basic_classification.ipynb

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   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
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   "source": [
    "#@title 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jYysdyb-CaWM"
   },
   "source": [
    "# Train your first neural network: basic classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "S5Uhzt6vVIB2"
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   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/beta/tutorials/keras/basic_classification\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/keras/basic_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/keras/basic_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/r2/tutorials/keras/basic_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FbVhjPpzn6BM"
   },
   "source": [
    "This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It's okay if you don't understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as we go.\n",
    "\n",
    "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tell TF not to consume all GPU memory so you can run more than one notebook at once\n",
    "import os\n",
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[37mcgpu11\u001b[m  Wed Jul 10 16:40:23 2019\n",
      "\u001b[36m[0]\u001b[m \u001b[34mTesla V100-SXM2-16GB\u001b[m |\u001b[31m 36'C\u001b[m, \u001b[32m  0 %\u001b[m | \u001b[36m\u001b[1m\u001b[33m    0\u001b[m / \u001b[33m16130\u001b[m MB |\n"
     ]
    }
   ],
   "source": [
    "!gpustat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dzLKpmZICaWN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-beta1\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",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yR0EdgrLCaWR"
   },
   "source": [
    "## Import the Fashion MNIST dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DLdCchMdCaWQ"
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   "source": [
    "This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:\n",
    "\n",
    "<table>\n",
    "  <tr><td>\n",
    "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
    "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
    "  </td></tr>\n",
    "  <tr><td align=\"center\">\n",
    "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
    "  </td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing we'll use here.\n",
    "\n",
    "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
    "\n",
    "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow:"
   ]
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  {
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   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Loading the dataset returns four NumPy arrays:\n",
    "\n",
    "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n",
    "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n",
    "\n",
    "The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 9. These correspond to the *class* of clothing the image represents:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>Label</th>\n",
    "    <th>Class</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T-shirt/top</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>Trouser</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>Pullover</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>Dress</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>Coat</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>Sandal</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>Shirt</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>Sneaker</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>Bag</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>Ankle boot</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## Explore the data\n",
    "\n",
    "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zW5k_xz1CaWX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cIAcvQqMCaWf"
   },
   "source": [
    "Likewise, there are 60,000 labels in the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TRFYHB2mCaWb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YSlYxFuRCaWk"
   },
   "source": [
