{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osvopUkP4_6o"
      },
      "source": [
        "# OCR model for reading Captchas\n",
        "\n",
        "**Author:** [A_K_Nain](https://twitter.com/A_K_Nain)<br>\n",
        "**Date created:** 2020/06/14<br>\n",
        "**Last modified:** 2020/06/26<br>\n",
        "**Description:** How to implement an OCR model using CNNs, RNNs and CTC loss."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmE2HyqU4_6q"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "This example demonstrates a simple OCR model built with the Functional API. Apart from\n",
        "combining CNN and RNN, it also illustrates how you can instantiate a new layer\n",
        "and use it as an \"Endpoint layer\" for implementing CTC loss. For a detailed\n",
        "guide to layer subclassing, please check out\n",
        "[this page](https://keras.io/guides/making_new_layers_and_models_via_subclassing/)\n",
        "in the developer guides."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxdzwtnT4_6r"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CWJIlad64_6r"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from pathlib import Path\n",
        "from collections import Counter\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubGS90Lq4_6s"
      },
      "source": [
        "## Load the data: [Captcha Images](https://www.kaggle.com/fournierp/captcha-version-2-images)\n",
        "Let's download the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xG4w6_u34_6s",
        "outputId": "73f5bdc8-babd-473e-f824-17989c1a8bee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   159  100   159    0     0    496      0 --:--:-- --:--:-- --:--:--   496\n",
            "100 8863k  100 8863k    0     0  9329k      0 --:--:-- --:--:-- --:--:-- 14.4M\n"
          ]
        }
      ],
      "source": [
        "!curl -LO https://github.com/AakashKumarNain/CaptchaCracker/raw/master/captcha_images_v2.zip\n",
        "!unzip -qq captcha_images_v2.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0T3bSBxk4_6s"
      },
      "source": [
        "The dataset contains 1040 captcha files as `png` images. The label for each sample is a string,\n",
        "the name of the file (minus the file extension).\n",
        "We will map each character in the string to an integer for training the model. Similary,\n",
        "we will need to map the predictions of the model back to strings. For this purpose\n",
        "we will maintain two dictionaries, mapping characters to integers, and integers to characters,\n",
        "respectively."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "EphrJ09U4_6t",
        "outputId": "fd45cd52-4b6c-40b4-c85f-d091c65d6b7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images found:  1040\n",
            "Number of labels found:  1040\n",
            "Number of unique characters:  19\n",
            "Characters present:  {'d', 'c', '2', 'e', 'p', 'y', '4', '3', 'g', 'x', '7', '5', 'f', '6', 'b', 'm', '8', 'w', 'n'}\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Path to the data directory\n",
        "data_dir = Path(\"./captcha_images_v2/\")\n",
        "\n",
        "# Get list of all the images\n",
        "images = sorted(list(map(str, list(data_dir.glob(\"*.png\")))))\n",
        "labels = [img.split(os.path.sep)[-1].split(\".png\")[0] for img in images]\n",
        "characters = set(char for label in labels for char in label)\n",
        "\n",
        "print(\"Number of images found: \", len(images))\n",
        "print(\"Number of labels found: \", len(labels))\n",
        "print(\"Number of unique characters: \", len(characters))\n",
        "print(\"Characters present: \", characters)\n",
        "\n",
        "# Batch size for training and validation\n",
        "batch_size = 16\n",
        "\n",
        "# Desired image dimensions\n",
        "img_width = 200\n",
        "img_height = 50\n",
        "\n",
        "# Factor by which the image is going to be downsampled\n",
        "# by the convolutional blocks. We will be using two\n",
        "# convolution blocks and each block will have\n",
        "# a pooling layer which downsample the features by a factor of 2.\n",
        "# Hence total downsampling factor would be 4.\n",
        "downsample_factor = 4\n",
        "\n",
        "# Maximum length of any captcha in the dataset\n",
        "max_length = max([len(label) for label in labels])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMvoQa4s4_6u"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "XqhY7JFv4_6u"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Mapping characters to integers\n",
        "char_to_num = layers.StringLookup(\n",
        "    vocabulary=list(characters), mask_token=None\n",
        ")\n",
        "\n",
        "# Mapping integers back to original characters\n",
