keras_cifar10_tpu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8xn5mZrKTLeZ"
   },
   "source": [
    "# CIFAR 10 Classification: TPU Version\n",
    "\n",
    "The goal of this notebook is to implement a simple conv net to classify CIFAR10 images using TPU and compare its training time against GPU on local workstation and Colab GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N2Ro0bSxTkg3"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from time import time\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4BOJ_WfFUrF4"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "Load CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "Ug4jYDExTpUT",
    "outputId": "e47c00ac-f2fc-4c44-e98f-23cf1b2a9e4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train_full shape: (50000, 32, 32, 3), y_train_full.shape: (50000, 1)\n",
      "x_test shape: (10000, 32, 32, 3), y_test.shape: (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import cifar10, mnist\n",
    "\n",
    "(x_train_full, y_train_full), (x_test, y_test) = cifar10.load_data()\n",
    "print('x_train_full shape: {}, y_train_full.shape: {}'\n",
    "      .format(x_train_full.shape, y_train_full.shape))\n",
    "print('x_test shape: {}, y_test.shape: {}'.format(x_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8cWnDaOj8TSL",
    "outputId": "fb782be6-930e-4c8b-aa0e-b918578d4f3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train_full shape: (50000,), y_test shape: (10000,)\n"
     ]
    }
   ],
   "source": [
    "y_train_full = y_train_full.reshape(y_train_full.shape[0],)\n",
    "y_test = y_test.reshape(y_test.shape[0],)\n",
    "print('y_train_full shape: {}, y_test shape: {}'\n",
    "      .format(y_train_full.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1ZWzIU8Ay4Fp",
    "outputId": "21e906b3-dbdf-42f2-da9a-e3ee2f4bc027"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (40000, 32, 32, 3), y_train: (40000,), x_val: (10000, 32, 32, 3), y_val: (10000,)\n"
     ]
    }
   ],
   "source": [
    "# create validation set\n",
    "split = 0.2\n",
    "x_train, x_val, y_train, y_val = train_test_split(\n",
    "    x_train_full, y_train_full, test_size=0.2, random_state=42)\n",
    "print('x_train: {}, y_train: {}, x_val: {}, y_val: {}'\n",
    "      .format(x_train.shape, y_train.shape, x_val.shape, y_val.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ly83Vy1aZz1G"
   },
   "source": [
    "Lets plot 25 random images to get some idea about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wee4eONuUyY_"
   },
   "outputs": [],
   "source": [
    "# pick 25 random images and plot\n",
    "idxs = np.random.randint(x_train.shape[0], size=25)\n",
    "images = x_train[idxs]\n",
    "labels = y_train[idxs]\n",
    "classnames = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 724
    },
    "colab_type": "code",
    "id": "oJ-BHT8WYLLs",
    "outputId": "a2ab28d7-18b5-4c2b-caea-bcf085a10227"
   },
   "outputs": [
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       "    <!-- frog -->\n",
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       "L 27.9375 46.4375 \n",
       "z\n",
       "\" id=\"LiberationSans-66\"/>\n",
       "    </defs>\n",
       "    <g style=\"fill:#262626;\" transform=\"translate(330.542683 218.340388)scale(0.12 -0.12)\">\n",
       "     <use xlink:href=\"#LiberationSans-66\"/>\n",
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       "     <use x=\"61.083984\" xlink:href=\"#LiberationSans-6f\"/>\n",
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       "  </g>\n",
       "  <g id=\"axes_15\">\n",
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y=\"-426.750043\"/>\n",
       "   </g>\n",
       "   <g id=\"text_24\">\n",
       "    <!-- airplane -->\n",
       "    <g style=\"fill:#262626;\" transform=\"translate(319.533621 420.784526)scale(0.12 -0.12)\">\n",
       "     <use xlink:href=\"#LiberationSans-61\"/>\n",
       "     <use x=\"55.615234\" xlink:href=\"#LiberationSans-69\"/>\n",
       "     <use x=\"77.832031\" xlink:href=\"#LiberationSans-72\"/>\n",
       "     <use x=\"111.132812\" xlink:href=\"#LiberationSans-70\"/>\n",
       "     <use x=\"166.748047\" xlink:href=\"#LiberationSans-6c\"/>\n",
       "     <use x=\"188.964844\" xlink:href=\"#LiberationSans-61\"/>\n",
       "     <use x=\"244.580078\" xlink:href=\"#LiberationSans-6e\"/>\n",
       "     <use x=\"300.195312\" xlink:href=\"#LiberationSans-65\"/>\n",
