\nHR3J5WsZn0DmYvDCXHZN4lL54z9jjbA6iXHUqZtjjiQ2mhM5uIU9H79DyJjN+hmtqN118WqI5bX9\nd1y657f1cmDH+ojMNSgQa1fUvXzf11KGNsyGuXiONsf4+0Ahae3rNgLSmfL9V5hf7heQfVqS1tel\nFRgHYV5csudOWqxnfw3pQ1ENlz/FRxg0u6ce47H+NI/RrsloS99sxN91DuHt6ocuRXvwpiu4D3s+\n28/8xs6Fn+Jq078PeU6O4XwudjdgX0Dny4n1p5hfF1nHLU/guU15dArza7qIfSXdI/S2833A+new\nXxw3DO86xRsg961s5NIvQzjGRBaRHyY48jbdjeQ9MImUZjj91VHm50bmUhuRDwd58PfAP77DWkj3\nNhG6PZBL8LXb2yilVPon6Zrtam/Pjg17EOuL1WHcmwtbs5mf9zGsXZuPImbp9wwRedg/OQ9F3Mvb\nfpH5+ZN988xk7PNPHsDfvf2j59lnlpLYZheItc/Wk1/T5XLMuVEpiLcFVVwOWf7c75od5YcxYmbJ\n4z8t9UGlTFXZvIwKlWUOU/8XyZwRBEEQBEEQBEEQBEEYROTHGUEQBEEQBEEQBEEQhEHkqrImMzOk\nU1We5R0COiuRruPqgrQtlxjedWUISZnP/gIyhuDpqczv7MdIGZryylOa3dbG04MHBpAKVHn8gmaX\ndiI1NWLmHPaZ4g2QK7kNRzqSYcZI5nerF9Kdekl19eNbzzI/A+kscyIfsp6LK9Yxv5mTkZ4fNBcp\nwBbWPK2qt4NX0Dc13Y1ImwxewKtqd5K0q/B7kbbXWc+r83sORRpuVweqdNMOSkopdfR7pPRNemoq\nPtPA5Q4tF5GiSdMcLUhKtM9EXs3axgP3zc0bKe8V6YXqrzBch3FWk827kLB0NCIfCJs1jfm5xiO9\nuaUA127twVNam3K4hMeUFG3G/Au+gUtxWon8y382npNbCk99bcmH35VTRs2m6ZlKKWVnjWcwNBUS\npdIcXgn/p29e1ezc/aim7+2FtO4Pft/CPuNLKsEPo92FeNMvteAWyJzqs5Be+O6GfzO/jirIBT3C\ncV/SX/mW+Xl4QVbQ1dSk2RfWn2N++ZVIPRx+tzI5VJbjEOzCjqUtQsro/i+R5j72phHMj1ayH/3U\nJM0eGOAdZ6beM1Gzz3yE9PoR945mfh6hSBOdQP5/ZQ7SUbPXnlGU8MkYZ+e2Yn6ERHDpIO181kZi\njb6DS9wExMdtPx/U7OnX8RhtXAfpSwuRJ4y6i19TRwWX4ZoS56GI/2a6dP9FS2dqtp0/1sX+Dp5u\nbfwD68YQkgZ7+gMuRRx6J6RRk56fpdmle/i66BOKlOCz3+G50ZRtP53s48qBXZpt64w06vK9XA7T\nasR31LbgvsbEGZhfCJGRFHyPeBV1bxr/viqkEVuYIwaHjODf5zOeS8ZMTWcdxmNXI5e6XNiMMR01\nDXKekj1cxktTk60V7mGBkcsTGnOQon+xFDLjB5NuZH7VmURqa4G11cEGktlde0+wz9BnsuQmrF1Z\nuo5AtttJancQ7P5u3uVi+4/pmr34JUgWW4xcEk3XHZo23qPrTNPTyiVjpmTiw2marZcj03kQNBTS\nreBRBuYX7QNJQtE2rGOGuVzGtfuVnzU7ZQrGOt0LK8VlFpNvhIzX1gfxwLiJp+1HjYJs1NoTsZHK\njpRSKiEN+ze6b8o4wiVYE6ZjL+c+HNde8jv/u86JiBsNR8h86OF70oCJYepacuRXPKt5b/A5kfQY\nZM1VGegQ1mQsZX49RJrYVooYZm7D96hhtyCmWllBJmUfwGWU+1ZCajvjTsRegwXGy+5Xt7PPxJAl\nOJZ0u/XN4pKG/l44upJuX2aW/Fzp/pqOhZ5OPuYKV+P9p57Eg1lPzWR+5tY6CYYJcU2AJKRsG5eS\nOcdjzWw8g/3c+PsnMD/6PtJWhH3a1nd3ML/hpIvZlTzsSyOH83Galoa9jZkF1upzORgfrg5cRjfE\nHPc52p/I/3VxLfEu7MsOEimQQdd5NGgR5FSdRL5HO80ppVQHWfvDp+L6aPcopZTym84l5qZm1E14\nZ/JINLBjx9/GnmHEcuzRd27nUq64eZCyPrwAcbTyDy4dtCSlIAp3IvZeKuPvGnS/6EQk6zc/dYNm\nDxnCx/bwe/A7QnMz9o2X1/GxNGcFZNz7/oPru23lUuZX8AuusaUMY7O7mY8LWt4iYxNke+MX8+5/\nnkkGdTUkc0YQBEEQBEEQBEEQBGEQkR9nBEEQBEEQBEEQBEEQBhH5cUYQBEEQBEEQBEEQBGEQuWrN\nmZIDxzS7p5VrUEPvQAvIox+iPoJHKW+ROkDaqw1dRmsncJ2zRzBaV5799CvNtnS1ZX7h81FjwY5o\nRM1pe7b/fMU+40I0nXGz0D60qYnXm3AOwzm0leM63HWaxPjHxmq29zHoefv7eM0HG1KTpI/UiTjz\n4W7mFz772rbv9Z2EOh8F3/D6ORH3Q39LdfcNWbyNGK0dQTV2+jaSybOgNWwlGnUbL15np7AEGlyL\nnfhuZ1Kz6MhPXMc4mtTe6G2Gvth3Ma9NkPcFWiW2pEC7qK/t4xKImh/mpEVq1qr1zM+H1ElpLcA1\n1Rzjmmf7QN5q2pRYuWEedNRx7X91JbT/NqdxvV6jg5ifwyRcrwtpW9ej00zWkTZ4ATNRW8R9mB/z\nK9+B+gtJSzCONryLFs7Lpk5ln6k/h+dOW2T2dfUxv64a1Dw6egzt8kq+53pRWh/nnlfwW7NhLB8T\nVqSVtGM05nn6hmPMb859/HxNzbEdmH9j5g5nx+hz8CTtF+39+Lg6uwn1PNo+RQtV9wTedrX4JPT5\nwx5GzLJ25N9Hx3s/aaMbcR/OL2Ye77vpGo2/FXgaz9RzTCDzayPrgRuZ2/r2yj7jDZpd/R10/JU5\nXKvvFUnazDqhXkDGtxnMb8TNvKaZKblwALWr8ir5+S24Ay0/ab21gLlRzM+9CtdvZ4UaFYab4phf\n0yXUZXLyQw0u/6lcW99ShJiwbS/GNK0zE38rb9joFkbqXLijRoBDEK9Nc3olagDRFrs/bt7H/FKy\noSen8zJ6CN9m7HkP7Y8D3TEXh5jzfyva+SriyN2rblCmhsYEfc0Z2vI0by+et5kZP0eqje8g9ef0\nrd3ve+1WzU76DXU/zu7ktUJoTEwYizpMX3+PlrBjovhYmjULWnYaR2c8xNvPVh9CDZpLlxEbvHQt\ny/eew75o5BrUHHMI4zWy6N7sBKnLN2ohn3untiNeJcxTJuXoqsOaHeTvxY7R2mdeoxGXanXtipsv\no/5deQPmkXteHfO7fjjioSWpK9N6uZ75lRtR+6Y3G88jfCTmbNKTvK5dTyfqhDTlkXp8ujat7kmo\nI3fpe9QBGx4TwfzaClAnqvEyrsM3zcD8hpA6HGak9lW+Lq65V3ira0mED9aTumz+fPJJW92hdyCG\n0VpdSill6YCYQ+9b3re8XppLNMZJ/mrUxAucx/fhKVNRW8b4K2pW0Pphvi58Tgz0Yf7mrsacCL2R\nx3XaLnzLyziH6Y/x/UcfCTd27mi9XH2G174Kvgm1iIb8ilqcrbo6Uf29OL9APmT+NrTOjEsiHy8N\np9DeO2A+4loRua9KKWXrg/vSS/ZD1z3K50vBBnzOn6whHaROjVJKGauxfvqQODflvjTNbte9s3aU\nYy7Gz8IYaDhdwfzMRmOMxY7Azdy3i9cEy3m3RLPjAhGHynS13Vo6UJczfy/qr0TMiGZ+A/38PdPU\nuCfgveHMu3vZsagF2Af2kbpUtz7LA3tHFWrr0Fo9BRd4HbTsXbg38UH4uwuX87qx5Ttxr7Ztw14v\n6qxRs6e8yPcIBfuwjwyZhPHjEMLnrLsX9sZpT+CaNj67mvnRGnBZRVg/g66LZH4fvbJGs19Y87hm\n62viFK5DXPJ8mI9vpSRzRhAEQRAEQRAEQRAEYVCRH2cEQRAEQRAEQRAEQRAGkavKmuwDkQbmYjCw\nY2f+i5TjgACkCTbq5DBRtyO1tq0BMhArK56CGkBaANOWceW61pWFWyGhMpA2qxc+hVTIO423YO5t\nR6pSdzfS/Npbi5ifswckOb2dkMbo08HTX0e7LUvSCjTlXt4qqyodad623kjXG/fcbcwvfyNJD5+o\nTA5NZXQbzVvd0jZtLn4kPWs4T53rJqlpzblIu718hD+fUfcjRazuDNJTbby4NCzYAymawQuQ8pn/\nDVK9xizmbXRnTr1Psx+95RbNrvkvT0sMjofUrI7IfDx1Mh87F8h0yjKQeu0cx8emmwFp5GVb0MI2\n+gHe4rh44wV1rVj9Jebbwtl8kNAnZesL2cHZL7hk51guzv3Rz9AmztLOivmp0bh/VJaY/lk6c4sO\nx/08/BXSy8cmID24T9dC2Dka0pYIZ6Qh07nyP38Ypo0lJGeBZNwopdSNK5BOeeQDnB/9jFJKBZM2\nvX1tkMQF6b6vYAckDNGTlMlJnYwYQ1uuKsXTVSOmYMzVneXptJ0knZS2It6/5Tjziw3Ac6QtHEu3\nXGZ+RUaksIdG4zPNV5AO31XfwT5jZ4c0XrdUpKp2VvMWn9UnEfPDbkKKcHU2T5uvPUX8vJESHTKN\n515TiReNa8MceHv5yn0YT9FpyqSMeWC8ZsfppA+0XW75DqR5f/L7NuZ3x0TMYRd/pNke+/II80u4\nHuOlZD/aMuYe4q1KgyIgd0gJgaQvdAKkS6Wb+XNvikDKdztJBze35duCpjY808TZOJ+7YriMbgiR\nEpw6ACli+qu/ML/ccqwLI29Ay9/vPt3M/Lx1kgFTQ1s8N1+qZcdG34nW7Gd/wn33c+fnVJSJdePB\nf6AF8PGtXD5cfQh7jctluP7GNj5fJs2EJMgwGzKa0cfwvCc/PZ19xsIWcfTce5CgOYbzVq2+0yDP\nvZyLOUulIkoptZK0Td6+BvutsGYuVQhOQIr+yHlEoqmTd8clXbs2zKOWYb/RXtbCjplZ4d8eMz5D\nm9qJT/MU8qPv/qHZUTHYO7oP43ulTpKqbxeAOGTjySXbMWGIofk/YQ12isDzqDyeyz7TRtrVm1ni\nvE9kcNnHbNIamYp6Ssp5G3FfIme0cUJr1w2rdjG/GNIqOCASMcQ3nkuYT+xHq+aUJcrkBM3HnuGP\nz9PZsSh/nEtLIfbvnVVcGrt770nNXrwcEofQWxOYXxtpgxtzz3WaXXs5m/lRKTmNbUFp2Jeam/N9\n7Y7nPsF3T8U1VR3g7xoeI3Hf6R6k8UIN86PvYPWZkEBWneXSr6CpiPPuYzH+elt4G3u9hMeUXCnF\nmh4XzKWStY24502r8W6VeB/f49eS/XplIca0n658Qlsn3ltOFUDy4qYrQZE6EXuO/h7sF+pP4f65\nJvuyz5zYDVnneFISo6amkfmd