Keras_MNIST_TPU_(public).ipynb

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      "name": "Keras MNIST TPU (public).ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xqLjB2cy5S7m"
      },
      "source": [
        "## MNIST on TPU (Tensor Processing Unit)<br>or GPU using tf.Keras and tf.data.Dataset\n",
        "<table><tr><td><img valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/keras-tensorflow-tpu300px.png\"   width=\"300\" alt=\"Keras+Tensorflow+Cloud TPU\"></td></tr></table>\n",
        "\n",
        "\n",
        "This sample trains an \"MNIST\" handwritten digit \n",
        "recognition model on a GPU or TPU backend using a Keras\n",
        "model. Data are handled using the tf.data.Datset API. This is\n",
        "a very simple sample provided for educational purposes. Do\n",
        "not expect outstanding TPU performance on a dataset as\n",
        "small as MNIST.\n",
        "\n",
        "<h3><a href=\"https://cloud.google.com/gpu/\"><img valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/gpu-hexagon.png\" width=\"50\"></a>  &nbsp;&nbsp;Train on GPU or TPU&nbsp;&nbsp; <a href=\"https://cloud.google.com/tpu/\"><img valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/tpu-hexagon.png\" width=\"50\"></a></h3>\n",
        "\n",
        "  1. Select a GPU or TPU backend (Runtime > Change runtime type) \n",
        "  1. Run all cells up to and including \"Train and validate the model\" and \"Visualize predictions\".\n",
        "\n",
        "<h3><a href=\"https://cloud.google.com/ml-engine/\"><img valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/mlengine-hexagon.png\" width=\"50\"></a>  &nbsp;&nbsp;Deploy to AI Platform</h3>\n",
        "\n",
        "  1. Configure a Google cloud project and bucket as well as the desired model name in \"Deploy the trained model\".\n",
        "  1. Run the remaining cells to the end to deploy your model to Cloud AI Platform Prediction and test the deployment.\n",
        "\n",
        "TPUs are located in Google Cloud, for optimal performance, they read data directly from Google Cloud Storage (GCS)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AoilhmYe1b5t",
        "outputId": "7f2db31a-1ee8-473b-cf76-b3444b554ec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os, re, time, json\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version 2.2.0-rc3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "qhdz68Xm3Z4Z",
        "colab": {}
      },
      "source": [
        "#@title visualization utilities [RUN ME]\n",
        "\"\"\"\n",
        "This cell contains helper functions used for visualization\n",
        "and downloads only. You can skip reading it. There is very\n",
        "little useful Keras/Tensorflow code here.\n",
        "\"\"\"\n",
        "\n",
        "# Matplotlib config\n",
        "plt.rc('image', cmap='gray_r')\n",
        "plt.rc('grid', linewidth=0)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "\n",
        "# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
        "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
        "  \n",
        "  # get one batch from each: 10000 validation digits, N training digits\n",
        "  batch_train_ds = training_dataset.unbatch().batch(N)\n",
        "  \n",
        "  # eager execution: loop through datasets normally\n",
        "  for validation_digits, validation_labels in validation_dataset:\n",
        "    validation_digits = validation_digits.numpy()\n",
        "    validation_labels = validation_labels.numpy()\n",
        "    break\n",
        "  for training_digits, training_labels in batch_train_ds:\n",
        "    training_digits = training_digits.numpy()\n",
        "    training_labels = training_labels.numpy()\n",
        "    break\n",
        "  \n",
        "  # these were one-hot encoded in the dataset\n",
        "  validation_labels = np.argmax(validation_labels, axis=1)\n",
        "  training_labels = np.argmax(training_labels, axis=1)\n",
        "  \n",
        "  return (training_digits, training_labels,\n",
        "          validation_digits, validation_labels)\n",
        "\n",
        "# create digits from local fonts for testing\n",
        "def create_digits_from_local_fonts(n):\n",
        "  font_labels = []\n",
        "  img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
        "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
        "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
        "  d = PIL.ImageDraw.Draw(img)\n",
        "  for i in range(n):\n",
        "    font_labels.append(i%10)\n",
        "    d.text((7+i*28,0 if i<10 else -4), str(i%10), fill=(255,255), font=font1 if i<10 else font2)\n",
