Day 5/MNIST_faishon_in_Google_Colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST faishon in Google Colab.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlF1TvRSDFWI",
        "colab_type": "code",
        "outputId": "c69ab1af-4877-43a3-c0dc-395d3b1d1b14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#importing the tensorflow \n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzwSlA4JOhur",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loading the MNIST fashion from tf Keras\n",
        "\n",
        "mnist=tf.keras.datasets.fashion_mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQLgbBj0RtqB",
        "colab_type": "code",
        "outputId": "b0e08b42-b813-4cec-b75b-8c5a67278210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# spliting the data\n",
        "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSZukSLESmup",
        "colab_type": "code",
        "outputId": "9e289951-8719-4af4-9cbc-f770e309725d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        }
      },
      "source": [
        "import numpy as np\n",
        "np.set_printoptions(linewidth=200)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#printing the shoe image\n",
        "plt.imshow(training_images[0])\n",
        "#priniting the show lable number\n",
        "print(\"Label \" , training_labels[0])\n",
        "\n",
        "print(\"Image \\n\" , training_images[0])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label  9\n",
            "Image \n",
            " [[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0   0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62  54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134 144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178 107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216 216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229 223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228 235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198 180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192 169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203 198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240 232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219 222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208 211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230 224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223 255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221 188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117 168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245 239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216 199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191 195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209 210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179 182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zs3l-qQYToEc",
        "colab_type": "text"
      },
      "source": [
        "You'll notice that all of the values in the number are between 0 and 255. If we are training a neural network, for various reasons it's easier if we treat all values as between 0 and 1, a process called 'normalizing'...and fortunately in Python it's easy to normalize a list like this without looping. You do it like this:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DECDo3enTovR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_images  = training_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyzdOoByTrHp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), \n",
        "                                    tf.keras.layers.Dense(128, activation=tf.nn.relu), \n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKQuRFHrUZsd",
        "colab_type": "text"
      },
      "source": [
        "Sequential: That defines a SEQUENCE of layers in the neural network\n",
        "\n",
        "Flatten: Remember earlier where our images were a square, when you printed them out? Flatten just takes that square and turns it into a 1 dimensional set.\n",
        "\n",
        "Dense: Adds a layer of neurons\n",
        "\n",
        "Each layer of neurons need an activation function to tell them what to do. There's lots of options, but just use these for now.\n",
        "\n",
        "Relu effectively means \"If X>0 return X, else return 0\" -- so what it does it it only passes values 0 or greater to the next layer in the network.\n",
        "\n",
        "Softmax takes a set of values, and effectively picks the biggest one, so, for example, if the output of the last layer looks like [0.1, 0.1, 0.05, 0.1, 9.5, 0.1, 0.05, 0.05, 0.05], it saves you from fishing through it looking for the biggest value, and turns it into [0,0,0,0,1,0,0,0,0] -- The goal is to save a lot of coding!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKFT0iNjTx0E",
        "colab_type": "code",
        "outputId": "5708bc1e-4d80-4685-b0fc-66151e6e85e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "model.compile(optimizer = tf.optimizers.Adam(),\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(training_images, training_labels, epochs=5)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5025 - accuracy: 0.8245\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3782 - accuracy: 0.8628\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3382 - accuracy: 0.8772\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3152 - accuracy: 0.8848\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2971 - accuracy: 0.8908\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd2c13659b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hq0SQPPMmiqV",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ecpx4u9maUN",
        "colab_type": "code",
        "outputId": "dadde98d-faae-49fc-9d6a-b4e0ed80ea70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.3575 - accuracy: 0.8701\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3575384318828583, 0.8701000213623047]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQTu8Mz3mt7X",