/x57jORkvDtRaaRSSoV44Tu8SQw+u+4080uYhveghrN4jy7Yzddt\nF0dcY+AK3nbeFNRfwF6sroXHVD+yj/zyNazrN87mLdHp3HEIwljo0sl9H3r/Ls3+9PHvNDtoF38H\nyzXinCYnQVq1el+6Zp+/l8+xh97Dd5///GfNDlnM40FZDmIi/Z0jMZXLlc4cx7tBF9mDdzXwvXH/\nANa/6hOQbfW187IadgGO6mpI5owgCIIgCIIgCIIgCMIgIj/OCIIgCIIgCIIgCIIgDCJXlTVVH0bq\na9463lWgnXQziLkPqbjFv3Nph5kZUm4bcpCqVbaDp2WHLkJqrYsL5CLed81kfvX1SE+1tkZa9bCn\n0IWpYD9Pj7bxQNrpmc8+x3nfxStCV11Gd6Cqg0iR8pvBU+snPY/P2dkZNLu9vZD5WZMOReZWuNU1\nl84zP9oR51pQSSQj4QvS2LH+fqQ9tjXCz9GHd12xtkbq35Vfv9RsQyRPf6XV//2n475ZWrozv6ah\nSN8s34OxEHEv0twz3uTdQF5cCikOlTw5hvD0bUt7yEXMzJDS21HP0w0HSPoZLfzvHMmlLmVHTxM/\nOFrb8Wvqa+cpe6bkxhmQUjjF8PPzGIU01oZMSGBiFyUyP+8T+Fz5PqSCDujS0HuJhM3eD6l30/41\ng/k5eSKVNuvJD3E+YzF21r6/hX1myWyMieL9OIfKRp4yOv0ZzPuGchzzH8WlaXRs+3vheQTrut6c\n+QJzO+luxJfv1u5kfgk6+aap8R6D8y/6jcfKoHmoyv/7K4hhM+7T6asQApXnONzrUXZcymVLuodR\nWZNrMpej0C5RNGZd2YQuGf6km5JSShUfhRSAdlhwDOTSB2/SoaS3C+mfoXNjmZ9HNGRcqUSekL2F\nrzvJt0A+QbuFGW7h3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d04Fxk/bNTi2EWQU53/z1r2nshlcLO5+8PntLihks8vVGjgUo7j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FESjcMsw9kKS71qpF6Jh/53OsGz/yziojjhyL/hVLnmb3p05B6OHRbRT6wsx6fALlvToLbm7P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TH0EOU1HjIiKk2hFnRKKWVLpEdb/7Vdx00Gy+0Jf8nQcSmxCneKcmd5VqQcsuYozotXMi/1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/HebLgucW4PPe47+b8QiuhaTmBk+MMOIxmVfTe3I/3mbEzcXoau8ZbZLNWGMcjXwI4+3N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\n+PZzP7O8+ppMLfb2m/LXX+J/ie3PwWo+sCeXszfkoD7YR+A7FlzgEp3Au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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kL8MEhNgrx9N", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits. You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n", + "\n", + "It can be interesting to stop training at different numbers of iterations and see the effect.\n", + "\n", + "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n", + "\n", + "What differences do you see visually for the different levels of convergence?" + ] + } + ] +} \ No newline at end of file diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb new file mode 100644 index 0000000..a08cdc8 --- /dev/null +++ b/sparsity_and_l1_regularization.ipynb @@ -0,0 +1,1175 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "sparsity_and_l1_regularization.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "yjUCX5LAkxAX" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Sparsity and L1 Regularization" + ] + }, + { + "metadata": { + "id": "g8ue2FyFIjnQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Calculate the size of a model\n", + " * Apply L1 regularization to reduce the size of a model by increasing sparsity" + ] + }, + { + "metadata": { + "id": "ME_WXE7cIjnS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n", + "\n", + "L1 regularization is a good way to increase sparsity.\n", + "\n" + ] + }, + { + "metadata": { + "id": "fHRzeWkRLrHF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and create feature definitions." + ] + }, + { + "metadata": { + "id": "pb7rSrLKIjnS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "3V7q8jk0IjnW", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pAG3tmgwIjnY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "4356e590-c0d5-48f4-cfe5-e6074e2cd5ac" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.7 2644.9 538.9 \n", + "std 2.1 2.0 12.6 2197.5 422.3 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1459.0 297.0 \n", + "50% 34.2 -118.5 29.0 2131.0 434.0 \n", + "75% 37.7 -118.0 37.0 3149.0 646.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1427.9 500.8 3.9 2.0 \n", + "std 1165.1 384.8 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 793.0 282.0 2.6 1.5 \n", + "50% 1165.0 410.0 3.5 1.9 \n", + "75% 1716.0 602.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 52.0 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gHkniRI1Ijna", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bLzK72jkNJPf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_buckets(feature_values, num_buckets):\n", + " quantiles = feature_values.quantile(\n", + " [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n", + " return [quantiles[q] for q in quantiles.keys()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "al2YQpKyIjnd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + "\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"households\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"longitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"latitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"housing_median_age\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"housing_median_age\"], 10))\n", + " bucketized_total_rooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_rooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n", + " bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_bedrooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n", + " bucketized_population = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"population\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"median_income\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"rooms_per_person\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"rooms_per_person\"], 10))\n", + "\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n", + "\n", + " feature_columns = set([\n", + " long_x_lat,\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_total_rooms,\n", + " bucketized_total_bedrooms,\n", + " bucketized_population,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hSBwMrsrE21n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Calculate the Model Size\n", + "\n", + "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works." + ] + }, + { + "metadata": { + "id": "e6GfTI0CFhB8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def model_size(estimator):\n", + " variables = estimator.get_variable_names()\n", + " size = 0\n", + " for variable in variables:\n", + " if not any(x in variable \n", + " for x in ['global_step',\n", + " 'centered_bias_weight',\n", + " 'bias_weight',\n", + " 'Ftrl']\n", + " ):\n", + " size += np.count_nonzero(estimator.get_variable_value(variable))\n", + " return size" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XabdAaj67GfF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Reduce the Model Size\n", + "\n", + "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n", + "\n", + "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n", + "\n", + "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?" + ] + }, + { + "metadata": { + "id": "G79hGRe7qqej", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Task 1: Find a good regularization coefficient.\n", + "\n", + "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n", + "\n", + "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n", + "\n", + "Again, the model will train on the entire data set, so expect it to run slower than normal." + ] + }, + { + "metadata": { + "id": "1Fcdm0hpIjnl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " regularization_strength,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " regularization_strength: A `float` that indicates the strength of the L1\n", + " regularization. A value of `0.0` means no regularization.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 7\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on validation data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " # Compute training and validation loss.\n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9H1CKHSzIjno", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "outputId": "950a7c86-e886-472e-9bf7-ad896e91ba85" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=0.01,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.31\n", + " period 01 : 0.28\n", + " period 02 : 0.27\n", + " period 03 : 0.26\n", + " period 04 : 0.25\n", + " period 05 : 0.25\n", + " period 06 : 0.25\n", + "Model training finished.\n", + "Model size: 787\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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CCCGE8RismDl48CBJSUmsWbOG+fPnM3/+/JuOv/rqq3z44YesXr2agoICdu3a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\njjbywdFGLJEm5uZ5F+pbUJSEPU7TvkVChcKMiMgVoi1mls5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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "yjUCX5LAkxAX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "hgGhy-okmkWL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n", + "stronger regularization gives us smaller models, but can affect the classification loss." + ] + }, + { + "metadata": { + "id": "_rV8YQWZIjns", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "outputId": "808ff15d-7eda-4a3a-9dc0-eb652560442d" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " regularization_strength=0.1,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.31\n", + " period 01 : 0.29\n", + " period 02 : 0.27\n", + " period 03 : 0.26\n", + " period 04 : 0.25\n", + " period 05 : 0.25\n", + " period 06 : 0.25\n", + "Model training finished.\n", + "Model size: 765\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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OAHq6hzMldDyldWUsP7USgxF2TQkhhBCmJsWMlZoytDuBXo58dySH02fLjDZu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\ntAQAv/71r/GDH/wAW7ZsEV977rnnxF/v378fDofjikGGiIj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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb new file mode 100644 index 0000000..ec01a81 --- /dev/null +++ b/synthetic_features_and_outliers.ipynb @@ -0,0 +1,1510 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "synthetic_features_and_outliers.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "i5Ul3zf5QYvW", + "jByCP8hDRZmM", + "WvgxW0bUSC-c" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Synthetic Features and Outliers" + ] + }, + { + "metadata": { + "id": "jnKgkN5fHbGy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Create a synthetic feature that is the ratio of two other features\n", + " * Use this new feature as an input to a linear regression model\n", + " * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data" + ] + }, + { + "metadata": { + "id": "VOpLo5dcHbG0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n", + "\n", + "First, we'll import the California housing data into a *pandas* `DataFrame`:" + ] + }, + { + "metadata": { + "id": "S8gm6BpqRRuh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "9D8GgUovHbG0", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "9bb25b8b-9487-4709-c5da-a474adcc19cc" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn.metrics as metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
13316-121.937.332.01260.0199.0560.0207.06.6346.7
6612-118.334.039.01306.0345.01332.0331.02.092.2
11154-121.037.630.01721.0399.01878.0382.02.583.9
6270-118.234.118.04297.01420.04332.01286.02.3192.5
544-117.032.817.015401.03280.07302.03176.03.3121.9
..............................
