Fashion_Code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MB81u75it3cx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "16e22df2-3b94-43d9-f91b-9c9ce59c5053"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "import distutils\n",
        "\n",
        "if distutils.version.LooseVersion(tf.__version__) < '1.14':\n",
        "    raise Exception('This notebook is compatible with TensorFlow 1.14 or higher, for TensorFlow 1.13 or lower please use the previous version at https://github.com/tensorflow/tpu/blob/r1.13/tools/colab/fashion_mnist.ipynb')\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "# add empty color dimension\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "\n",
        "\n",
        "def create_model():\n",
        "    model = tf.keras.models.Sequential()\n",
        "    model.add(tf.keras.layers.BatchNormalization(input_shape=x_train.shape[1:]))\n",
        "    model.add(tf.keras.layers.Conv2D(64, (5, 5), padding='same', activation='elu'))\n",
        "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2,2)))\n",
        "    model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "    model.add(tf.keras.layers.BatchNormalization(input_shape=x_train.shape[1:]))\n",
        "    model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', activation='elu'))\n",
        "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "    model.add(tf.keras.layers.BatchNormalization(input_shape=x_train.shape[1:]))\n",
        "    model.add(tf.keras.layers.Conv2D(256, (5, 5), padding='same', activation='elu'))\n",
        "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2,2)))\n",
        "    model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(256))\n",
        "    model.add(tf.keras.layers.Activation('elu'))\n",
        "    model.add(tf.keras.layers.Dropout(0.5))\n",
        "    model.add(tf.keras.layers.Dense(10))\n",
        "    model.add(tf.keras.layers.Activation('softmax'))\n",
        "    return model\n",
        "\n",
        "\n",
        "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
        "tf.config.experimental_connect_to_cluster(resolver)\n",
        "tf.tpu.experimental.initialize_tpu_system(resolver)\n",
        "strategy = tf.distribute.experimental.TPUStrategy(resolver)\n",
        "\n",
        "with strategy.scope():\n",
        "  model = create_model()\n",
        "  model.compile(\n",
        "      optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3, ),\n",
        "      loss='sparse_categorical_crossentropy',\n",
        "      metrics=['sparse_categorical_accuracy'])\n",
        "\n",
        "model.fit(\n",
        "    x_train.astype(np.float32), y_train.astype(np.float32),\n",
        "    epochs=17,\n",
        "    steps_per_epoch=60,\n",
        "    validation_data=(x_test.astype(np.float32), y_test.astype(np.float32)),\n",
        "    validation_freq=17\n",
        ")\n",
        "\n",
        "model.save_weights('./fashion_mnist.h5', overwrite=True)\n",
        "LABEL_NAMES = ['t_shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle_boots']\n",
        "\n",
        "cpu_model = create_model()\n",
        "cpu_model.load_weights('./fashion_mnist.h5')\n",
        "\n",
        "from matplotlib import pyplot\n",
        "\n",
        "def plot_predictions(images, predictions):\n",
        "    n = images.shape[0]\n",
        "    nc = int(np.ceil(n / 4))\n",
        "    f, axes = pyplot.subplots(nc, 4)\n",
        "    for i in range(nc * 4):\n",
        "        y = i // 4\n",
        "        x = i % 4\n",
        "        axes[x, y].axis('off')\n",
        "\n",
        "        label = LABEL_NAMES[np.argmax(predictions[i])]\n",
        "        confidence = np.max(predictions[i])\n",
        "        if i > n:\n",
        "            continue\n",
        "        axes[x, y].imshow(images[i])\n",
        "        axes[x, y].text(0.5, 0.5, label + '\\n%.3f' % confidence, fontsize=14)\n",
        "\n",
        "    pyplot.gcf().set_size_inches(8, 8)\n",
        "\n",
        "\n",
        "plot_predictions(np.squeeze(x_test[:16]),\n",
        "                 cpu_model.predict(x_test[:16]))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.110.101.178:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.110.101.178:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.110.101.178:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.110.101.178:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "WARNING:absl:`tf.distribute.experimental.TPUStrategy` is deprecated, please use  the non experimental symbol `tf.distribute.TPUStrategy` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
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          "name": "stderr",
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
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        },
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        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
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        },
        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
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        },
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
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        },
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/17\n",
            "60/60 [==============================] - 7s 22ms/step - loss: 1.1921 - sparse_categorical_accuracy: 0.6726\n",
            "Epoch 2/17\n",
            "60/60 [==============================] - 1s 22ms/step - loss: 0.5334 - sparse_categorical_accuracy: 0.8179\n",
            "Epoch 3/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.4439 - sparse_categorical_accuracy: 0.8484\n",
            "Epoch 4/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.3802 - sparse_categorical_accuracy: 0.8660\n",
            "Epoch 5/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.3434 - sparse_categorical_accuracy: 0.8782\n",
            "Epoch 6/17\n",
            "60/60 [==============================] - 1s 22ms/step - loss: 0.3180 - sparse_categorical_accuracy: 0.8859\n",
            "Epoch 7/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.2865 - sparse_categorical_accuracy: 0.8971\n",
            "Epoch 8/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.2702 - sparse_categorical_accuracy: 0.9010\n",
            "Epoch 9/17\n",
            "60/60 [==============================] - 1s 22ms/step - loss: 0.2499 - sparse_categorical_accuracy: 0.9097\n",
            "Epoch 10/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.2343 - sparse_categorical_accuracy: 0.9146\n",
            "Epoch 11/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.2219 - sparse_categorical_accuracy: 0.9181\n",
            "Epoch 12/17\n",
            "60/60 [==============================] - 1s 22ms/step - loss: 0.2104 - sparse_categorical_accuracy: 0.9222\n",
            "Epoch 13/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.1987 - sparse_categorical_accuracy: 0.9270\n",
            "Epoch 14/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.1862 - sparse_categorical_accuracy: 0.9311\n",
            "Epoch 15/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.1749 - sparse_categorical_accuracy: 0.9351\n",
            "Epoch 16/17\n",
            "60/60 [==============================] - 1s 21ms/step - loss: 0.1649 - sparse_categorical_accuracy: 0.9390\n",
            "Epoch 17/17\n",
            "60/60 [==============================] - 7s 117ms/step - loss: 0.1573 - sparse_categorical_accuracy: 0.9412 - val_loss: 0.2315 - val_sparse_categorical_accuracy: 0.9222\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}