MNIST_Digits.ipynb

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      "name": "MNIST Digits.ipynb",
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          "height": 575
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        "outputId": "43c68749-77ba-4b46-87ac-97797fbdfab4"
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      "source": [
        "!pip install tensorflow-gpu==2.0.0.alpha0"
      ],
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      "outputs": [
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          "text": [
            "Collecting tensorflow-gpu==2.0.0.alpha0\n",
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            "\u001b[K     |████████████████████████████████| 419kB 49.4MB/s \n",
            "\u001b[?25hCollecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301\n",
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            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0.alpha0) (1.0.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0.alpha0) (46.1.3)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0.alpha0) (2.10.0)\n",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
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          "name": "stdout"
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      "metadata": {
        "id": "TRNp1Lx-7JF-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxRpHrDN7Jc5",
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          "height": 34
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        "outputId": "24a9ed35-229d-4edd-9c9d-a3ff6f655670"
      },
      "source": [
        "tf.__version__"
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      "execution_count": 8,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0-rc2'"
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          "execution_count": 8
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        "id": "GjRXdTPf7Uu_",
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          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "f590bd1e-57c2-465c-852c-707e3827e8fa"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
      ],
      "execution_count": 9,
      "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",
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            "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"
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "6BppJfUi8Bz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOCIscLR8Isr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b06c33b9-0cd4-43ad-b1bc-73d11b5de078"
      },
      "source": [
        "X_train = X_train.reshape(-1, 28*28)\n",
        "X_train.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
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          "height": 34
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        "outputId": "51a1f90e-6439-40f0-d914-c9a76653baf2"
      },
      "source": [
        "X_test = X_test.reshape(-1, 28*28)\n",
        "X_test.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 784)"
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          "metadata": {
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          "execution_count": 12
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      "metadata": {
        "id": "lZWo_UWF8QZt",
        "colab_type": "code",
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      "source": [
        "model = tf.keras.models.Sequential()\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=128, activation='relu', input_shape=(784, )))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Dense(units=10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPT5CYf483Q3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bP7Q25JC85Qs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "aa7c0faf-dd17-487a-8848-9a41849fce30"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIYdbGTV86HW",
        "colab_type": "code",
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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}