        "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
        "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
        "  return font_digits, font_labels\n",
        "\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_digits(digits, predictions, labels, title, n):\n",
        "  plt.figure(figsize=(13,3))\n",
        "  digits = np.reshape(digits, [n, 28, 28])\n",
        "  digits = np.swapaxes(digits, 0, 1)\n",
        "  digits = np.reshape(digits, [28, 28*n])\n",
        "  plt.yticks([])\n",
        "  plt.xticks([28*x+14 for x in range(n)], predictions)\n",
        "  for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
        "    if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
        "  plt.imshow(digits)\n",
        "  plt.grid(None)\n",
        "  plt.title(title)\n",
        "  \n",
        "# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
        "def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
        "  idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
        "  for i in range(lines):\n",
        "    display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
        "                   \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)\n",
        "    \n",
        "# utility to display training and validation curves\n",
        "def display_training_curves(training, validation, title, subplot):\n",
        "  if subplot%10==1: # set up the subplots on the first call\n",
        "    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
        "    plt.tight_layout()\n",
        "  ax = plt.subplot(subplot)\n",
        "  ax.grid(linewidth=1, color='white')\n",
        "  ax.plot(training)\n",
        "  ax.plot(validation)\n",
        "  ax.set_title('model '+ title)\n",
        "  ax.set_ylabel(title)\n",
        "  ax.set_xlabel('epoch')\n",
        "  ax.legend(['train', 'valid.'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VE6zp3q_HNTi",
        "colab_type": "text"
      },
      "source": [
        "*(you can double-ckick on collapsed cells to view the non-essential code inside)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4jujVYWY9-6",
        "colab_type": "text"
      },
      "source": [
        "### TPU or GPU detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hd5zB1G7Y9-7",
        "colab_type": "code",
        "outputId": "26d4e608-c635-43ad-d058-180d1b60d6f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        }
      },
      "source": [
        "# Detect hardware\n",
        "try:\n",
        "  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection\n",
        "except ValueError:\n",
        "  tpu = None\n",
        "  gpus = tf.config.experimental.list_logical_devices(\"GPU\")\n",
        "    \n",
        "# Select appropriate distribution strategy\n",
        "if tpu:\n",
        "  tf.config.experimental_connect_to_cluster(tpu)\n",
        "  tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "  strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "  print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])  \n",
        "elif len(gpus) > 1:\n",
        "  strategy = tf.distribute.MirroredStrategy(gpus)\n",
        "  print('Running on multiple GPUs ', [gpu.name for gpu in gpus])\n",
        "elif len(gpus) == 1:\n",
        "  strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
        "  print('Running on single GPU ', gpus[0].name)\n",
        "else:\n",
        "  strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
        "  print('Running on CPU')\n",
        "print(\"Number of accelerators: \", strategy.num_replicas_in_sync)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.65.180.114:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:TPU system grpc://10.65.180.114:8470 has already been initialized. Reinitializing the TPU can cause previously created variables on TPU to be lost.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.65.180.114:8470\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.65.180.114:8470\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running on TPU  ['10.65.180.114:8470']\n",
            "Number of accelerators:  8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lvo0t7XVIkWZ"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cCpkS9C_H7Tl",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 64 * strategy.num_replicas_in_sync # Gobal batch size.\n",
        "# The global batch size will be automatically sharded across all\n",
        "# replicas by the tf.data.Dataset API. A single TPU has 8 cores.\n",
        "# The best practice is to scale the batch size by the number of\n",
        "# replicas (cores). The learning rate should be increased as well.\n",
        "\n",
        "LEARNING_RATE = 0.01\n",