12376-121.538.56.09104.01535.03759.01481.05.1174.5
15375-122.337.634.01693.0281.0846.0291.05.4339.4
3241-117.933.917.01452.0188.0630.0194.06.9285.2
1318-117.234.117.02285.0554.01412.0541.01.894.3
8494-118.534.228.04054.0712.02164.0746.05.0258.0
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17000 rows × 9 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "13316 -121.9 37.3 32.0 1260.0 199.0 \n", + "6612 -118.3 34.0 39.0 1306.0 345.0 \n", + "11154 -121.0 37.6 30.0 1721.0 399.0 \n", + "6270 -118.2 34.1 18.0 4297.0 1420.0 \n", + "544 -117.0 32.8 17.0 15401.0 3280.0 \n", + "... ... ... ... ... ... \n", + "12376 -121.5 38.5 6.0 9104.0 1535.0 \n", + "15375 -122.3 37.6 34.0 1693.0 281.0 \n", + "3241 -117.9 33.9 17.0 1452.0 188.0 \n", + "1318 -117.2 34.1 17.0 2285.0 554.0 \n", + "8494 -118.5 34.2 28.0 4054.0 712.0 \n", + "\n", + " population households median_income median_house_value \n", + "13316 560.0 207.0 6.6 346.7 \n", + "6612 1332.0 331.0 2.0 92.2 \n", + "11154 1878.0 382.0 2.5 83.9 \n", + "6270 4332.0 1286.0 2.3 192.5 \n", + "544 7302.0 3176.0 3.3 121.9 \n", + "... ... ... ... ... \n", + "12376 3759.0 1481.0 5.1 174.5 \n", + "15375 846.0 291.0 5.4 339.4 \n", + "3241 630.0 194.0 6.9 285.2 \n", + "1318 1412.0 541.0 1.8 94.3 \n", + "8494 2164.0 746.0 5.0 258.0 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "metadata": { + "id": "I6kNgrwCO_ms", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll set up our input function, and define the function for model training:" + ] + }, + { + "metadata": { + "id": "5RpTJER9XDub", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VgQPftrpHbG3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \n", + " Returns:\n", + " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", + " after training the model.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label].astype('float32')\n", + "\n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + " \n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Create a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", + " \n", + " return calibration_data" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FJ6xUNVRm-do", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Try a Synthetic Feature\n", + "\n", + "Both the `total_rooms` and `population` features count totals for a given city block.\n", + "\n", + "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n", + "\n", + "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n", + "\n", + "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n", + "the final RMSE should be.)" + ] + }, + { + "metadata": { + "id": "isONN2XK32Wo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**." + ] + }, + { + "metadata": { + "id": "5ihcVutnnu1D", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "36d0b85e-760b-419a-a65c-ba64b732a2e5" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE\n", + "#\n", + "california_housing_dataframe[\"rooms_per_person\"] = california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"]\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.74\n", + " period 01 : 189.63\n", + " period 02 : 170.31\n", + " period 03 : 153.35\n", + " period 04 : 141.26\n", + " period 05 : 133.87\n", + " period 06 : 130.84\n", + " period 07 : 130.81\n", + " period 08 : 130.97\n", + " period 09 : 131.46\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 194.0 207.3\n", + "std 89.0 116.0\n", + "min 44.5 15.0\n", + "25% 159.0 119.4\n", + "50% 191.0 180.4\n", + "75% 218.1 265.0\n", + "max 4248.6 500.0" + ], + "text/html": [ + "
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QMuBqgnt1A8WB7udVqFod9i4Day5cfvJ9CIzh9MyDuUJHYaWecH8HEf7ufT/s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\nM2ZkMKMbJ6k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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i5Ul3zf5QYvW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Leaz2oYMQcBf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "f79fb9dc-9e32-4fea-9284-fe8e8954df95" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 213.90\n", + " period 01 : 191.45\n", + " period 02 : 170.67\n", + " period 03 : 153.59\n", + " period 04 : 140.21\n", + " period 05 : 133.52\n", + " period 06 : 131.28\n", + " period 07 : 130.88\n", + " period 08 : 132.04\n", + " period 09 : 132.18\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 197.3 207.3\n", + "std 91.1 116.0\n", + "min 44.5 15.0\n", + "25% 161.6 119.4\n", + "50% 194.2 180.4\n", + "75% 222.0 265.0\n", + "max 4344.1 500.0" + ], + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean197.3207.3
std91.1116.0
min44.515.0
25%161.6119.4
50%194.2180.4
75%222.0265.0
max4344.1500.0
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ozwONHgIa4+7UGBnFWlIL9PjonHRoZLliEdbqKCp18sG6ckrNMGmwgY4x9eOr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\nkU7VQhuzpqWj013YC+6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZjQrZ8mcHFiU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Identify Outliers\n", + "\n", + "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values. Ideally, these would lie on a perfectly correlated diagonal line.\n", + "\n", + "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n", + "\n", + "Do you see any oddities? Trace these back to the source data by looking at the distribution of values in `rooms_per_person`." + ] + }, + { + "metadata": { + "id": "P0BDOec4HbG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "ec8a674d-b444-46b7-ddf9-276dd027eb90" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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pTOr2xTRVvvOZKstlHWAi6z6pSv+c/GwAZ3u9434OsMsqERWWrANRlU4NpQLx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\nrUYFfyAUb3DX5xawt/MC9nZegLVMRwm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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jByCP8hDRZmM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "s0tiX2gdRe-S", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "99c2938f-5152-4b13-8a56-145b70b3fa99" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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pTOr2xTRVvvOZKstlHWAi6z6pSv+c/GwAZ3u9434OsMsqERWWrANRlU4NpQLx\nVt6FplQAyxun5zVVVoh1gHQp4Ocd3qQ/56ZJIio0Wc/BDAsjRQtCQHSa7vYlM+Hzj+DjP/fD48t9\ndCbFOkBs3ccxMJxyXSvVfeOmSSIqNFmPiKKjCCWEYGGLnsZYqnV4+XfH0OMcQjgSHSFNtxnxxDeb\noFUX/taOXQ8ym7TQaVXxNPNEqUaSqda7WAmaiKQi60AEACMFbP8wlhAMjdrnE44AZ3u9+Mf/6MBT\n91w/oePm86U/dj0o3frZdJtx1BpRzNj1LlaCJiKpyToQOQaGUYw4pNeqcMNfTMZ/f5g8dfy8wwuP\nLwCTIbeq4BP50k+3HqTXqjBJr4bLI8STIu5YcQ3eOnQO7xy5kDZZohh9joiossg6ECXNpZaIAoDZ\npMPcK81oXT0bn/Z48HaKQBSOAOd6vbj2Ksu419KNdibypZ+uJFAgGMLjG5ug/XwPU+y8961dgDXX\nz0x5PawETUSFIOtAZDMboFYpJZ+eWzzLim/ccg1sCUkFM+zGlOssSkX09USZRjsT/dLPVBLIliIh\nIl1qeK717oiI8iHrSX6dRoWF15glP8+H3X346esdaN/XjVA4GvRMBi2m24xJf3+6zThuWi5TS4eJ\nlv+XohVEthUgiIgmQtaBCAC+8oVrCnIefyCEP35wflQvoEf+ajGmWgxQKqJ/ViqAmfZo1hxwOY3a\n4wukHe0IwZAoX/pilgQC2OeIiApD1lNzAKBVKQp6vs4uB9Yuuxrb938yKk36qinV2Hj7HNQadQiF\nw9i2uyv+eo1RiwFv8gy22GjHWqOHQa9JOrWW7Ze+FJtkWQmaiKQm+0D00Sf9BT1fv1vAtj+cGtUU\nr98TQL/HCUuNHq0tDeOSDlIFIeDyaKdtT3fSdOqZdmPOX/pitoJgJWgikprsp+aOnilsIKo2anHi\n0+Tn7Oxypp2GS6axoe7z9yZ/j88/gpFC9bpIg5WgiUgqsg5EQjCEc73ugp7z2ivMcKXYKNrn9uOz\ni56USQcAYDbqxq3fTDRRgYhIzmQ9NTfoFTAwNFKw802rM2Dj7XNw6txA0rUcADjc5YDZpE1a1cBi\n0uFH316CYWFk1BRXvt1YiYjKgWxHRKFwGDvf+6xg56vSKvHUPddDp1GiSpc6fh8/0w+DXpP0NYNe\nDZNBO26Kq5Sz09g4j4ikJtsRUduebuxN0W5bCs/8r5uhUiqxbXcXzjmGUv5ev9uPGmPy0j4XnEPY\nuvMEWlc3jCvZU2rZaawxR0RAYQoeyzIQ+QMjOSUETNS8a2phqtKkrX4QU2PUYjBFllw4AuztvACV\nKpqJNvYvuJSy01hjjqiyFfJVtEajAAAgAElEQVRhVJaByOVOvbgvBZ8vCCB99YOYxtl1OHq6L+Ua\nEgB0nHQgFI7gaLdz3F+wmKnX+WKNOSIq5MOoLOdYzNWpqxBI4dNLQ/HqB2ZT8mk3pQJYMteOWxZP\nx8J6a9rj9XsE7O04n7LcT7GVexYf172I0sv0MCr2vx1Zjoj0WjUaG2yjorWUwhHgzIVBHD7RC68/\neZZeBMD7J3rx/ole6LRKTLdNwgXHEJLtAEpVLLVURhvlmsXHdS+i7BS64LFs//XduWoWllybPNNM\nCs//54fY23kBgRTdYBM7UgiBMM47hjDdNinp75Z6m+5SzuKbiEyFZ4koqtAFj2UbiFRKJb5y01XF\nvoy0hoURrGycNqoIafTPpV/RWuwCqsVW6KkGIjkr9MOoLKfmYmxmA3RqJYSR4rULT6ffI+D266/A\nhlWzR2XCbdvdlXRasZRGG+VWY469lYhyU8gtJbIORDqNCotnW3Ho48KlcufCYtLFv8ATv+RKbc9Q\nOqWQxSeGcl33IpJKIR9GswpEzz33HD744AOMjIzge9/7HhYsWIBHH30UoVAINpsNzz//PLRaLXbs\n2IEtW7ZAqVRiw4YNWL9+vSQXneiWxdNLNhA1NtiS/sXl8hdciM1klSA21VDqI1GiUlOIh9GMgejd\nd9/FqVOn0NbWBpfLha9//eu46aab0NraijVr1uDFF19Ee3s71q5di1deeQXt7e3QaDS44447sHr1\natTW1kr6AXzDQUmPnw+VUoHli6dmHOGk+wsWM8OLwSxKTiNRokqSMRAtWbIECxcuBABUV1djeHgY\nhw4dwlNPPQUAWLlyJTZv3oyrr74aCxYsgMlkAgA0NTWho6MDq1atkvDyAUu1XtLj5yMUjkCpVE4o\nJTjVZrJQOILbl8yMTyWlCzBMVx6t3Na9iMpFxkCkUqlgMESf2tvb23HLLbfgT3/6E7Ta6MZOq9UK\nh8MBp9MJi8USf5/FYoHDIf2UWV1tleTnyEfHSUfee4LSZXi93XkeezvOQ69VAlBACIRSBhiW6Umu\nXNa9iMpF1skKu3fvRnt7OzZv3ozbbrst/vNIJPmmmFQ/T2Q2G6BW5/dEarNFR16XTjvzer/U+j0C\n3nj7DP73hsVQqXIbffQ4h9DvSZ7hFduD5A9czhSMBRhDlRb3rV3w+esjOHq6L+kxjnQ78bUVs2AK\njMTvI00M76M4eB/FIbf7mFUg2r9/P37xi1/g3/7t32AymWAwGOD3+6HX63Hp0iXY7XbY7XY4nZeD\nQm9vLxYvXpz2uC6XL6+LttlMuHhpEG17uvHuRxczv6FI9hw+CyUiOY8+QsEQLKbkGV7pvHPkAtZc\nPxM6jQq9Lh8cruGkv+cY8ON/v7APNnMVFtZbK3aqTiw2mwkOh6fYlyF7vI/iKOX7mCpAZvz28Xg8\neO655/DLX/4ynniwdOlS7Ny5EwCwa9cuLFu2DIsWLcKxY8fgdrsxNDSEjo4ONDc3i/gRRotNO3mH\nC9cYLx/5bJZMt5ksncTKDOl2RgPRkkS9rmFWFiCioss4Inrrrbfgcrnw/e9/P/6zZ555Bk8++STa\n2towbdo0rF27FhqNBg8//DDuvfdeKBQKPPDAA/HEBbEVug3EROS7WTIxw6vf44cCqUsDxSTuh0mX\nrjxWqdS4I6LKpIhks5gjkXyHjyMKJb739O6kBUVLjbVaj5/cd0PeX/Kx1Oud732WsRFgS/OMUdOA\nl7PmnOh3+1PeL6UC+Ol3b+QCfp5KeSpETngfxVHK9zHV1JwsKyvE2kDkuoYiNqUC0KiVEFIUQgUm\nvlkyluHVuroBKpUyHlR02ugxA8FQyv0wienKDpcPP28/ysoCRFRyZBmICt0GIpUIgOvm2HHg+PiE\nCb1WhS8sjG5qFWNDabI9MED6fUQxOo0KM+wmVhYgopIky0AERNdQgiNhvP1h+ukqKVlMetyxoh4K\nACc+c8HlEWA26TD3CjP+anUDdBql6BtKx+6ByWU6bWxlgbray1lzRETFIss1otgcaK/Lh02/fFfk\nq8reTLsRPn8Q/W4BZpMWc6+0oHX1bBh0GgBIWWV77FpOocVGaPVXWeEZTJ7iTdkr5Tl5OeF9FEcp\n38e807dLWY1RV5QPoFQAM2yTcLbXG2+y1u8J4MDxi9i+/xMAgMcXwOETvUnfX+z+N7FRlV4r2wEx\nEZURWX8TBYIhFKMTUTgCeFMUW+046UAoHMGHXU4MeANJf4f9b4iILpN1IDrX6y3Kec1GHVwpWnr3\newTs7Tif/v3MUhuHFcKJKpesA5HdXJyCp1V6FVwpYqBSkXnjKbPULmOFcCKSbSAKhcPx9ZhCcw8l\nn3ID0gehWqMWzXPtzFJLwArhRCTbR862Pd14J8n+HakpgLzr2y2st6C1pUHyJ30hGEKvy1fUhIhs\npGt3UeyEDiIqHFmOiIpZa24iue4ffTIAIRhKOi0nxhqJ3Ka5Br0C+lNUx2BCB1HlkGUgcrlTf4GV\nsmRfrmIGD7lNc8UqhLPsEFFlK73H5CzEas3JTbIv11jwiO1HigWPXFszyHGaK127CyZ0EFUOWQai\nWK05uRn75Spm8MhmmqsU3blqFlqaZ8BarYdSEa1W3tI8gwkdRBVEllNzwOe15kJhvJ2hNUIpSCyA\nmkjMNZJ001y1Rh0CI+H4+lRsPcpUU5z090TJirlyJERUWWQZiEKh6LrKu0XImsvHJL0a65bXj1vz\nEXONJF0jPJ8wgh+99h4s1ToY9BoMDQfg8gRKqlX42GKuRFQ5ZDk1t/n/+wi7D59L2weolLg8QtKp\nMbHXSMZOc+k/71nkD4Ti609ne73o9wTYKpyISobsApEQDOHd4z3Fvoxxao3aNK/pUo5uxFwjiU1z\n/eS+G/Djby/BJH12A95STWYgosogu6m5Qa8Ax0BptS7Qa1V46p7r8czrHejp9417fVKVJuXoRoo1\nEp1GBa1GlXWKO/fsEFExyW5EVGPUwVZb/EX2ZAIjyUcVPn8w44gjtkaSKgjlWi0htv6UjXz27Mil\negMRlT7ZjYh0GhVunD8VO/afKfalxAWCIZzr9abJgBNGjThyqaKQ74bXdMkLY+WyHiW36g1EVPpk\nF4gA4J6vzINvOICOk73o96QuQFooZpMeM+zGjBlw+XyJT6RawtjW4GZTLGsuiAGvkFercLlVbyCi\n0ifLQKRSRddVrr2iFi//7nixLweNDXUwGbQpRyCNDXUAgF+9dWJUodZMX+KZNryuW16fdiSTav0p\n31bhE70eIqJkZD2XcuRMX1HPr1QAK5umx0cUyTLgbr1uOsKRCJ7414Mpq4UfPtELj2/8yE6saglj\n15/ybRWe7nr63X6cOT/INSMiypksR0RA9On8ozP9Rb2GcAS4fcnM+LRashHIm2+fxh8zrNMMeAP4\n8eb3cd3c0dN0pVYUNN31KBTAC7/5kGtGYLdZolzJNhANeoWkX4iFplIqxv0sNuLw+AL44ER27Spc\n3vHTdOkSDopRFDTd9cQaAlb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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kMQD0Uq3RqTX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n", + "\n", + "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:" + ] + }, + { + "metadata": { + "id": "POTM8C_ER1Oc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "445e8b46-1b13-4b00-b659-fb9cbf296569" + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "9l0KYpBQu8ed", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Clip Outliers\n", + "\n", + "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n", + "\n", + "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n", + "\n", + " clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n", + "\n", + "The above `clipped_feature` will have no values less than `0`." + ] + }, + { + "metadata": { + "id": "rGxjRoYlHbHC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "677af3a4-1364-47c4-945c-a233504f8732" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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oSM9YurYN59tD2Wzbe8rb4YiIiHiUakqI+Bhfbu1ZOZLj7JoSlXxhJIeISGNk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\nulDf5EBdkwN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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "b1d8laPy8vYC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "482464d8-c0b4-4d82-ef58-257445401bbe" + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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TKshla7JSIgGaojRLYBbzrKi5he6/fRFeeftcUe4iwuxEjMfxvV90YsAbSisx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/D/vSEWe0SEkRYCoRiqKly494bZ+PraGfi8PYh/2d6EYDjbxOt22bDxlrn4y9E2HDvdhe4+8blU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\nGAEQgUwgEAgEwgiACGQCgUAgEEYA/xd78xKvo0/EsAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "WvgxW0bUSC-c", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "8YGNjXPaSMPV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results." + ] + }, + { + "metadata": { + "id": "Ged5Yw4Xu6Jy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "" + ] + }, + { + "metadata": { + "id": "9YyARz6gSR7Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "652a19fb-ec1b-4abb-898e-aada1f7d1908" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 33, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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WUFJJUWn1j4mISOhcOqEHkeF2Fn+yk+yCcrPLERERCQmFEiJCTKST+GhntY/F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\nqokNZPkk9m5R2bZGIxRwrk1nX2xOPgrZWR72vdXC/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KMS4BTDsmaiBXTyCiQ+/0eb8XnE\nx3Y2WJbNjbaE04BoinKhOUpH58jnXJZhebmPmip/xg8iL2fi50sguBNYlk13b4rW4YZDx4jyYbSR\n6jBul0xpkYfSIg8lRW7n52IPJYVuvB6F/PwsuroW2vZVIBDcT8iSxMfeVUsiZXCiqZuv//gsf/Br\naxZlzFkgELy9ENUN8+PfMN8kdZPewQT73mrh1KXuSXfVE1dbOPln/0Trd14Ay8JfEqTiHdWEd2/D\nrNuF6fOkmxERpwHhL3SaEdLIL4+hpJOk0RlxkjTcqkVZKEVR0ECd4XfMc69c4sCZDmTJ5RhXqvnI\nkopt26SMXtzuPv7kQ6v54a8us//43EdjpvP5+PsfNvLaoT5Sg25s01morJmsWevlj357xYTicq6q\nk/GPPd1IxnRkB9wMRJNkZ3moq8qloWlEkTM8lqFHNX70gwjPp84DIEkyqtdA8+toAT0zarJ94xLR\nkJiGeMLkrYY+3jzWxYVLURqbo8TiZuZ2v09hY12QmnQyRtUyHx632OER3FlicXNE8dA+on5ov5kk\npU9UPeTlaNSvyqK02ENpkZuSdCMiN1sTsbICgeCOo8gy//f7VvNX3z/FiaZunv33C3z83SuRhbeS\nQCC4DUSFw+35N8w3o4vh8U2S4WJe7e5i87H9dD33ApgWvqIAFXuryX5kG2bdwxh+v9OMiMccnwhf\nHnjDmWaEbUN/XOZ6v0Zf3HkL+F0W5eEk+QGT2fydm0gZHL8Qx++qRlPCSJKEZevE9TaSRie2nWJb\n3RIkSbrl0ZjxPh9dvSlefLmTl38Wo7/PAjxIsoU7lHR8IjwmCU0nK2vi+eZqjjj+sW+lIZEb9PCZ\nj24injQyHhKvHGkjFdWc0YzoyFiGJNtsWpfFzs251K8O8NMjV9MNFGvB0yXuVbp6UqO8ICJcvRFn\nVCItxYVutm4IUVPlmFKWFntEESe4I5iWTVd3KtNwGFY8tLYn6RuYqHrwuGWWlHjGKB+WFHsoLnSL\nxplAILjr0FSF/+fX1/LF753k0NkOfG6Vp/dWC9NngUBwy4imRJqFiBm8lXjR8cXwaPyRAdYffYXy\ns0fosky8+X4q9laT88hWzPqHMbICEO+HeBJkDfy54BnbjOiKKtzo1xhKOusJe0zKsnVyvOYEv4VJ\nn1PK5tgFg9dOJLGtSlwqGGaUpHGTlNkDODt929cU8cE9VfQMJG5pNGbY58O2QY9opAZd6FEVkIhj\novkNXMEUml8fLfyYcdxmNuaI03mMzAWfR8XrVujrM3n9YBdHTvbTfznImLEMv44WMCgsUPnT31+e\neZ8sVLrEvYph2Fy9ERvjBzFa0q6pEiuW+9lQl015qYvaSj+hoHAGFyws0ZhBa/s4r4eOBB03k+jG\nWNWDJEFejot1q7MyoxbDyofcbE38MS8QCO4pPC6VP36qns9/5zi/PN6C16Py/oeWL/ayBALBPYpo\nSqSZz5jBW40XnaoY9saGWH9sP6tPH0IxTTy5PsofrSLn0S1kPfg4A7YKyQFHHaFojjLCE2a4y2Ba\n0JFO0kikkzTy/AblYZ2gx5rweJPR1W9x4JTO0XM6iRQoMiD1MRhvw7SiY47NDbr57XfWoMjyLY3G\n2LZNw7l+bjTJpIaC2JZzzRS3iSuYwh1MkR3S6I9M3HGcj3Gb6TxGANyaTFKf+rrZtjOW0XjG5KN/\ncopoxClOZAny8hWidhTNryO7rEwjaENt0YT322zTJW6l+XW3E4kaNDZHM02IpssxkqmRax4Kqjyw\nIURtVYDaKj+VFT40TRZz9IJ5xzRtOruTtA4bTY5KuegfNCYc73HLVCzxjvg8FHkoLXZTXOjB7RJz\n1wKB4O1DwKvxyQ+u4y+/fZwXD17F51b57XevXuxlCQSCexDRlBjHfMQM3mq86Phi2BOPUn/8NdY2\nHEA1dNzZXsr3VDK0diU/opq1+UvZEI86ha3iAn8euEOZZoRuQtugRsuAhm5KSNgUB3XKQjo+18TZ\n5fFYtk3jNZM3GnQuXHNm84N+iYfXa2xdo/LioTb2HYtOuN/6FfmZ4nguozE9fSleO9TLqwd7udGW\nANxIioU7O4ErmEJNR5HmBj3UVeaMMYyc6pwzMVlBP10jJTfo4T9/ZAPPv3qZC9f66I8kCQfcRGMm\ng30SekRz0jIs5zVIyRZbN4Z5YH2YDWtD+P1yumF1+4qcW21+3W3Ytk17Z9Ixo0yrIJzX30GSoKzE\nQ211gNpKP7XVAYryXWJnWTCvRKJG2mByrPKh/WYSw5yoeijIdbF+TZDSInda9eAoH7LDQvUgEAju\nH0IBN3/6oXX85b8c51/3X8LlUdm1tliMSwoEgjkhmhLzzO3Eiw4Xw0OdfdSf+BX1J99A1VO4gm7K\n9qwiVr+Sn8orqF5TwUeWe5EliZZenatDbnZuqsw0IxKGk6TRNugkaSiyTXk4RWnIwK3O3IyIJ22O\nnNM5cEqnZ8A5fmmxzM56jbWVKmo6InG2Iy/THZdMWhw+0c/+Az2cOjeEZYOqSmzfFEb2JzjT1jFh\nrGT4vooi33JxP11BP1MjJRzw8PF3reRKS4wDR3tpOBuh40qMzFiGaqIFHaNKl9/gd55eOabRNV+K\nnFttfi02Kd2i+WpslB9ElMGhkR1nj1tm7cosaqv81Fb5qan0i+hTwbxgGDY3u5OZZAtH9eD8PPo9\nOIzPK7Os3DuScJFuPhQXunFp907jTyAQCBaSvLCXT35wHV/83gm+/bMLHDndzn949yrywt7FXppA\nILhHEH/pzzO3Ey+qJuLsOfc6Wf/+E1zJJFrARdljK0mtX8nLWg0r1i7lt5Y6X/DXunV+0hDhxLUk\nOUEPm9dZGLbK9T6NzoiKjYRLsSgLpyieRZIGQHuPyYEGnbcuGKQMUBXYvEplZ53GkoJbN48cf1zQ\n7+Ly1QRf/+YNDh7rIxZ3FBArKv3s3p7Dzi3ZBPxqunGgTtp4uN1xm5kK+skaKfVVuawuKeTZ77Vw\n7OQA7el4SUkCT8AGTwLXuLEMt0sh4JvobXC7ipzbaX7dafoHdKf50OxEczZfi2GMmrfPz3Wxc0s2\nK6v91FQFWLrEi6KIHRbBrTMYMTINh9HKh47OJKY59lhZgoJ8N9XLfJQUeVhS5KGk2Bm9CAdVoXoQ\nCASCWVCS5+e/ffwBvrf/EgdPtfOZZ4/w9KMr2LG2SHyPCgSCGRFNiXnmVjwUzFicm994jvavfZPc\n/iFUn0bZEzUYm1ZyNLiGtZuq+VDY+UJv7kzxk5NRTrWMnF91+zjd7iaiO+f2aU4zojDLmDFJw7Rs\nzl1xRjQutZjpdUrsXauxZbVGwDvzL5LZFtj9/QavHhxg/8EebnY5aRZ5ORqP78ln9/ZcSos9Y46f\nTePhVor7WNLgjVMTRz9gbEH/9KMreOemCg691cf5izF+/sIQP4g1A85u/taNYTbXh9hQF+TV0628\n8PrlCedLpEx+9PqVeVcu3E7zayGxLJsbbYkxKoiOzpF1yjIsL/dRU+VnZVWAmir/hPhWgWA2GIZN\nR1cy3XhI0NI+rIBIMBQxJxzv9ylULvU74xbDCRdFHooK3GhC9SAQCAS3TcCr8amPbObH+5v4l5cv\n8uxPz9NwqZuPPFZDlk/8rhcIBFMjmhLzzFw8FKx4gpvfep72r/4TRm8/ilel4p3VFD2+CXPdQxjh\nHB7HKegs1cs/vNLJm5dGPBzKSopYXVNJQV4OER2CHpPysE6ub+YkjUjc5vBZnUOndfqGnF3rqiUK\nO+s1Vi1TUOZpFjAWNzl4rI/9B3o5dzECgNsls2tbDrt35LCmNmvM3OFkHg/zbfj43ZcvkkhNblTZ\nN5Tg4pUhLl9JcqxhgHMXI5mYyfxcFw9tzWHzuhBragJjCpmn31nDLw5fnfS8C6FcuJXm10IQT5g0\nXYlxoclpQjQ2R4nFRwpCv09hY12Qmko/K6sDVC3ziYhDwayxbZvBIWPEZHJY+dCeoKMrOSYCFpym\nV2G+m5pK/6iEC6cBEcoSqgeBQCBYaCRJYsfaYmrKwvzDv5/nrYtdXGod4HeeWEldZe5iL08gENyl\niKbEAvAbu5bTeL2f1q4Ilu3Ig0vzA/zGLicqyUqm6PqXf6Ptb55F7+xBcauUP1pF8RMbkTbtwsrJ\nRTYSuEiC5gN/PrLLTyCsI8txlpeXsrqmilAwAEAiOsC2ahch78xJGi2dJm+c0jnRaGCY4FJh21pn\nRKMod36KRdOyOX1+iP0HenjzeD+plNP0WFMbYPf2XLZtDOP1KuPuc+umjcP3Pd7YSe9QipwsFxtq\nCia9b1I3uXC9b8y/OWkZCnpUw4q5+Mz/N6J4qF7mY/O6EJvXhahY4p2yqBmM6mPSIUazEMqFuTS/\n5pOuntQoFUSEqzfiYwrD4kL3mFSMJcUeYXYlmBHdsOi4mRzTfBhOuIhEJ6oeAn6F6mX+TKzmcOOh\nqMCNNptZNYFAIBAsKHlhL3/2m+t56eh1fvjaZf7q+w3s3lDKB3ZX3TXjpQKB4O5hQZsSFy9e5A/+\n4A/46Ec/yoc//GHa29v5sz/7M0zTJD8/ny984Qu4XC5eeOEFvvnNbyLLMh/4wAd46qmnFnJZC87z\nr17mRmck8/+WDTc6Izy/7yKPdl+g7Sv/QKq9E9mlULZ7OcVPbEDevBsrLw/bSIKRAJcf/PlOUwIw\nTNi+YRWFZWtQVA3TsrjR2krIFeddWwpR5KkbEoZpc7rZ4I0Gnavt6QSLkMTOOo3NqzS87rkVjVMp\nElraE+w/0MNrh3rp6XPiOosL3OzansOu7TkU5E29e/+dfU3sP96a+f+5mDZ+95dNvPLWyH17h1Ls\nO9aCZdt8eG/NmGOHxx5sE/SY5qRlRNWR2FEFtqwPsbk+xMb6ENmhiX4Qk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qMAACAASURB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9h6N5Y5dd7rfJZF2DAy533zuWlKDFrAKzL3AIBhAJcRoeZBCNArdbCYQJg1YyhGyV8I9aHooo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\nTE+V+u2/EDq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jNCIiIiIiIiKKCoYSRERERERERBQVDCWIiIiIiIiIKCoYShARERERERFR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\nRERR8f8BMtsCN6j6Q2gAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "528a46b4-b693-4d3f-dc06-18f1b20b5060" + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 35, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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cSt/Nq1CsXz4bDE3JfhchQcy4E1cyHh+Pu2+ei4e3NCcMXk2NGWd7hifsZMVo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\nS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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/validation.ipynb b/validation.ipynb new file mode 100644 index 0000000..e62535f --- /dev/null +++ b/validation.ipynb @@ -0,0 +1,1592 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "validation.