        "colab_type": "text"
      },
      "source": [
        "# Exercise 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJB-KcXhmvg9",
        "colab_type": "code",
        "outputId": "3e80b87a-8df4-425a-e4b2-9007a559faba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# classifying all the test images\n",
        "print(type(model.predict(test_images)))\n",
        "classifications = model.predict(test_images)\n",
        "\n",
        "#this return an numpy array of how each image has probabilty of all the 9 labels\n",
        "\n",
        "print(classifications[0])\n",
        "#or\n",
        "for i in classifications[7]:\n",
        "  print(i)\n",
        "\n",
        "print(test_labels[7])\n",
        "\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "[4.0918832e-05 4.0966874e-10 1.2976401e-06 3.7697848e-07 1.1556973e-07 5.3369086e-02 2.2033148e-05 5.5055644e-02 5.7198845e-06 8.9150476e-01]\n",
            "1.1777097e-05\n",
            "1.8158245e-07\n",
            "0.00048713933\n",
            "3.152421e-05\n",
            "0.0152614815\n",
            "1.298516e-07\n",
            "0.98420066\n",
            "6.213668e-10\n",
            "7.1437357e-06\n",
            "3.854802e-09\n",
            "6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrM1Rus8szHO",
        "colab_type": "text"
      },
      "source": [
        "### What does this list represent?\n",
        "\n",
        "It's the probability that this item is each of the 10 classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0WYGuiYT3E0w",
        "colab_type": "text"
      },
      "source": [
        "# Excersise 2\n",
        "Experiment with different values for the dense layer with 512 neurons. What different results do you get for loss, training time etc? Why do you think that's the case?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GSZSwV5UObQP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "61cd26d8-91e9-445a-f39f-70f22babec60"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(training_images, training_labels) ,  (test_images, test_labels) = mnist.load_data()\n",
        "\n",
        "training_images = training_images/255.0\n",
        "test_images = test_images/255.0\n",
        "\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),\n",
        "                                    tf.keras.layers.Dense(1024, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n",
        "\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'sparse_categorical_crossentropy')\n",
        "\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "\n",
        "model.evaluate(test_images, test_labels)\n",
        "\n",
        "classifications = model.predict(test_images)\n",
        "\n",
        "print(classifications[0])\n",
        "print(test_labels[0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc4\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1838\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0746\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0475\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0344\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0263\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0710\n",
            "[2.8948730e-09 3.2543088e-09 1.5517941e-09 2.1585643e-05 8.5978994e-14 5.2639570e-10 1.4742414e-14 9.9997461e-01 3.8601744e-09 3.8460880e-06]\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGQ1HfSM5LsS",
        "colab_type": "text"
      },
      "source": [
        "Answer\n",
        "The correct answer is (1) by adding more Neurons we have to do more calculations, slowing down the process, but in this case they have a good impact -- we do get more accurate. That doesn't mean it's always a case of 'more is better', you can hit the \n",
        "#law of diminishing returns very quickly!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzRdrhfg5W6s",
        "colab_type": "text"
      },
      "source": [
        "#Exercise 3: \n",
        "\n",
        "What would happen if you remove the Flatten() layer. Why do you think that's the case? \n",
        "\n",
        "You get an error about the shape of the data. It may seem vague right now, but it reinforces the rule of thumb that the first layer in your network should be the same shape as your data. Right now our data is 28x28 images, and 28 layers of 28 neurons would be infeasible, so it makes more sense to 'flatten' that 28,28 into a 784x1. Instead of wriitng all the code to handle that ourselves, we add the Flatten() layer at the begining, and when the arrays are loaded into the model later, they'll return an error\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UZplg9f6NAQ",
        "colab_type": "text"
      },
      "source": [
        "##Exercise 4: \n",
        "\n",
        "Consider the final (output) layers. Why are there 10 of them? What would happen if you had a different amount than 10? For example, try training the network with 5\n",
        "\n",
        "You get an error as soon as it finds an unexpected value. Another rule of thumb -- the number of neurons in the last layer should match the number of classes you are classifying for. In this case it's the digits 0-9, so there are 10 of them, hence you should have 10 neurons in your final layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h43uFv365MPs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqoH44XH6Vz5",
        "colab_type": "text"
      },
      "source": [
        "##Exercise 5: \n",
        "\n",
        "Consider the effects of additional layers in the network. What will happen if you add another layer between the one with 512 and the final layer with 10. \n",
        "\n",