        "LEARNING_RATE_EXP_DECAY = 0.6 if strategy.num_replicas_in_sync == 1 else 0.7\n",
        "# Learning rate computed later as LEARNING_RATE * LEARNING_RATE_EXP_DECAY**epoch\n",
        "# 0.7 decay instead of 0.6 means a slower decay, i.e. a faster learnign rate.\n",
        "\n",
        "training_images_file   = 'gs://mnist-public/train-images-idx3-ubyte'\n",
        "training_labels_file   = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
        "validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
        "validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lz1Zknfk4qCx"
      },
      "source": [
        "### tf.data.Dataset: parse files and prepare training and validation datasets\n",
        "Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZE8dgyPC1_6m",
        "colab": {}
      },
      "source": [
        "def read_label(tf_bytestring):\n",
        "    label = tf.io.decode_raw(tf_bytestring, tf.uint8)\n",
        "    label = tf.reshape(label, [])\n",
        "    label = tf.one_hot(label, 10)\n",
        "    return label\n",
        "  \n",
        "def read_image(tf_bytestring):\n",
        "    image = tf.io.decode_raw(tf_bytestring, tf.uint8)\n",
        "    image = tf.cast(image, tf.float32)/256.0\n",
        "    image = tf.reshape(image, [28*28])\n",
        "    return image\n",
        "  \n",
        "def load_dataset(image_file, label_file):\n",
        "    imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
        "    imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
        "    labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
        "    labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
        "    dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
        "    return dataset \n",
        "  \n",
        "def get_training_dataset(image_file, label_file, batch_size):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache()  # this small dataset can be entirely cached in RAM\n",
        "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
        "    dataset = dataset.repeat()\n",
        "    dataset = dataset.batch(batch_size)\n",
        "    dataset = dataset.prefetch(-1)  # fetch next batches while training on the current one (-1: autotune prefetch buffer size)\n",
        "    return dataset\n",
        "  \n",
        "def get_validation_dataset(image_file, label_file):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache() # this small dataset can be entirely cached in RAM\n",
        "    dataset = dataset.batch(10000)\n",
        "    return dataset\n",
        "\n",
        "# instantiate the datasets\n",
        "training_dataset = get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_fXo6GuvL3EB"
      },
      "source": [
        "### Let's have a look at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yZ4tjPKvL2eh",
        "outputId": "ff4c9642-bf9f-4006-b5ce-c6b2ed15a1db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "N = 24\n",
        "(training_digits, training_labels,\n",
        " validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
        "display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
        "display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
        "font_digits, font_labels = create_digits_from_local_fonts(N)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KIc0oqiD40HC"
      },
      "source": [
        "### Keras model: 3 convolutional layers, 2 dense layers\n",
        "If you are not sure what cross-entropy, dropout, softmax or batch-normalization mean, head here for a crash-course: [Tensorflow and deep learning without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/#featured-code-sample)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "56y8UNFQIVwj",
        "outputId": "506bd7c8-2238-42fd-8830-5b9d25ae0e5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "# This model trains to 99.4% accuracy in 10 epochs (with a batch size of 64)  \n",
        "\n",
        "def make_model():\n",
        "    model = tf.keras.Sequential(\n",
        "      [\n",
        "        tf.keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1), name=\"image\"),\n",
        "\n",
        "        tf.keras.layers.Conv2D(filters=12, kernel_size=3, padding='same', use_bias=False), # no bias necessary before batch norm\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True), # no batch norm scaling necessary before \"relu\"\n",
        "        tf.keras.layers.Activation('relu'), # activation after batch norm\n",
        "\n",
        "        tf.keras.layers.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
        "        tf.keras.layers.Activation('relu'),\n",
        "\n",