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       "   </g>\n",
       "  </g>\n",
       "  <g id=\"axes_25\">\n",
       "   <g clip-path=\"url(#p70d7df68f2)\">\n",
       "    <image height=\"77\" id=\"image6c9576c8ca\" transform=\"scale(1 -1)translate(0 -77)\" width=\"77\" x=\"394.756358\" xlink:href=\"data:image/png;base64,\n",
       "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\" y=\"-426.750043\"/>\n",
       "   </g>\n",
       "   <g id=\"text_25\">\n",
       "    <!-- ship -->\n",
       "    <g style=\"fill:#262626;\" transform=\"translate(422.232866 420.784526)scale(0.12 -0.12)\">\n",
       "     <use xlink:href=\"#LiberationSans-73\"/>\n",
       "     <use x=\"50\" xlink:href=\"#LiberationSans-68\"/>\n",
       "     <use x=\"105.615234\" xlink:href=\"#LiberationSans-69\"/>\n",
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       "    </g>\n",
       "   </g>\n",
       "  </g>\n",
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     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5,5, figsize=(8,9))\n",
    "for i, ax in enumerate(axes.flat):\n",
    "  ax.imshow(images[i])\n",
    "  ax.axis('off')\n",
    "  idx = labels[i]\n",
    "  ax.set_title(classnames[idx])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "btl1W-LnOiwL"
   },
   "outputs": [],
   "source": [
    "# Using Dataset\n",
    "# def scale(x, min_val=0.0, max_val=255.0):\n",
    "#   x = tf.to_float(x)\n",
    "#   return tf.div(tf.subtract(x, min_val), tf.subtract(max_val, min_val))\n",
    "\n",
    "# convert to dataset\n",
    "# train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
    "# train_ds = train_ds.map(lambda x, y: (scale(x), tf.one_hot(y, 10))).shuffle(10000)\n",
    "\n",
    "# test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
    "# test_ds = test_ds.map(lambda x, y: (scale(x), tf.one_hot(y, 10))).shuffle(10000)\n",
    "\n",
    "# def train_generator(batch_size):\n",
    "#   while True:\n",
    "#     images, labels = train_ds.batch(batch_size).make_one_shot_iterator().get_next()\n",
    "#     yield images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7Kc-LS3ncJOu"
   },
   "outputs": [],
   "source": [
    "def train_gen(batch_size):\n",
    "  while True:\n",
    "    offset = np.random.randint(0, x_train.shape[0] - batch_size)\n",
    "    yield x_train[offset:offset+batch_size], y_train[offset:offset + batch_size]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eorI22azaHv3"
   },
   "source": [
    "## Build a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 605
    },
    "colab_type": "code",
    "id": "rJUSzhMJQRo7",
    "outputId": "d516c49d-e396-4f0d-d048-52ef49faf89f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 10, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 10, 64)        18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 10, 10, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 3, 3, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 128)         73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 3, 3, 128)         512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 1, 1, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 111,946\n",
      "Trainable params: 111,498\n",
      "Non-trainable params: 448\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Conv2D(32, (3,3), padding='same', activation='relu', input_shape=(32,32,3)))\n",
    "model.add(tf.keras.layers.BatchNormalization())\n",
    "model.add(tf.keras.layers.MaxPooling2D(pool_size=(3,3)))\n",
    "model.add(tf.keras.layers.Conv2D(64, (3,3), padding='same', activation='relu'))\n",
    "model.add(tf.keras.layers.BatchNormalization())\n",
    "model.add(tf.keras.layers.MaxPooling2D(pool_size=(3,3)))\n",
    "model.add(tf.keras.layers.Conv2D(128, (3,3), padding='same', activation='relu'))\n",
    "model.add(tf.keras.layers.BatchNormalization())\n",
    "model.add(tf.keras.layers.MaxPooling2D(pool_size=(3,3)))\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.4))\n",
    "model.add(tf.keras.layers.Dense(10, activation='relu'))\n",
    "model.add(tf.keras.layers.Activation('softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Km4T-mEttdPM"
   },
   "source": [
    "## Train\n",
    "\n",
    "Train the model on TPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356
    },
    "colab_type": "code",
    "id": "gZ5yKcUvPXQD",
