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Add tensorboard example (aymericdamien#381)
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* add tensorboard example

* fix desc

* add tensorboard run cmd
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@aymericdamien
aymericdamien authored Jul 26, 2020
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1 change: 1 addition & 0 deletions README.md
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#### 4 - Utilities
- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0.
- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models.
- **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard.

#### 5 - Data Management
- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...).
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1 change: 1 addition & 0 deletions tensorflow_v2/README.md
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#### 4 - Utilities
- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0.
- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models.
- **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard.

#### 5 - Data Management
- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...).
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350 changes: 350 additions & 0 deletions tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tensorboard\n",
"Graph, Loss, Accuracy & Weights visualization using Tensorboard and TensorFlow v2. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n",
"\n",
"- Author: Aymeric Damien\n",
"- Project: https://github.com/aymericdamien/TensorFlow-Examples/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import, division, print_function\n",
"\n",
"import tensorflow as tf\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Path to save logs into.\n",
"logs_path = '/tmp/tensorflow_logs/example/'\n",
"\n",
"# MNIST dataset parameters.\n",
"num_classes = 10 # total classes (0-9 digits).\n",
"num_features = 784 # data features (img shape: 28*28).\n",
"\n",
"# Training parameters.\n",
"learning_rate = 0.001\n",
"training_steps = 3000\n",
"batch_size = 256\n",
"display_step = 100\n",
"\n",
"# Network parameters.\n",
"n_hidden_1 = 128 # 1st layer number of neurons.\n",
"n_hidden_2 = 256 # 2nd layer number of neurons."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Prepare MNIST data.\n",
"from tensorflow.keras.datasets import mnist\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"# Convert to float32.\n",
"x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n",
"# Flatten images to 1-D vector of 784 features (28*28).\n",
"x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n",
"# Normalize images value from [0, 255] to [0, 1].\n",
"x_train, x_test = x_train / 255., x_test / 255."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Use tf.data API to shuffle and batch data.\n",
"train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
"train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Store layers weight & bias\n",
"\n",
"# A random value generator to initialize weights.\n",
"random_normal = tf.initializers.RandomNormal()\n",
"\n",
"weights = {\n",
" 'h1_weights': tf.Variable(random_normal([num_features, n_hidden_1]), name='h1_weights'),\n",
" 'h2_weights': tf.Variable(random_normal([n_hidden_1, n_hidden_2]), name='h2_weights'),\n",
" 'logits_weights': tf.Variable(random_normal([n_hidden_2, num_classes]), name='logits_weights')\n",
"}\n",
"biases = {\n",
" 'h1_bias': tf.Variable(tf.zeros([n_hidden_1]), name='h1_bias'),\n",
" 'h2_bias': tf.Variable(tf.zeros([n_hidden_2]), name='h2_bias'),\n",
" 'logits_bias': tf.Variable(tf.zeros([num_classes]), name='logits_bias')\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Construct model and encapsulating all ops into scopes, making\n",
"# Tensorboard's Graph visualization more convenient.\n",
"\n",
"# The computation graph to be traced.\n",
"@tf.function\n",
"def neural_net(x):\n",
" with tf.name_scope('Model'):\n",
" with tf.name_scope('HiddenLayer1'):\n",
" # Hidden fully connected layer with 128 neurons.\n",
" layer_1 = tf.add(tf.matmul(x, weights['h1_weights']), biases['h1_bias'])\n",
" # Apply sigmoid to layer_1 output for non-linearity.\n",
" layer_1 = tf.nn.sigmoid(layer_1)\n",
" with tf.name_scope('HiddenLayer2'):\n",
" # Hidden fully connected layer with 256 neurons.\n",
" layer_2 = tf.add(tf.matmul(layer_1, weights['h2_weights']), biases['h2_bias'])\n",
" # Apply sigmoid to layer_2 output for non-linearity.\n",
" layer_2 = tf.nn.sigmoid(layer_2)\n",
" with tf.name_scope('LogitsLayer'):\n",
" # Output fully connected layer with a neuron for each class.\n",
" out_layer = tf.matmul(layer_2, weights['logits_weights']) + biases['logits_bias']\n",
" # Apply softmax to normalize the logits to a probability distribution.\n",
" out_layer = tf.nn.softmax(out_layer)\n",
" return out_layer"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Cross-Entropy loss function.\n",
"def cross_entropy(y_pred, y_true):\n",
" with tf.name_scope('CrossEntropyLoss'):\n",
" # Encode label to a one hot vector.\n",
" y_true = tf.one_hot(y_true, depth=num_classes)\n",
" # Clip prediction values to avoid log(0) error.\n",
" y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n",
" # Compute cross-entropy.\n",