        "        tf.keras.layers.Conv2D(filters=32, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
        "        tf.keras.layers.Activation('relu'),\n",
        "\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(200, use_bias=False),\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
        "        tf.keras.layers.Activation('relu'),\n",
        "        tf.keras.layers.Dropout(0.4), # Dropout on dense layer only\n",
        "\n",
        "        tf.keras.layers.Dense(10, activation='softmax')\n",
        "      ])\n",
        "\n",
        "    model.compile(optimizer='adam', # learning rate will be set by LearningRateScheduler\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "     # Going back and forth between TPU and host is expensive. Better to run 128 batches on the TPU before reporting back.\n",
        "    return model\n",
        "    \n",
        "with strategy.scope():\n",
        "    model = make_model()\n",
        "\n",
        "# print model layers\n",
        "model.summary()\n",
        "\n",
        "# set up learning rate decay\n",
        "lr_decay = tf.keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: LEARNING_RATE * LEARNING_RATE_EXP_DECAY**epoch,\n",
        "    verbose=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "image (Reshape)              (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 28, 28, 12)        108       \n",
            "_________________________________________________________________\n",
            "batch_normalization_20 (Batc (None, 28, 28, 12)        36        \n",
            "_________________________________________________________________\n",
            "activation_20 (Activation)   (None, 28, 28, 12)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 14, 14, 24)        10368     \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 14, 14, 24)        72        \n",
            "_________________________________________________________________\n",
            "activation_21 (Activation)   (None, 14, 14, 24)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 7, 7, 32)          27648     \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 7, 7, 32)          96        \n",
            "_________________________________________________________________\n",
            "activation_22 (Activation)   (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 200)               313600    \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 200)               600       \n",
            "_________________________________________________________________\n",
            "activation_23 (Activation)   (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                2010      \n",
            "=================================================================\n",
            "Total params: 354,538\n",
            "Trainable params: 354,002\n",
            "Non-trainable params: 536\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CuhDh8ao8VyB"
      },
      "source": [
        "### Train and validate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TTwH_P-ZJ_xx",
        "outputId": "daf34d0a-5832-4585-963a-d8e9df60d065",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        }
      },
      "source": [
        "EPOCHS = 10\n",
        "steps_per_epoch = 60000//BATCH_SIZE  # 60,000 items in this dataset\n",
        "print(\"Steps per epoch: \", steps_per_epoch)\n",
        "  \n",
        "history = model.fit(training_dataset,\n",
        "                    steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
        "                    validation_data=validation_dataset, validation_steps=1,\n",
        "                    callbacks=[lr_decay])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Steps per epoch:  117\n",
            "\n",
            "Epoch 00001: LearningRateScheduler reducing learning rate to 0.01.\n",
            "Epoch 1/10\n",
            "117/117 [==============================] - 4s 35ms/step - accuracy: 0.9467 - loss: 0.1770 - val_accuracy: 0.3019 - val_loss: 3.4094 - lr: 0.0100\n",
            "\n",
            "Epoch 00002: LearningRateScheduler reducing learning rate to 0.006999999999999999.\n",
            "Epoch 2/10\n",
            "117/117 [==============================] - 3s 24ms/step - accuracy: 0.9845 - loss: 0.0493 - val_accuracy: 0.4960 - val_loss: 1.6974 - lr: 0.0070\n",
            "\n",
            "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0049.\n",
            "Epoch 3/10\n",
            "117/117 [==============================] - 3s 28ms/step - accuracy: 0.9892 - loss: 0.0338 - val_accuracy: 0.8752 - val_loss: 0.3548 - lr: 0.0049\n",
            "\n",
            "Epoch 00004: LearningRateScheduler reducing learning rate to 0.003429999999999999.\n",