        "Ans: There isn't a significant impact -- because this is relatively simple data. For far more complex data (including color images to be classified as flowers that you'll see in the next lesson), extra layers are often necessary. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpIGOBam5yp1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "72fdda16-f105-4f57-daa3-4c9965877df4"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(training_images, training_labels) ,  (test_images, test_labels) = mnist.load_data()\n",
        "\n",
        "training_images = training_images/255.0\n",
        "test_images = test_images/255.0\n",
        "\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),\n",
        "                                    tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(256, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n",
        "\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'sparse_categorical_crossentropy')\n",
        "\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "\n",
        "model.evaluate(test_images, test_labels)\n",
        "\n",
        "classifications = model.predict(test_images)\n",
        "\n",
        "print(classifications[0])\n",
        "print(test_labels[0])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc4\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1831\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0790\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0546\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0418\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0309\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0853\n",
            "[5.1994121e-11 2.9761441e-08 1.5448398e-08 6.6056984e-08 6.4211547e-10 1.8078644e-09 2.9613082e-16 9.9999917e-01 1.9821831e-09 6.7959377e-07]\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eu1K4Ks64ku",
        "colab_type": "text"
      },
      "source": [
        "#Exercise 6: \n",
        "\n",
        "Consider the impact of training for more or less epochs. Why do you think that would be the case? \n",
        "\n",
        "Try 15 epochs -- you'll probably get a model with a much better loss than the one with 5\n",
        "Try 30 epochs -- you might see the loss value stops decreasing, and sometimes increases. This is a side effect of something called 'overfitting' which you can learn about [somewhere] and it's something you need to keep an eye out for when training neural networks. There's no point in wasting your time training if you aren't improving your loss, right! :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VDgpeMn6-Hx",
        "colab_type": "text"
      },
      "source": [
        "#Exercise 7: \n",
        "\n",
        "Before you trained, you normalized the data, going from values that were 0-255 to values that were 0-1. What would be the impact of removing that? Here's the complete code to give it a try. Why do you think you get different results? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2I7iO_9c69fk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "801b2c14-2306-4681-a47d-0363834349ec"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "mnist = tf.keras.datasets.mnist\n",
        "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
        "training_images=training_images/255.0\n",
        "test_images=test_images/255.0\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "model.evaluate(test_images, test_labels)\n",
        "classifications = model.predict(test_images)\n",
        "print(classifications[0])\n",
        "print(test_labels[0])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc4\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2011\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0813\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0507\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0371\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0281\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0697\n",
            "[2.5871550e-08 2.0595010e-10 2.2954566e-08 4.4542580e-06 1.3554571e-13 2.3356275e-09 5.6988778e-13 9.9999559e-01 7.3698297e-10 2.1681895e-08]\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcAihjsy9ua8",
        "colab_type": "text"
      },
      "source": [
        "#Exercise 8: \n",
        "\n",
        "Earlier when you trained for extra epochs you had an issue where your loss might change. It might have taken a bit of time for you to wait for the training to do that, and you might have thought 'wouldn't it be nice if I could stop the training when I reach a desired value?' -- i.e. 95% accuracy might be enough for you, and if you reach that after 3 epochs, why sit around waiting for it to finish a lot more epochs....So how would you fix that? Like any other program...you have callbacks! Let's see them in action..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59bY--Os6nk-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "7efe3edd-4e8a-429c-84df-c2ff19da83fd"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('loss')<0.4):\n",
        "      print(\"\\nReached 60% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()\n",
        "mnist = tf.keras.datasets.fashion_mnist\n",
        "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
        "training_images=training_images/255.0\n",
        "test_images=test_images/255.0\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n",
        "model.fit(training_images, training_labels, epochs=5, callbacks=[callbacks])\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc4\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4717\n",
            "Epoch 2/5\n",
            "1871/1875 [============================>.] - ETA: 0s - loss: 0.3588\n",
            "Reached 60% accuracy so cancelling training!\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3589\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd2b4602780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "te_ahAVW9w0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# What is Callback functions in tensorflow ??\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtNHi7WB-TUK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}