    "Each label is an integer between 0 and 9:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XKnCTHz4CaWg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TMPI88iZpO2T"
   },
   "source": [
    "There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2KFnYlcwCaWl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rd0A0Iu0CaWq"
   },
   "source": [
    "And the test set contains 10,000 images labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iJmPr5-ACaWn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## Preprocess the data\n",
    "\n",
    "The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m4VEw8Ud9Quh"
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   "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": {
    "colab_type": "text",
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "We scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, we divide the values by 255. It's important that the *training set* and the *testing set* be preprocessed in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "To verify that the data is in the correct format and that we're ready to build and train the network, let's display the first 25 images from the *training set* and display the class name below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oZTImqg_CaW1"
   },
   "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": {
    "colab_type": "text",
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## Build the model\n",
    "\n",
    "Building the neural network requires configuring the layers of the model, then compiling the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### Set up the layers\n",
    "\n",
    "The basic building block of a neural network is the *layer*. Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.\n",
    "\n",
    "Most of deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9ODch-OFCaW4"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
    "\n",
    "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer is a 10-node *softmax* layer that returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 classes.\n",
    "\n",
    "### Compile the model\n",
    "\n",
    "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n",
    "\n",
    "* *Loss function* —This measures how accurate the model is during training. We want to minimize this function to \"steer\" the model in the right direction.\n",
    "* *Optimizer* —This is how the model is updated based on the data it sees and its loss function.\n",
    "* *Metrics* —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## Train the model\n",
    "\n",
    "Training the neural network model requires the following steps:\n",
    "\n",
    "1. Feed the training data to the model. In this example, the training data is in the `train_images` and `train_labels` arrays.\n",
    "2. The model learns to associate images and labels.\n",
    "3. We ask the model to make predictions about a test set—in this example, the `test_images` array. We verify that the predictions match the labels from the `test_labels` array.\n",
    "\n",
    "To start training,  call the `model.fit` method—so called because it \"fits\" the model to the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0710 16:40:27.953198 46912496740800 deprecation.py:323] From /usr/common/software/tensorflow/gpu-tensorflow/2.0.0-beta-py36/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 3s 53us/sample - loss: 0.4998 - accuracy: 0.8244\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 3s 48us/sample - loss: 0.3763 - accuracy: 0.8637\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 3s 48us/sample - loss: 0.3364 - accuracy: 0.8762\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 3s 47us/sample - loss: 0.3127 - accuracy: 0.8840\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.2957 - accuracy: 0.8906\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 3s 47us/sample - loss: 0.2806 - accuracy: 0.8956\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 3s 48us/sample - loss: 0.2704 - accuracy: 0.8997\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.2569 - accuracy: 0.9047\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 3s 48us/sample - loss: 0.2472 - accuracy: 0.9077\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.2402 - accuracy: 0.9095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2aab1ab3abe0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.88 (or 88%) on the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oEw4bZgGCaXB"