            "Epoch 4/10\n",
            "117/117 [==============================] - 3s 24ms/step - accuracy: 0.9927 - loss: 0.0240 - val_accuracy: 0.9619 - val_loss: 0.1147 - lr: 0.0034\n",
            "\n",
            "Epoch 00005: LearningRateScheduler reducing learning rate to 0.0024009999999999995.\n",
            "Epoch 5/10\n",
            "117/117 [==============================] - ETA: 0s - accuracy: 0.9950 - loss: 0.0170WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 3s 24ms/step - accuracy: 0.9950 - loss: 0.0170 - val_accuracy: 0.9873 - val_loss: 0.0412 - lr: 0.0024\n",
            "\n",
            "Epoch 00006: LearningRateScheduler reducing learning rate to 0.0016806999999999994.\n",
            "Epoch 6/10\n",
            "114/117 [============================>.] - ETA: 0s - accuracy: 0.9961 - loss: 0.0132WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 3s 23ms/step - accuracy: 0.9961 - loss: 0.0130 - val_accuracy: 0.9915 - val_loss: 0.0253 - lr: 0.0017\n",
            "\n",
            "Epoch 00007: LearningRateScheduler reducing learning rate to 0.0011764899999999997.\n",
            "Epoch 7/10\n",
            "114/117 [============================>.] - ETA: 0s - accuracy: 0.9967 - loss: 0.0110WARNING:tensorflow:7 out of the last 7 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:7 out of the last 7 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 3s 24ms/step - accuracy: 0.9967 - loss: 0.0109 - val_accuracy: 0.9929 - val_loss: 0.0225 - lr: 0.0012\n",
            "\n",
            "Epoch 00008: LearningRateScheduler reducing learning rate to 0.0008235429999999996.\n",
            "Epoch 8/10\n",
            "117/117 [==============================] - ETA: 0s - accuracy: 0.9977 - loss: 0.0084WARNING:tensorflow:8 out of the last 8 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:8 out of the last 8 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 3s 23ms/step - accuracy: 0.9977 - loss: 0.0084 - val_accuracy: 0.9935 - val_loss: 0.0203 - lr: 8.2354e-04\n",
            "\n",
            "Epoch 00009: LearningRateScheduler reducing learning rate to 0.0005764800999999997.\n",
            "Epoch 9/10\n",
            "117/117 [==============================] - ETA: 0s - accuracy: 0.9982 - loss: 0.0076WARNING:tensorflow:9 out of the last 9 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:9 out of the last 9 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 3s 24ms/step - accuracy: 0.9982 - loss: 0.0076 - val_accuracy: 0.9935 - val_loss: 0.0199 - lr: 5.7648e-04\n",
            "\n",
            "Epoch 00010: LearningRateScheduler reducing learning rate to 0.0004035360699999998.\n",
            "Epoch 10/10\n",
            "117/117 [==============================] - ETA: 0s - accuracy: 0.9985 - loss: 0.0064WARNING:tensorflow:10 out of the last 10 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:10 out of the last 10 calls to <function Model.make_test_function.<locals>.test_function at 0x7f077125e9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r117/117 [==============================] - 3s 24ms/step - accuracy: 0.9985 - loss: 0.0064 - val_accuracy: 0.9937 - val_loss: 0.0199 - lr: 4.0354e-04\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w12OId8Mz7dF",
        "outputId": "c2a56d02-53ff-4352-9fc2-bed7e48f35b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "# recognize digits from local fonts\n",
        "probabilities = model.predict(font_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_digits(font_digits, predicted_labels, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
        "\n",
        "# recognize validation digits\n",
        "probabilities = model.predict(validation_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5tzVi39ShrEL"
      },
      "source": [
        "## Deploy the trained model to AI Platform prediction\n",
        "\n",
        "Push your trained model to production on AI Platform for a serverless, autoscaled, REST API experience.\n",
        "\n",
        "You will need a GCS (Google Cloud Storage) bucket and a GCP project for this.\n",
        "Models deployed on AI Platform autoscale to zero if not used. There will be no AI Platform charges after you are done testing.\n",
        "Google Cloud Storage incurs charges. Empty the bucket after deployment if you want to avoid these. Once the model is deployed, the bucket is not useful anymore."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3Y3ztMY_toCP"
      },
      "source": [
        "### Configuration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "colab_type": "code",
        "id": "iAZAn7yIhqAS",
        "colab": {}
      },
      "source": [