    "outputId": "e60bf24f-fb64-4c54-c027-8e177dfff223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Querying Tensorflow master (b'grpc://10.17.106.138:8470') for TPU system metadata.\n",
      "INFO:tensorflow:Found TPU system:\n",
      "INFO:tensorflow:*** Num TPU Cores: 8\n",
      "INFO:tensorflow:*** Num TPU Workers: 1\n",
      "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 3788293867414123363)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 14218655969421810422)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 5812111958003439941)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 8454891999339572241)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 15413725777721296706)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 1794556755405902195)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 8722551663338303265)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 10265242285712045992)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 83959706521883974)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 6395746279842848549)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 17317425135332706070)\n",
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 13109444320199115483)\n",
      "WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.\n",
      "INFO:tensorflow:Connecting to: b'grpc://10.17.106.138:8470'\n"
     ]
    }
   ],
   "source": [
    "tpu_model = tf.contrib.tpu.keras_to_tpu_model(\n",
    "    model,\n",
    "    strategy=tf.contrib.tpu.TPUDistributionStrategy(\n",
    "        tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l3K2npOZB0N_"
   },
   "outputs": [],
   "source": [
    "tpu_model.compile(\n",
    "    optimizer=tf.train.AdamOptimizer(learning_rate=1e-3, ),\n",
    "    loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
    "    metrics=['sparse_categorical_accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1179
    },
    "colab_type": "code",
    "id": "4kyKWJBGRVrk",
    "outputId": "941b602f-43b8-4983-b013-532c38a89dc7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "INFO:tensorflow:New input shapes; (re-)compiling: mode=train, [TensorSpec(shape=(128, 32, 32, 3), dtype=tf.float32, name='conv2d_input0'), TensorSpec(shape=(128, 1), dtype=tf.float32, name='activation_target0')]\n",
      "INFO:tensorflow:Overriding default placeholder.\n",
      "INFO:tensorflow:Remapping placeholder for conv2d_input\n",
      "INFO:tensorflow:Started compiling\n",
      "INFO:tensorflow:Finished compiling. Time elapsed: 2.0243589878082275 secs\n",
      "INFO:tensorflow:Setting weights on TPU model.\n",
      "39/40 [============================>.] - ETA: 0s - loss: 2.2382 - sparse_categorical_accuracy: 0.2308INFO:tensorflow:New input shapes; (re-)compiling: mode=eval, [TensorSpec(shape=(128, 32, 32, 3), dtype=tf.float32, name='conv2d_input0'), TensorSpec(shape=(128, 1), dtype=tf.float32, name='activation_target0')]\n",
      "INFO:tensorflow:Overriding default placeholder.\n",
      "INFO:tensorflow:Remapping placeholder for conv2d_input\n",
      "INFO:tensorflow:Started compiling\n",
      "INFO:tensorflow:Finished compiling. Time elapsed: 0.8057072162628174 secs\n",
      "INFO:tensorflow:New input shapes; (re-)compiling: mode=eval, [TensorSpec(shape=(98, 32, 32, 3), dtype=tf.float32, name='conv2d_input0'), TensorSpec(shape=(98, 1), dtype=tf.float32, name='activation_target0')]\n",
      "INFO:tensorflow:Overriding default placeholder.\n",
      "INFO:tensorflow:Remapping placeholder for conv2d_input\n",
      "INFO:tensorflow:Started compiling\n",
      "INFO:tensorflow:Finished compiling. Time elapsed: 0.7855441570281982 secs\n",
      "40/40 [==============================] - 12s 301ms/step - loss: 2.2341 - sparse_categorical_accuracy: 0.2316 - val_loss: 2.4674 - val_sparse_categorical_accuracy: 0.1320\n",
      "Epoch 2/25\n",
      "40/40 [==============================] - 5s 127ms/step - loss: 1.8243 - sparse_categorical_accuracy: 0.3746 - val_loss: 2.0508 - val_sparse_categorical_accuracy: 0.2984\n",
      "Epoch 3/25\n",
      "40/40 [==============================] - 5s 127ms/step - loss: 1.5533 - sparse_categorical_accuracy: 0.4785 - val_loss: 1.7824 - val_sparse_categorical_accuracy: 0.3864\n",
      "Epoch 4/25\n",
      "40/40 [==============================] - 5s 127ms/step - loss: 1.3299 - sparse_categorical_accuracy: 0.5449 - val_loss: 1.4432 - val_sparse_categorical_accuracy: 0.4952\n",
      "Epoch 5/25\n",
