Resources -- 3a - Basic neural network in TensorFlow 2.0 -- DenseVariational layer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Deep probabilistic neural network in TensorFlow 2.0\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow_probability import distributions as tfd\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "print(tf.__version__)\n",
    "tf.get_logger().setLevel('ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen how to solve simple regression problems in TensorFlow 2.0 and the Keras layers library, but how about doing something deeper?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', \n",
    "               'Trouser', \n",
    "               'Pullover', \n",
    "               'Dress', \n",
    "               'Coat',\n",
    "               'Sandal', \n",
    "               'Shirt', \n",
    "               'Sneaker', \n",
    "               'Bag', \n",
    "               'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore these data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the image data we have is represented as a series of pixels, each with an integer r/g/b value of between 0 and 255 -- let's transform those such that they're usable by a neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize the r/g/b pixel values to between zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what these look like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've seen what these images look like and made them consumable by a TensorFlow 2.0 model, let's construct such a model and plug them in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow_probability import distributions as tfd\n",
    "\n",
    "\n",
    "# Specify the surrogate posterior over `keras.layers.Dense` `kernel` and `bias`.\n",
    "\n",
    "def posterior_mean_field(kernel_size, bias_size=0, dtype=None):\n",
    "    n = kernel_size + bias_size\n",
    "    c = np.log(np.expm1(1.))\n",
    "    return tf.keras.Sequential([\n",
    "      tfp.layers.VariableLayer(2 * n, dtype=dtype),\n",
    "      tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n",
    "          tfd.Normal(loc=t[..., :n],\n",
    "                     scale=1e-5 + tf.nn.softplus(c + t[..., n:])),\n",
    "          reinterpreted_batch_ndims=1)),\n",
    "  ])\n",
    "\n",
    "# Specify the prior over `keras.layers.Dense` `kernel` and `bias`.\n",
    "\n",
    "def prior_trainable(kernel_size, bias_size=0, dtype=None):\n",
    "    n = kernel_size + bias_size\n",
    "    return tf.keras.Sequential([\n",
    "      tfp.layers.VariableLayer(n, dtype=dtype),\n",
    "      tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n",
    "          tfd.Normal(loc=t, scale=1),\n",
    "          reinterpreted_batch_ndims=1)),\n",
    "    ])\n",
    "\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tfp.layers.DenseVariational(128, activation='relu', make_posterior_fn=posterior_mean_field, make_prior_fn=prior_trainable,),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a loss function, we'll choose sparse softmax cross entropy loss between logit outputs and labels -- this is a great loss function for mutually exclusive discrete classification tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01),\n",
    "              loss='categorical_crossentropy',  # tf.nn.sparse_softmax_cross_entropy_with_logits\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/15\n",
      "59968/60000 [============================>.] - ETA: 0s - loss: 726.4185 - accuracy: 0.6126\n",
      "Epoch 00001: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 16s 266us/sample - loss: 726.3683 - accuracy: 0.6126\n",
      "Epoch 2/15\n",
      "59904/60000 [============================>.] - ETA: 0s - loss: 727.0346 - accuracy: 0.6085\n",
      "Epoch 00002: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 16s 268us/sample - loss: 727.0759 - accuracy: 0.6086\n",
      "Epoch 3/15\n",
      "59968/60000 [============================>.] - ETA: 0s - loss: 727.0531 - accuracy: 0.6137\n",
      "Epoch 00003: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 17s 278us/sample - loss: 727.0394 - accuracy: 0.6137\n",
      "Epoch 4/15\n",