        "PROJECT = \"\" #@param {type:\"string\"}\n",
        "BUCKET = \"gs://\"  #@param {type:\"string\", default:\"jddj\"}\n",
        "NEW_MODEL = True #@param {type:\"boolean\"}\n",
        "MODEL_NAME = \"mnist\" #@param {type:\"string\"}\n",
        "MODEL_VERSION = \"v1\" #@param {type:\"string\"}\n",
        "\n",
        "assert PROJECT, 'For this part, you need a GCP project. Head to http://console.cloud.google.com/ and create one.'\n",
        "assert re.search(r'gs://.+', BUCKET), 'For this part, you need a GCS bucket. Head to http://console.cloud.google.com/storage and create one.'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lzd6Qi464PsA"
      },
      "source": [
        "### Colab-only auth"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "colab_type": "code",
        "id": "MPx0nvyUnvgT",
        "colab": {}
      },
      "source": [
        "IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ  # this is always set on Colab, the value is 0 or 1 depending on GPU presence\n",
        "if IS_COLAB_BACKEND:\n",
        "  from google.colab import auth\n",
        "  auth.authenticate_user() # Authenticates the Colab machine to access your private GCS buckets."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GxQTtjmdIbmN"
      },
      "source": [
        "### Export the model for serving from AI Platform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GOgh7Kb7SzzG",
        "colab": {}
      },
      "source": [
        "export_path = os.path.join(BUCKET, 'keras_export', str(time.time()))\n",
        "\n",
        "# The serving function performig data pre- and post-processing.\n",
        "# The model itself is captured  by this function by closure.\n",
        "# Pre-processing:  images are received in uint8 format converted\n",
        "#                  to float32 before being sent to through the model.\n",
        "# Post-processing: the Keras model outputs digit probabilities. We want\n",
        "#                  the detected digits. An additional tf.argmax is needed.\n",
        "# @tf.function turns the code in this function into a Tensorflow graph that\n",
        "# can be exported. This way, the model itself, as well as its pre- and post-\n",
        "# processing steps are exported in the SavedModel and deployed in a single step.\n",
        "@tf.function(input_signature=[tf.TensorSpec([None, 28*28], dtype=tf.uint8)])\n",
        "def my_serve(images):\n",
        "  images = tf.cast(images, tf.float32)/255   # pre-processing\n",
        "  probabilities = model(images, training=False) # prediction from model (inference graph only)\n",
        "  classes = tf.argmax(probabilities, axis=-1) # post-processing\n",
        "  return {'digits': classes}\n",
        "\n",
        "# exporting in the Tensorflow standard SavedModel format with a serving input function\n",
        "model.save(export_path, signatures={'serving_default': my_serve}, save_format=\"tf\")\n",
        "print(\"Model exported to: \", export_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zm7cpCRQC8-w",
        "colab_type": "code",
        "outputId": "098f6030-e899-4fd1-89e1-8ac8fafd1126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "# saved_model_cli: a useful too for troubleshooting SavedModels (the tool is part of the Tensorflow installation)\n",
        "!saved_model_cli show --dir {export_path}\n",
        "!saved_model_cli show --dir {export_path} --tag_set serve\n",
        "!saved_model_cli show --dir {export_path} --tag_set serve --signature_def serving_default\n",
        "# A note on naming:\n",
        "# The \"serve\" tag set (i.e. serving functionality) is the only one exported by tf.saved_model.save\n",
        "# All the other names are defined by the user in the fllowing lines of code:\n",
        "#      def myserve(self, images):\n",
        "#                        ******\n",
        "#        return {'digits': classes}\n",
        "#                 ******\n",
        "#      tf.saved_model.save(..., signatures={'serving_default': serving_model.myserve})\n",
        "#                                            ***************"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The given SavedModel contains the following tag-sets:\n",
            "serve\n",
            "The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:\n",
            "SignatureDef key: \"__saved_model_init_op\"\n",
            "SignatureDef key: \"serving_default\"\n",
            "The given SavedModel SignatureDef contains the following input(s):\n",
            "  inputs['images'] tensor_info:\n",
            "      dtype: DT_UINT8\n",
            "      shape: (-1, 784)\n",
            "      name: serving_default_images:0\n",