      "40/40 [==============================] - 5s 128ms/step - loss: 1.1259 - sparse_categorical_accuracy: 0.6107 - val_loss: 1.3337 - val_sparse_categorical_accuracy: 0.5280\n",
      "Epoch 6/25\n",
      "40/40 [==============================] - 5s 135ms/step - loss: 1.0812 - sparse_categorical_accuracy: 0.6299 - val_loss: 1.1240 - val_sparse_categorical_accuracy: 0.5944\n",
      "Epoch 7/25\n",
      "40/40 [==============================] - 5s 130ms/step - loss: 0.9903 - sparse_categorical_accuracy: 0.6594 - val_loss: 1.1736 - val_sparse_categorical_accuracy: 0.6144\n",
      "Epoch 8/25\n",
      "40/40 [==============================] - 5s 128ms/step - loss: 0.8811 - sparse_categorical_accuracy: 0.7027 - val_loss: 1.0815 - val_sparse_categorical_accuracy: 0.6128\n",
      "Epoch 9/25\n",
      "40/40 [==============================] - 5s 127ms/step - loss: 0.8537 - sparse_categorical_accuracy: 0.7152 - val_loss: 1.0205 - val_sparse_categorical_accuracy: 0.6336\n",
      "Epoch 10/25\n",
      "40/40 [==============================] - 5s 125ms/step - loss: 0.8123 - sparse_categorical_accuracy: 0.7209 - val_loss: 0.9942 - val_sparse_categorical_accuracy: 0.6600\n",
      "Epoch 11/25\n",
      "40/40 [==============================] - 5s 129ms/step - loss: 0.7017 - sparse_categorical_accuracy: 0.7621 - val_loss: 0.9644 - val_sparse_categorical_accuracy: 0.6736\n",
      "Epoch 12/25\n",
      "40/40 [==============================] - 5s 128ms/step - loss: 0.7147 - sparse_categorical_accuracy: 0.7508 - val_loss: 0.9652 - val_sparse_categorical_accuracy: 0.6736\n",
      "Epoch 13/25\n",
      "40/40 [==============================] - 5s 130ms/step - loss: 0.6518 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.9552 - val_sparse_categorical_accuracy: 0.6824\n",
      "Epoch 14/25\n",
      "40/40 [==============================] - 5s 131ms/step - loss: 0.6311 - sparse_categorical_accuracy: 0.7818 - val_loss: 0.9865 - val_sparse_categorical_accuracy: 0.6712\n",
      "Epoch 15/25\n",
      "40/40 [==============================] - 5s 130ms/step - loss: 0.5919 - sparse_categorical_accuracy: 0.8002 - val_loss: 0.9918 - val_sparse_categorical_accuracy: 0.6752\n",
      "Epoch 16/25\n",
      "40/40 [==============================] - 5s 130ms/step - loss: 0.5601 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.9994 - val_sparse_categorical_accuracy: 0.6704\n",
      "Epoch 17/25\n",
      "40/40 [==============================] - 5s 130ms/step - loss: 0.5251 - sparse_categorical_accuracy: 0.8207 - val_loss: 0.9576 - val_sparse_categorical_accuracy: 0.6880\n",
      "Epoch 18/25\n",
      "40/40 [==============================] - 5s 129ms/step - loss: 0.4831 - sparse_categorical_accuracy: 0.8295 - val_loss: 0.9664 - val_sparse_categorical_accuracy: 0.6856\n",
      "Epoch 19/25\n",
      "40/40 [==============================] - 5s 124ms/step - loss: 0.4863 - sparse_categorical_accuracy: 0.8359 - val_loss: 1.0340 - val_sparse_categorical_accuracy: 0.6792\n",
      "Epoch 20/25\n",
      "40/40 [==============================] - 5s 128ms/step - loss: 0.4150 - sparse_categorical_accuracy: 0.8586 - val_loss: 1.0684 - val_sparse_categorical_accuracy: 0.6800\n",
      "Epoch 21/25\n",
      "40/40 [==============================] - 5s 132ms/step - loss: 0.4162 - sparse_categorical_accuracy: 0.8598 - val_loss: 1.0896 - val_sparse_categorical_accuracy: 0.6904\n",
      "Epoch 22/25\n",
      "40/40 [==============================] - 5s 129ms/step - loss: 0.4304 - sparse_categorical_accuracy: 0.8521 - val_loss: 1.2943 - val_sparse_categorical_accuracy: 0.6520\n",
      "Epoch 23/25\n",
      "40/40 [==============================] - 5s 129ms/step - loss: 0.3524 - sparse_categorical_accuracy: 0.8830 - val_loss: 1.1612 - val_sparse_categorical_accuracy: 0.6696\n",
      "Epoch 24/25\n",
      "40/40 [==============================] - 5s 125ms/step - loss: 0.3443 - sparse_categorical_accuracy: 0.8826 - val_loss: 1.1580 - val_sparse_categorical_accuracy: 0.6760\n",
      "Epoch 25/25\n",
      "40/40 [==============================] - 5s 128ms/step - loss: 0.3706 - sparse_categorical_accuracy: 0.8742 - val_loss: 1.2354 - val_sparse_categorical_accuracy: 0.6776\n",
      "CPU times: user 28 s, sys: 10.6 s, total: 38.6 s\n",
      "Wall time: 2min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "batch_size = 1024\n",
    "history = tpu_model.fit_generator(\n",
    "    train_gen(batch_size), epochs=25, \n",