" return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n",
"\n",
"# Accuracy metric.\n",
"def accuracy(y_pred, y_true):\n",
" with tf.name_scope('Accuracy'):\n",
" # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n",
" correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n",
" return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n",
"\n",
"# Stochastic gradient descent optimizer.\n",
"with tf.name_scope('Optimizer'):\n",
" optimizer = tf.optimizers.SGD(learning_rate)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Optimization process. \n",
"def run_optimization(x, y):\n",
" # Wrap computation inside a GradientTape for automatic differentiation.\n",
" with tf.GradientTape() as g:\n",
" pred = neural_net(x)\n",
" loss = cross_entropy(pred, y)\n",
" \n",
" # Variables to update, i.e. trainable variables.\n",
" trainable_variables = weights.values() + biases.values()\n",
"\n",
" # Compute gradients.\n",
" gradients = g.gradient(loss, trainable_variables)\n",
" \n",
" # Update weights/biases following gradients.\n",
" optimizer.apply_gradients(zip(gradients, trainable_variables))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Visualize weights & biases as histogram in Tensorboard.\n",
"def summarize_weights(step):\n",
" for w in weights:\n",
" tf.summary.histogram(w.replace('_', '/'), weights[w], step=step)\n",
" for b in biases:\n",
" tf.summary.histogram(b.replace('_', '/'), biases[b], step=step)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Create a Summary Writer to log the metrics to Tensorboad.\n",
"summary_writer = tf.summary.create_file_writer(logs_path)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"step: 100, loss: 568.735596, accuracy: 0.140625\n",
"step: 200, loss: 413.169342, accuracy: 0.535156\n",
"step: 300, loss: 250.977036, accuracy: 0.714844\n",
"step: 400, loss: 173.749298, accuracy: 0.800781\n",
"step: 500, loss: 156.936569, accuracy: 0.839844\n",
"step: 600, loss: 137.818451, accuracy: 0.847656\n",
"step: 700, loss: 93.407814, accuracy: 0.929688\n",
"step: 800, loss: 90.832336, accuracy: 0.906250\n",
"step: 900, loss: 86.932831, accuracy: 0.914062\n",
"step: 1000, loss: 78.824707, accuracy: 0.906250\n",
"step: 1100, loss: 94.388290, accuracy: 0.902344\n",
"step: 1200, loss: 96.240608, accuracy: 0.894531\n",
"step: 1300, loss: 96.657593, accuracy: 0.898438\n",
"step: 1400, loss: 71.909309, accuracy: 0.914062\n",
"step: 1500, loss: 67.343407, accuracy: 0.941406\n",
"step: 1600, loss: 63.693596, accuracy: 0.941406\n",
"step: 1700, loss: 60.081478, accuracy: 0.914062\n",
"step: 1800, loss: 63.764942, accuracy: 0.921875\n",
"step: 1900, loss: 58.722507, accuracy: 0.921875\n",
"step: 2000, loss: 66.727455, accuracy: 0.917969\n",
"step: 2100, loss: 70.566788, accuracy: 0.949219\n",
"step: 2200, loss: 64.642334, accuracy: 0.925781\n",
"step: 2300, loss: 54.872856, accuracy: 0.941406\n",
"step: 2400, loss: 64.342377, accuracy: 0.925781\n",
"step: 2500, loss: 74.306488, accuracy: 0.921875\n",
"step: 2600, loss: 40.165890, accuracy: 0.949219\n",
"step: 2700, loss: 64.992249, accuracy: 0.925781\n",
"step: 2800, loss: 43.422794, accuracy: 0.957031\n",
"step: 2900, loss: 46.625320, accuracy: 0.937500\n",
"step: 3000, loss: 62.517433, accuracy: 0.914062\n"
]
}
],
"source": [
"# Run training for the given number of steps.\n",
"for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n",
" \n",
" # Start to trace the computation graph. The computation graph remains \n",
" # the same at each step, so we just need to export it once.\n",
" if step == 1:\n",
" tf.summary.trace_on(graph=True, profiler=True)\n",
" \n",
" # Run the optimization (computation graph).\n",
" run_optimization(batch_x, batch_y)\n",
" \n",
" # Export the computation graph to tensorboard after the first\n",
" # computation step was performed.\n",
" if step == 1:\n",
" with summary_writer.as_default():\n",
" tf.summary.trace_export(\n",
" name=\"trace\",\n",
" step=0,\n",
" profiler_outdir=logs_path)\n",
"\n",
" if step % display_step == 0:\n",
" pred = neural_net(batch_x)\n",
" loss = cross_entropy(pred, batch_y)\n",
" acc = accuracy(pred, batch_y)\n",
" print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))\n",
" \n",
" # Write loss/acc metrics & weights to Tensorboard every few steps, \n",
" # to avoid storing too much data.\n",
" with summary_writer.as_default():\n",
" tf.summary.scalar('loss', loss, step=step)\n",
" tf.summary.scalar('accuracy', acc, step=step)\n",
" summarize_weights(step)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Tensorboard\n",
"\n",
"To run tensorboard, run the following command in your terminal:\n",
"```\n",
"tensorboard --logdir=/tmp/tensorflow_logs\n",
"```\n",
"\n",
"And then connect your web browser to: [http://localhost:6006](http://localhost:6006)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![tensorboard1](../../../resources/img/tf2/tensorboard1.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![tensorboard2](../../../resources/img/tf2/tensorboard2.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![tensorboard3](../../../resources/img/tf2/tensorboard3.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![tensorboard4](../../../resources/img/tf2/tensorboard4.png)"
]
}
],
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