            "The given SavedModel SignatureDef contains the following output(s):\n",
            "  outputs['digits'] tensor_info:\n",
            "      dtype: DT_INT64\n",
            "      shape: (-1)\n",
            "      name: StatefulPartitionedCall:0\n",
            "Method name is: tensorflow/serving/predict\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zy3T3zk0u2J0"
      },
      "source": [
        "### Deploy the model\n",
        "This uses the command-line interface. You can do the same thing through the AI Platform UI at https://console.cloud.google.com/ai-platform/models\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nGv3ITiGLPL3",
        "colab": {}
      },
      "source": [
        "# Create the model\n",
        "if NEW_MODEL:\n",
        "  !gcloud ai-platform models create {MODEL_NAME} --project={PROJECT} --regions=us-central1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "o3QtUowtOAL-",
        "colab": {}
      },
      "source": [
        "# Create a version of this model (you can add --async at the end of the line to make this call non blocking)\n",
        "# Additional config flags are available: https://cloud.google.com/ai-platform/prediction/docs/reference/rest/v1/projects.models.versions\n",
        "!echo \"Deployment takes a couple of minutes. You can watch your deployment here: https://console.cloud.google.com/ai-platform/models/{MODEL_NAME}\"\n",
        "!gcloud ai-platform versions create {MODEL_VERSION} --model={MODEL_NAME} --origin={export_path} --project={PROJECT} --runtime-version=2.1 --python-version=3.7"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jE-k1Zn6kU2Z"
      },
      "source": [
        "### Test the deployed model\n",
        "Your model is now available as a REST API. Let us try to call it. The cells below use the \"gcloud ai-platform\"\n",
        "command line tool but any tool that can send a JSON payload to a REST endpoint will work."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zZCt0Ke2QDer",
        "colab": {}
      },
      "source": [
        "  # prepare digits to send to online prediction endpoint\n",
        "digits_float32 = np.concatenate((font_digits, validation_digits[:100-N])) # pixel values in [0.0, 1.0] float range\n",
        "digits_uint8 = np.round(digits_float32*255).astype(np.uint8) # pixel values in [0, 255] int range\n",
        "labels = np.concatenate((font_labels, validation_labels[:100-N]))\n",
        "with open(\"digits.json\", \"w\") as f:\n",
        "  for digit in digits_uint8:\n",
        "    # the format for AI Platform online predictions is: one JSON object per line\n",
        "    data = json.dumps({\"images\": digit.tolist()})  # \"images\" because that was the name you gave this parametr in the serving funtion my_serve\n",
        "    f.write(data+'\\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "n6PqhQ8RQ8bp",
        "outputId": "b38af039-b332-4c7b-f91d-04d354889918",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "# Request online predictions from deployed model (REST API) using the \"gcloud ml-engine\" command line.\n",
        "predictions = !gcloud ai-platform predict --model={MODEL_NAME} --json-instances digits.json --project={PROJECT} --version {MODEL_VERSION}\n",
        "print(predictions)\n",
        "\n",
        "predictions = np.stack([json.loads(p) for p in predictions[1:]]) # first element is the name of the output layer: drop it, parse the rest\n",
        "display_top_unrecognized(digits_float32, predictions, labels, N, 100//N)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['DIGITS', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '1', '2', '3', '7', '2', '1', '0', '4', '1', '4', '9', '5', '9', '0', '6', '9', '0', '1', '5', '9', '7', '3', '4', '9', '6', '6', '5', '4', '0', '7', '4', '0', '1', '3', '1', '3', '4', '7', '2', '7', '1', '2', '1', '1', '7', '4', '2', '3', '5', '1', '2', '4', '4', '6', '3', '5', '5', '6', '0', '4', '1', '9', '5', '7', '8', '9', '3', '7', '4', '6', '4', '3', '0', '7', '0', '2', '9', '1', '7']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SVY1pBg5ydH-"
      },
      "source": [
        "## License"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hleIN5-pcr0N"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "author: Martin Gorner<br>\n",
        "twitter: @martin_gorner\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Copyright 2020 Google LLC\n",
        "\n",
        "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",
        "    http://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.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "This is not an official Google product but sample code provided for an educational purpose\n"
      ]
    }
  ]
}