    "    steps_per_epoch=np.ceil(x_train.shape[0]/batch_size),\n",
    "    validation_data = (x_val, y_val),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D7_JyM0DMjyb"
   },
   "source": [
    "### Plot Loss and Accuracy\n",
    "\n",
    "Lets see how did we do on training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GTvRJC1TGmW3"
   },
   "outputs": [],
   "source": [
    "def plot(losses, accuracies, subplot_title):\n",
    "  fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14,4))\n",
    "  \n",
    "  ax1.plot(losses)\n",
    "  ax1.set_xlabel('Epochs')\n",
    "  ax1.set_ylabel('Loss')\n",
    "  ax1.set_title(subplot_title[0])\n",
    "  \n",
    "  ax2.plot(accuracies)\n",
    "  ax2.set_xlabel('Epochs')\n",
    "  ax2.set_ylabel('Accuracy')\n",
    "  ax2.set_title(subplot_title[1])\n",
    "    \n",
    "  plt.show()"
   ]
  },
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   "execution_count": 16,
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    "id": "QwX0X8K03g4Z",
    "outputId": "df06f546-577f-45a1-e9b4-927d009e51e3"
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    "# Training\n",
    "plot(history.history['loss'], \n",
    "     history.history['sparse_categorical_accuracy'], \n",
    "     subplot_title=['Training Loss', 'Training Accuracy']\n",
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   "source": [
    "# Validation\n",
    "plot(history.history['val_loss'], \n",
    "     history.history['val_sparse_categorical_accuracy'], \n",
    "     subplot_title=['Validation Loss', 'Validation Accuracy']\n",
    "    )"
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  {
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   "source": [
    "## Test accuracy\n",
    "\n",
    "Next, we plot the model predictions on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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    "colab_type": "code",
    "id": "nBOxCSu9u0lF",
    "outputId": "8714875e-69e6-47bb-c8b9-6008b1fd3eb0"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Copying TPU weights to the CPU\n"
     ]
    }
   ],
   "source": [
    "cpu_model = tpu_model.sync_to_cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BafyMADD4qAY"
   },
   "outputs": [],
   "source": [
    "idxs = np.random.randint(x_test.shape[0], size=25)\n",
    "images = x_test[idxs]\n",
    "true_labels = y_test[idxs]\n",
    "\n",
    "preds = np.argmax(cpu_model.predict(images), axis=1)"
   ]
  },
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   "execution_count": 20,
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    "id": "9upOrU015Myg",
    "outputId": "26f9e36a-ce49-4632-dd92-1289e16607f6"
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       "   </g>\n",
       "   <g id=\"text_10\">\n",
       "    <!-- bird -->\n",
       "    <g style=\"fill:#ff0000;\" transform=\"translate(423.235054 117.118319)scale(0.12 -0.12)\">\n",
       "     <use xlink:href=\"#LiberationSans-62\"/>\n",
       "     <use x=\"55.615234\" xlink:href=\"#LiberationSans-69\"/>\n",
       "     <use x=\"77.832031\" xlink:href=\"#LiberationSans-72\"/>\n",
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       "   <g clip-path=\"url(#peb5455846c)\">\n",
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   ],
   "source": [
    "fig, axes = plt.subplots(5,5, figsize=(8,9))\n",
    "for i, ax in enumerate(axes.flat):\n",
    "  ax.imshow(images[i])\n",
    "  ax.axis('off')\n",
    "  idx = preds[i]\n",
    "  color = 'g' if idx == true_labels[i] else 'r'\n",
    "  ax.set_title(classnames[idx], color=color)\n",
    "\n",
    "plt.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Now that we've got the basic model working, you can try improving its accuracy. \n",
    "For example, you can try :\n",
    "* preprocessing\n",
    "* transfer learning\n",
    "* learning rates using learning rate finder\n",
    "* different network architecture\n",
    "\n",
    "\n",
    "You can try to match/beat the CIFAR10 accuracy listed on this <a href=\"http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130\">page</a>."
   ]
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   "execution_count": 0,
   "metadata": {
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    "colab_type": "code",
    "id": "OWMepxya5PEN"
   },
   "outputs": [],
   "source": []
  }
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