      "59968/60000 [============================>.] - ETA: 0s - loss: 727.2208 - accuracy: 0.6138\n",
      "Epoch 00004: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 16s 273us/sample - loss: 727.2274 - accuracy: 0.6138\n",
      "Epoch 5/15\n",
      "59840/60000 [============================>.] - ETA: 0s - loss: 726.8210 - accuracy: 0.6130\n",
      "Epoch 00005: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 17s 284us/sample - loss: 726.7667 - accuracy: 0.6130\n",
      "Epoch 6/15\n",
      "59872/60000 [============================>.] - ETA: 0s - loss: 726.7946 - accuracy: 0.6146\n",
      "Epoch 00006: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 17s 280us/sample - loss: 726.7840 - accuracy: 0.6147\n",
      "Epoch 7/15\n",
      "59904/60000 [============================>.] - ETA: 0s - loss: 726.6918 - accuracy: 0.6147\n",
      "Epoch 00007: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 17s 290us/sample - loss: 726.6924 - accuracy: 0.6146\n",
      "Epoch 8/15\n",
      "59840/60000 [============================>.] - ETA: 0s - loss: 726.8121 - accuracy: 0.6152\n",
      "Epoch 00008: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 308us/sample - loss: 726.8825 - accuracy: 0.6151\n",
      "Epoch 9/15\n",
      "59968/60000 [============================>.] - ETA: 0s - loss: 726.8072 - accuracy: 0.6170\n",
      "Epoch 00009: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 324us/sample - loss: 726.8112 - accuracy: 0.6170\n",
      "Epoch 10/15\n",
      "59968/60000 [============================>.] - ETA: 0s - loss: 727.4368 - accuracy: 0.6168\n",
      "Epoch 00010: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 325us/sample - loss: 727.4345 - accuracy: 0.6168\n",
      "Epoch 11/15\n",
      "59904/60000 [============================>.] - ETA: 0s - loss: 726.1850 - accuracy: 0.6106\n",
      "Epoch 00011: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 319us/sample - loss: 726.1652 - accuracy: 0.6106\n",
      "Epoch 12/15\n",
      "59872/60000 [============================>.] - ETA: 0s - loss: 726.7769 - accuracy: 0.6168\n",
      "Epoch 00012: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 20s 327us/sample - loss: 726.8209 - accuracy: 0.6168\n",
      "Epoch 13/15\n",
      "59936/60000 [============================>.] - ETA: 0s - loss: 726.5082 - accuracy: 0.6179\n",
      "Epoch 00013: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 316us/sample - loss: 726.4690 - accuracy: 0.6179\n",
      "Epoch 14/15\n",
      "59872/60000 [============================>.] - ETA: 0s - loss: 727.7692 - accuracy: 0.6218\n",
      "Epoch 00014: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 727.8141 - accuracy: 0.6217\n",
      "Epoch 15/15\n",
      "59968/60000 [============================>.] - ETA: 0s - loss: 726.6376 - accuracy: 0.6204\n",
      "Epoch 00015: saving model to saved_models/mnist_fashion.ckpt\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 726.6649 - accuracy: 0.6204\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x15c5cb5c0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint_path = \"saved_models/mnist_fashion.ckpt\"\n",
    "\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
    "                                                 save_weights_only=True,\n",
    "                                                 verbose=1)\n",
    "\n",
    "binary_train_labels = tf.keras.utils.to_categorical(train_labels)\n",
    "model.fit(train_images, binary_train_labels, epochs=15, callbacks=[cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 1s - loss: 698.4412 - accuracy: 0.6247\n",
      "\n",
      "Test accuracy: 0.6247\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  tf.keras.utils.to_categorical(test_labels), verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tfp.layers.DenseVariational(128, activation='relu', make_posterior_fn=posterior_mean_field, make_prior_fn=prior_trainable,),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "loaded.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01),\n",
    "              loss='categorical_crossentropy',  # tf.nn.sparse_softmax_cross_entropy_with_logits\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 1s - loss: 707.4386 - accuracy: 0.6280\n",