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "4Xp9NhOCYSuz", + "pECTKgw5ZvFK", + "dER2_43pWj1T", + "I-La4N9ObC1x", + "yTghc_5HkJDW" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zbIgBK-oXHO7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Validation" + ] + }, + { + "metadata": { + "id": "WNX0VyBpHpCX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Use multiple features, instead of a single feature, to further improve the effectiveness of a model\n", + " * Debug issues in model input data\n", + " * Use a test data set to check if a model is overfitting the validation data" + ] + }, + { + "metadata": { + "id": "za0m1T8CHpCY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), to try and predict `median_house_value` at the city block level from 1990 census data." + ] + }, + { + "metadata": { + "id": "r2zgMfWDWF12", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "8jErhkLzWI1B", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First off, let's load up and prepare our data. This time, we're going to work with multiple features, so we'll modularize the logic for preprocessing the features a bit:" + ] + }, + { + "metadata": { + "id": "PwS5Bhm6HpCZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "J2ZyTzX0HpCc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "sZSIaDiaHpCf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **training set**, we'll choose the first 12000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "P9wejvw7HpCf", + "colab_type": "code", + "outputId": "dba474d2-30cd-496b-8ff6-e57cfb900bc5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + } + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2650.0 540.6 \n", + "std 2.1 2.0 12.6 2186.6 422.4 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1463.0 298.0 \n", + "50% 34.2 -118.5 29.0 2131.5 434.0 \n", + "75% 37.7 -118.0 37.0 3151.2 650.2 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1428.9 501.9 3.9 2.0 \n", + "std 1163.7 384.2 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 790.0 282.0 2.6 1.5 \n", + "50% 1166.0 409.0 3.6 1.9 \n", + "75% 1718.0 606.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "JlkgPR-SHpCh", + "colab_type": "code", + "outputId": "a28b2770-8995-46e7-a095-b48c70b739d7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + } + }, + "cell_type": "code", + "source": [ + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "training_targets.describe()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 12000.0\n", + "mean 208.6\n", + "std 117.1\n", + "min 15.0\n", + "25% 120.0\n", + "50% 181.1\n", + "75% 266.4\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "5l1aA2xOHpCj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "fLYXLWAiHpCk", + "colab_type": "code", + "outputId": "2b446a58-712c-4c78-a42a-bf37e2431d50", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + } + }, + "cell_type": "code", + "source": [ + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_examples.describe()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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mean35.6-119.528.62628.4536.61431.3499.63.92.0
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25%33.9-121.818.01460.0295.0787.0281.02.61.5
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75%37.7-118.037.03149.0646.01733.2603.04.72.3
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 5000.0 5000.0 5000.0 5000.0 5000.0 \n", + "mean 35.6 -119.5 28.6 2628.4 536.6 \n", + "std 2.1 2.0 12.6 2164.1 419.3 \n", + "min 32.5 -124.2 2.0 12.0 3.0 \n", + "25% 33.9 -121.8 18.0 1460.0 295.0 \n", + "50% 34.2 -118.5 29.0 2121.0 433.5 \n", + "75% 37.7 -118.0 37.0 3149.0 646.0 \n", + "max 42.0 -114.5 52.0 32054.0 5290.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 5000.0 5000.0 5000.0 5000.0 \n", + "mean 1431.3 499.6 3.9 2.0 \n", + "std 1109.1 385.3 1.9 1.3 \n", + "min 8.0 4.0 0.5 0.0 \n", + "25% 787.0 281.0 2.6 1.5 \n", + "50% 1168.0 409.0 3.5 1.9 \n", + "75% 1733.2 603.0 4.7 2.3 \n", + "max 15507.0 5050.0 15.0 52.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "oVPcIT3BHpCm", + "colab_type": "code", + "outputId": "0879d820-d060-4715-e039-cdd38771a39f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + } + }, + "cell_type": "code", + "source": [ + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "validation_targets.describe()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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median_house_value
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 204.3\n", + "std 113.2\n", + "min 26.6\n", + "25% 118.5\n", + "50% 178.4\n", + "75% 261.9\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "z3TZV1pgfZ1n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Examine the Data\n", + "Okay, let's look at the data above. We have `9` input features that we can use.\n", + "\n", + "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense will get you far.\n", + "\n", + "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data." + ] + }, + { + "metadata": { + "id": "4Xp9NhOCYSuz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "gqeRmK57YWpy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's check our data against some baseline expectations:\n", + "\n", + "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n", + "\n", + "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n", + "\n", + "If you look closely, you may see some oddities:\n", + "\n", + "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n", + "\n", + "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n", + "\n", + "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n", + "\n", + "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source." + ] + }, + { + "metadata": { + "id": "fXliy7FYZZRm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Plot Latitude/Longitude vs. Median House Value" + ] + }, + { + "metadata": { + "id": "aJIWKBdfsDjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n", + "\n", + "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`." + ] + }, + { + "metadata": { + "id": "5_LD23bJ06TW", + "colab_type": "code", + "cellView": "both", + "outputId": "e22229d8-cde0-4f1b-e7dc-41f34f7c21e1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 498 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(13, 8))\n", + "\n", + "ax = plt.subplot(1, 2, 1)\n", + "ax.set_title(\"Validation Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(validation_examples[\"longitude\"],\n", + " validation_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n", + "\n", + "ax = plt.subplot(1,2,2)\n", + "ax.set_title(\"Training Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(training_examples[\"longitude\"],\n", + " training_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n", + "_ = plt.plot()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ZGKaBL8eL32vQ1XrgDdsDxNLEFpGoYv3WVsaXXH7tmRD9lQQB59miWT4Wzerh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WpPGMJwTpE/rNKcNtCTsUhGK2/Ycrz6hjSvvDlxpCEMw7Jl4QC6+wL6BmQTMSGEOBEiVUmu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\n7hRpSsGBPkVQZ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "32_DbjnfXJlC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n", + "\n", + "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n", + "\n", + "**Go back up and look at the data from Task 1 again.