   },
   "source": [
    "## Evaluate accuracy\n",
    "\n",
    "Next, compare how the model performs on the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 33us/sample - loss: 0.3512 - accuracy: 0.8784\n",
      "\n",
      "Test accuracy: 0.8784\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy represents *overfitting*. Overfitting is when a machine learning model performs worse on new, previously unseen inputs than on the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xsoS7CPDCaXH"
   },
   "source": [
    "## Make predictions\n",
    "\n",
    "With the model trained, we can use it to make predictions about some images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.9521258e-08, 7.3569234e-11, 3.0172764e-08, 3.9523372e-12,\n",
       "       1.7094287e-08, 5.6421697e-05, 5.4653537e-08, 2.6831403e-02,\n",
       "       1.1165190e-08, 9.7311199e-01], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qsqenuPnCaXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "So, the model is most confident that this image is an ankle boot, or `class_names[9]`. Examining the test label shows that this classification is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sd7Pgsu6CaXP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ygh2yYC972ne"
   },
   "source": [
    "We can graph this to look at the full set of 10 class predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    predictions_array, true_label, img = predictions_array[i], 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[i], true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "Let's look at the 0th image, predictions, and prediction array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HV5jw-5HwSmO"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ko-uzOufSCSe"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAC6CAYAAACQs5exAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAANOUlEQVR4nO3df5BW1X3H8fdhQVgECxQEWsWtJoo/hskkq5WZKklbgu2YsU46aWfi2Jh2YtJxMp22sek0U+pUbf+pbXSGTEwm2KFMQpspGTD2l5nS0BasgggkQWEatZQqbgwisK6we/rHcxd3956L+7DAV3ffr5kdnv0+5znPuc8On717zz33ppwzkqRzb1L0ACRpojKAJSmIASxJQQxgSQpiAEtSEANYkoJMjh6AFG3u3Lm5q6srehgap7Zt29aTc55Xes4A1oTX1dXFU089FT0MjVMppReanvMQhCQFMYAlKYgBLElBDGBJCtLWJJyzxTqbnn/+eXp6elL0OKRzpa0AdrZYZ1N3d3f0EKRzykMQkhTEAJakIAawJAUxgCUpiAEsSUEMYEkKYgBLUhADWJKCGMCSFMQAlqQgBrAkBTGAJSmIASxJQQxgSQpiAEtSEANYkoIYwJIUxACWpCAGsCQFMYAlKYgBLElBDGBJCmIAS1IQA1iSghjAkhTEAJakIAawJAUxgCUpiAEsSUEMYEkKYgBLUhADWJKCGMCSFMQAlqQgBrAkBTGAJSmIASxJQQxgSQpiAEtSEANYkoIYwJIUxACWpCAGsCQFMYAlKYgBLElBDGBJCjI5egDvZKtWrSrWd+/ePeq2o5VzLtZTSmPqV9I7l3vAkhTEAJakIAawJAUxgCUpiAEsSUHGfBZEb29vsd7Z2TmmPs4777zTHtOgjo6OUbd99NFHa7UDBw4U21544YW12u23316r3XfffcXXX3zxxbVaO2c79Pf3j7ptO5+BpHPLPWBJCmIAS1IQA1iSghjAkhRkzJNwpckngLvuuqtWW7ZsWbFtOxN2Z0tpKfF1111XbFuaILzoootqtXXr1hVfX5rEu/XWW4ttZ86cWas1TayVJuealjiPlUukpbFzD1iSghjAkhTEAJakIAawJAUxgCUpSFtnQQwMDHD06NFhtf379xfbbtiwoVY7duxYse0111xTq82ZM6fYdvr06cVxlbz44ou12urVq4ttFyxYUKvNnTu32Hbjxo212i233FKrHTp0qPj6xx57rFbbs2dPse2ll15aqy1fvrzY9pJLLinWx6p0dkXTZz5pUv13usuhpTL3gCUpiAEsSUEMYEkKYgBLUpC2JuF6e3uLdwQuGTlZB7B27dpi2yVLltRqTdcDLtX37dtXbLtr165a7c033yy2veGGG2q17du3F9uuWLGiVitNDjZtw0033VSrHTx4sNj2ueeeq9W2bNlSbHvllVfWaldffXWxbXd3d602b968YtvSJJoTa2LBAnj55dG1nT8fXnrp7I7nXcg9YEmnZ7Th227bCcQAlqQgBrAkBTGAJSlIW5Nw/f39tdVdr776arnjyfWuX3vttWLb9evX12qzZ88utj1+/HitVrpmLsDSpUtrtcsvv7zYtrSCq7RCD6Cnp6dWK63ya1rNV/rMSpN4AIsWLRpVDeDw4cO12ubNm4ttn3zyyVGPYdasWbVa06q70rWOFy9eXGw7derUYl2aKNwDlqQgBrAkBTGAJSmIASxJQQxgSQrS1lkQkyZN4vzzzx9WKy2VBbjjjjtqta6urmLb0lkBb7zxRrFtaUZ+2rRpxbalPnbu3FlsWzJjxoxivXS2QGmJ80sNSy9LS5QvuOCCYttSv6WzHaB8/eKmMzFKmj7z0jLpAwcOFNuWPpt777232Pa2224b9n3T9ZOl8co9YEnvCgsWQEqj+yrcX+EdyQCW9K4wHi89YQBLUhADWJKCtDUJd+jQodrNNhcuXFhsW5qkaZo8Kt14smm57YkTJ0b1XgB9fX21WukGk02aJoVKS6qnTJlSq5WW5UJ7k3AlTUuG58+fX6s1bW9pcq9pMrNUb/pZln4WKaVi2wceeGDY9y+/W/5ulM4Q94AlKYgBLElBDGBJCmIAS1IQA1iSgrR1FkRfX1/tDsSXXXZZsW3pYuZNd1Tev39/rdbOUteBgYFi25KmtqWzApruoFya1S9dXPyVV14pvr7UtrOzs9i2dHZFk9KF4pu29/XXX6/Vms76KLVtWqZdWs68d+/eYtuR79f0eUvjlXvAkhTEAJakIAawJAUxgCUpSNvXAx45CbZ169Zi23aWupbalu4yDOUlu6Xr4AIcOXKkVmtnKXJHR0exXrrjc6lWutMylJciNylNwjVNgJWu29v0OZaWEjddD7h0J+rS9kJ5qXhTv/fcc8+w71euXFlsJ41X7gFLUhADWJKCGMCSFMQAlqQgBrAkBWnrLIhFixbx0EMP1WolpbvxlpbKQvksiKYzBUqz+qW7KgPMnDmzVivN0kP5jIWmmf7S8t7e3t5arelC5KVta1qG28642mlb+vmU7jgN5bNXmu62fMUVV9Rqy5cvL7Yd6cEHHxxVO2m8cA9YkoIYwJIUxACWpCAGsCQFaWsSrqOjg9mzZw+r3X///Wd0QJI0UbgHLElBDGBJCmIAS1IQA1iSghjAkhTEAJakIAawJAUxgCUpiAEsSUEMYEkKYgBLUhADWJKCGMCSFMQAlqQgBrAkBTGAJSmIASxJQQxgSQpiAEtSEANYkoIYwJIUxACWpCAGsCQFMYAlKYgBLElBDGBJCmIAS1IQA1iSghjAkhTEAJakIAawJAUxgCUpiAEsSUEMYEkKYgBLUhADWJKCGMCSFMQAlqQgBrAkBTGAJSmIASxJQQxgSQpiAEtSkMntNN62bVtPSumFszUYTXiXRA9AOpfaCuCc87yzNRBJmmg8BCFJQQxgSQoSH8Ap/REpfY+UdpLSDlL62TPU7yZS6j6tNik9Qko/rMazg5TeV9VvGTLOp0jp56r6FaS0jZSeIaWlVW0yKT1OStNP8f5/RUo3Vo9vJqWnqz6+T0p3ntZ2n0pKR8b4+sdJafYZGo004bV1DPiMa4XVzcD7ybmPlOYC54WO6S2fI+dvjqh9B9hAzpmUlgB/CywG7gQ+DzwP/DnwUeAzwBpyPlbsPaU5wPXk/DukNAV4GLiOnPeT0lSg68xv0mlKKQEJWAP8NnBf7IDOLCeXz5GUxvPbnUrj5HJsAMNCoIec+wDIuefkMyn9MfARoBP4T+DOKvg2AU8AHwJmAb9JzptJqRNYDVwF/KB63WBfXwKurWrfJOeVpzXanIfuQZ4P5Orx8arv6cBxUppVjX3FKXr7VeAfq8czaf0sflS9Tx/wbDX2R4DDQDewALj75C+GlD4HfAyYCqw/uV0pfQu4GJgGfJGcHx72zq1fdBuBe8n528V+UuoC/gH4V2Ap8CvABmAz4yyAnVxWmJxz3BfMyLAjw3MZVmVYNuS5OUMer8nwkerxpgx/UT3+5QyPV49/N8PXqsdLMpzI0D2sL+ioXr9kSF/dhXE9kuHZDDsz/GWGqUOeuzXDngyvZlha1RZVfW2p3vuBYdtS3va/PrlNre+/muFghq9n+HiGSUPG8ncZJmW4KsO+qv7hDA9nSNVzj2a4ccT2dmbYneEnq++PZJif4YkMy0/ZD3RlGMhw/Yhx7z3Zn19++TWmr9hjwK09yg8AnwJeAdaR0ieqZz9ESk+Q0i7g54Grh7zy76t/t/HWn+o3An9T9bsT2Dmk/cdIaTvwdNXPVW8zsj+kdWjhWmAO8AdDxryenBfT2iP806r2Ijl/kJyXAseAnwL2kNIaUlpHSpcX3mNhtc2D/f4W8AvAfwG/D3xtSNtvkfMAOX8fmF/VPlx9PQ1sr8b73uq5z5LSM8BWWnvCg/UptA6j3E3O/zKKfl4g560jxn2w2j5JYxR9CAJy7gc2AZuqsP0NUvoGsAroJuf/IaU/ofXn9KC+6t9+hm9DZqSUfoZWoF1Lzj+u/qSfVms3fEz/d/J9UlpdvX5km++S0mWkNJehh05af55/AfgssJbWceGVwMdH9NBbG0fOu4BdpLQG+CHwiZPjGLJFQ/79M3L+8ojt/SDwi8BScj5WHbIZfJ8TtH5prQD+7W366QKO1ra71VdvoS6pTbF7wK2zB947pPI+4AXeCoweUppB63jp2/kugyGX0jXAkqp+Aa0geY2U5gO/NIpxLaz+TbT2dHdX37+nqkFK76c1YfijIa9bBvwvOe+ldTx4gNYvidKZED8A3lO9bkYVnIMGP4dT+Sfgk9XnAyn9NCldCPwE8OMqfBcD1w95TQY+CSwmpc+/TT91rW1fQOuXiqQxit4DngE8VE1anQD2AZ8i50Ok9BVgF63/7E+Ooq8vAatJaSewg9af8pDzM6T0NPA94L+B/xhFX2tJaR6tvcMdwKer+keB20npOK29wF8j59ZedyucvkBrMgtaZzWspfUZf6bwHt+mdfbEV6v3uZuUvlz1e5S39n7Lcv5nUroS2FJN9x4BbqM1sffp6nN4ltZhiKGv6yelXwc2ktJhcl7V0E9/4V0/AGwl5xOnHJukUUmD+aEAKf07cDM5H4oeyqik9EVap+F9J3oo0ngQvxBjYvs9YFH0INqw2/CVzhz3gCUpiHvAkhTEAJakIAawJAUxgCUpiAEsSUEMYEkK8v9PKJSMBRx++wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "Let's plot several images with their predictions. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label. Note that the model can be wrong even when very confident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "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, test_labels, test_images)\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "    plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R32zteKHCaXT"
   },
   "source": [
    "Finally, use the trained model to make a prediction about a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yRJ7JU7JCaXT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. Accordingly, even though we're using a single image, we need to add it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lDFh5yF_CaXW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "Now predict the correct label for this image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o_rzNSdrCaXY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.9521162e-08 7.3569234e-11 3.0172703e-08 3.9523302e-12 1.7094287e-08\n",
      "  5.6421533e-05 5.4653430e-08 2.6831403e-02 1.1165210e-08 9.7311199e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6Ai-cpLjO-3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(0, predictions_single, test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`model.predict` returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2tRmdq_8CaXb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YFc2HbEVCaXd"
   },
   "source": [
    "And, as before, the model predicts a label of 9."
   ]
  }
 ],
 "metadata": {
  "colab": {
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