      "\n",
      "Test accuracy: 0.628\n"
     ]
    }
   ],
   "source": [
    "loaded.load_weights(checkpoint_path)\n",
    "\n",
    "test_loss, test_acc = loaded.evaluate(test_images,  tf.keras.utils.to_categorical(test_labels), verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model trains fairly quickly, even without a GPU. Fortunately, it's small and fairly shallow as deep neural networks go. Let's take a look at the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.1704397e-04, 4.7957772e-05, 5.0912536e-04, 3.3501433e-06,\n",
       "        4.5422593e-04, 3.4501934e-01, 7.3859468e-04, 1.3839976e-01,\n",
       "        1.2913620e-01, 3.8537437e-01], dtype=float32),\n",
       " array([1.0871658e-01, 7.8330643e-02, 1.3911177e-01, 2.9974042e-02,\n",
       "        1.9347187e-02, 5.2980012e-03, 2.4503610e-01, 9.9839130e-04,\n",
       "        3.7300277e-01, 1.8446801e-04], dtype=float32))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_images)\n",
    "predictions[0], predictions[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.17043974e-04, 4.79577720e-05, 5.09125355e-04, 3.35014329e-06,\n",
       "        4.54225927e-04, 3.45019341e-01, 7.38594681e-04, 1.38399765e-01,\n",
       "        1.29136205e-01, 3.85374367e-01],\n",
       "       [1.08716577e-01, 7.83306435e-02, 1.39111772e-01, 2.99740415e-02,\n",
       "        1.93471871e-02, 5.29800123e-03, 2.45036095e-01, 9.98391304e-04,\n",
       "        3.73002768e-01, 1.84468008e-04],\n",
       "       [3.63105879e-04, 9.28063452e-01, 2.90399482e-09, 7.15700313e-02,\n",
       "        3.03035563e-06, 2.35974781e-12, 3.57606154e-07, 5.42188415e-15,\n",
       "        5.10833320e-11, 3.75527314e-14],\n",
       "       [1.03540144e-04, 9.89663780e-01, 1.39549416e-09, 1.02310162e-02,\n",
       "        1.56631052e-06, 3.25547818e-11, 1.77785665e-07, 1.39594248e-15,\n",
       "        5.41046409e-11, 1.93457040e-14],\n",
       "       [1.38580039e-01, 1.90072041e-03, 1.87814295e-01, 9.08437520e-02,\n",
       "        1.90637544e-01, 2.80998973e-03, 1.87508896e-01, 2.27659964e-03,\n",
       "        1.97284356e-01, 3.43821215e-04],\n",
       "       [2.90579684e-02, 1.45846248e-01, 2.07768844e-05, 8.24108005e-01,\n",
       "        5.85638976e-04, 4.72134758e-08, 3.74635216e-04, 1.06333956e-10,\n",
       "        6.61381091e-06, 5.55752007e-11],\n",
       "       [1.12412125e-02, 1.18360558e-05, 2.25923210e-01, 8.48872866e-03,\n",
       "        7.75265768e-02, 8.58863220e-02, 1.66927483e-02, 1.06228936e-05,\n",
       "        5.74191570e-01, 2.71839999e-05],\n",
       "       [1.46782443e-01, 4.05167229e-03, 2.26595744e-01, 6.78042024e-02,\n",
       "        1.56804189e-01, 6.96536968e-04, 2.64752090e-01, 2.20922651e-04,\n",
       "        1.32228166e-01, 6.40355793e-05],\n",
       "       [4.06311080e-03, 2.04077023e-05, 3.21083958e-03, 1.69626530e-03,\n",
       "        1.02357171e-03, 5.54387212e-01, 1.64599379e-03, 1.26129940e-01,\n",
       "        2.99223512e-01, 8.59912857e-03],\n",
       "       [2.47355527e-03, 2.40840345e-05, 1.75582170e-02, 1.61675457e-03,\n",
       "        5.18618617e-03, 2.12104738e-01, 4.44470393e-03, 1.13029994e-01,\n",
       "        6.02295935e-01, 4.12658080e-02]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our softmax function effectively squished the model outputs into a distribution and seems to be most heavily activated on class 9. Let's verify that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    predictions_array, true_label = predictions_array, true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: are the other outputs of the softmax function probabilities?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now try to plot predictions from the test set generally and see how well we're doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "    plot_image(i, predictions[i], test_labels, test_images)\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "    plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate uncertainties!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}