**\n", + "\n", + "Do you see any other differences in the distributions of features or targets between the training and validation data?" + ] + }, + { + "metadata": { + "id": "pECTKgw5ZvFK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "49NC4_KIZxk_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75th percentile value for total_rooms per city block?\n", + "\n", + "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n", + "\n", + "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created." + ] + }, + { + "metadata": { + "id": "025Ky0Dq9ig0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n", + "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution." + ] + }, + { + "metadata": { + "id": "JFsd2eWHAMdy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n", + "\n", + "By the way, there's an important lesson here.\n", + "\n", + "**Debugging in ML is often *data debugging* rather than code debugging.**\n", + "\n", + "If the data is wrong, even the most advanced ML code can't save things." + ] + }, + { + "metadata": { + "id": "dER2_43pWj1T", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "BnEVbYJvW2wu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n", + "\n", + "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here." + ] + }, + { + "metadata": { + "id": "xCdqLpQyAos2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 4: Train and Evaluate a Model\n", + "\n", + "**Spend 5 minutes or so trying different hyperparameter settings. Try to get the best validation performance you can.**\n", + "\n", + "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n", + "\n", + "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n" + ] + }, + { + "metadata": { + "id": "rzcIPGxxgG0t", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "CvrKoBmNgRCO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)" + ] + }, + { + "metadata": { + "id": "wEW5_XYtgZ-H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "D0o2wnnzf8BD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n", + "\n", + "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n", + "\n", + "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n", + "\n", + "See how much better you can do now that we can use multiple features.\n", + "\n", + "Check the data using some of the methods we've looked at before. These might include:\n", + "\n", + " * Comparing distributions of predictions and actual target values\n", + "\n", + " * Creating a scatter plot of predictions vs. target values\n", + "\n", + " * Creating two scatter plots of validation data using `latitude` and `longitude`:\n", + " * One plot mapping color to actual target `median_house_value`\n", + " * A second plot mapping color to predicted `median_house_value` for side-by-side comparison." + ] + }, + { + "metadata": { + "id": "UXt0_4ZTEf4V", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zFFRmvUGh8wd", + "colab_type": "code", + "outputId": "4065f1c0-2c1c-4670-a158-37a9465b9f91", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + } + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n", + " learning_rate=0.0001,\n", + " steps=100,\n", + " batch_size=1,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.98\n", + " period 01 : 213.53\n", + " period 02 : 202.07\n", + " period 03 : 192.19\n", + " period 04 : 183.53\n", + " period 05 : 177.77\n", + " period 06 : 173.37\n", + " period 07 : 170.90\n", + " period 08 : 169.22\n", + " period 09 : 168.57\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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cpdnYQgghRHkjDYyGdIqOIfVDedKxFsfiTrD2/GZNrx5qXM+NQR3qkpqRy5er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\nALFqA+anzMYC8+yI6cqEOxtnrwtVl4Z3abqcLt/zKqUMOQM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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "I-La4N9ObC1x", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Xyz6n1YHbGef", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i1imhjFzbWwt", + "colab_type": "code", + "outputId": "963f26b1-73d7-47c4-f76d-344ab28b7254", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + } + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " learning_rate=0.00003,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 219.12\n", + " period 01 : 202.05\n", + " period 02 : 187.84\n", + " period 03 : 177.13\n", + " period 04 : 170.59\n", + " period 05 : 168.71\n", + " period 06 : 168.22\n", + " period 07 : 169.08\n", + " period 08 : 170.43\n", + " period 09 : 171.23\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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w14MfYeetfRjUsSdk0oZ/GV+b6IfXlh7FLydvYVg/Dxgb6nwqWoSnzQ01Dc6L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\nUVx/OtZNIyJqhWfiJaJmcpMH4MFx92HTiS34rPT/8OSeFZi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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "65sin-E5NmHN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 5: Evaluate on Test Data\n", + "\n", + "**In the cell below, load in the test data set and evaluate your model on it.**\n", + "\n", + "We've done a lot of iteration on our validation data. Let's make sure we haven't overfit to the pecularities of that particular sample.\n", + "\n", + "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n", + "\n", + "How does your test performance compare to the validation performance? What does this say about the generalization performance of your model?" + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "outputId": "91e0f94c-522d-4a5e-aebe-4e7814c03e00", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "#\n", + "# YOUR CODE HERE\n", + "#\n", + "testexamples = preprocess_features(california_housing_test_data)\n", + "testtargets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predictTest= lambda: my_input_fn(\n", + " testexamples, \n", + " testtargets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "testpredictions = linear_regressor.predict(input_fn=predictTest)\n", + "testpredictions = np.array([item['predictions'][0] for item in testpredictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(testpredictions, testtargets))\n", + "\n", + "print(\"RMSE: %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RMSE: 162.85\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "yTghc_5HkJDW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "_xSYTarykO8U", + "colab_type": "code", + "outputId": "08c0dfca-85a6-48e4-ca35-67a73e304456", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(\n", + " test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 162.85\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file