        "num_to_char = layers.StringLookup(\n",
        "    vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True\n",
        ")\n",
        "\n",
        "\n",
        "def split_data(images, labels, train_size=0.9, shuffle=True):\n",
        "    # 1. Get the total size of the dataset\n",
        "    size = len(images)\n",
        "    # 2. Make an indices array and shuffle it, if required\n",
        "    indices = np.arange(size)\n",
        "    if shuffle:\n",
        "        np.random.shuffle(indices)\n",
        "    # 3. Get the size of training samples\n",
        "    train_samples = int(size * train_size)\n",
        "    # 4. Split data into training and validation sets\n",
        "    x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]\n",
        "    x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]\n",
        "    return x_train, x_valid, y_train, y_valid\n",
        "\n",
        "\n",
        "# Splitting data into training and validation sets\n",
        "x_train, x_valid, y_train, y_valid = split_data(np.array(images), np.array(labels))\n",
        "\n",
        "\n",
        "def encode_single_sample(img_path, label):\n",
        "    # 1. Read image\n",
        "    img = tf.io.read_file(img_path)\n",
        "    # 2. Decode and convert to grayscale\n",
        "    img = tf.io.decode_png(img, channels=1)\n",
        "    # 3. Convert to float32 in [0, 1] range\n",
        "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
        "    # 4. Resize to the desired size\n",
        "    img = tf.image.resize(img, [img_height, img_width])\n",
        "    # 5. Transpose the image because we want the time\n",
        "    # dimension to correspond to the width of the image.\n",
        "    img = tf.transpose(img, perm=[1, 0, 2])\n",
        "    # 6. Map the characters in label to numbers\n",
        "    label = char_to_num(tf.strings.unicode_split(label, input_encoding=\"UTF-8\"))\n",
        "    # 7. Return a dict as our model is expecting two inputs\n",
        "    return {\"image\": img, \"label\": label}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NL3snjcx4_6v"
      },
      "source": [
        "## Create `Dataset` objects"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "zbBjX41l4_6v"
      },
      "outputs": [],
      "source": [
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "train_dataset = (\n",
        "    train_dataset.map(\n",
        "        encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE\n",
        "    )\n",
        "    .batch(batch_size)\n",
        "    .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        ")\n",
        "\n",
        "validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))\n",
        "validation_dataset = (\n",
        "    validation_dataset.map(\n",
        "        encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE\n",
        "    )\n",
        "    .batch(batch_size)\n",
        "    .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AxTA6RB4_6v"
      },
      "source": [
        "## Visualize the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "9rYrTA7Q4_6v",
        "outputId": "8327d8e6-395f-4c8d-fae1-4b73adb35210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "_, ax = plt.subplots(4, 4, figsize=(10, 5))\n",
        "for batch in train_dataset.take(1):\n",
        "    images = batch[\"image\"]\n",
        "    labels = batch[\"label\"]\n",
        "    for i in range(16):\n",
        "        img = (images[i] * 255).numpy().astype(\"uint8\")\n",
        "        label = tf.strings.reduce_join(num_to_char(labels[i])).numpy().decode(\"utf-8\")\n",
        "        ax[i // 4, i % 4].imshow(img[:, :, 0].T, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(label)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RW16Hk4s4_6w"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4bOjCK074_6w",
        "outputId": "a71930d0-9ad9-4591-8ad9-aad48fb52cca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"ocr_model_v1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " image (InputLayer)             [(None, 200, 50, 1)  0           []                               \n",
            "                                ]                                                                 \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 200, 50, 32)  320         ['image[0][0]']                  \n",
            "                                                                                                  \n",
            " pool1 (MaxPooling2D)           (None, 100, 25, 32)  0           ['Conv1[0][0]']                  \n",
            "                                                                                                  \n",
            " Conv2 (Conv2D)                 (None, 100, 25, 64)  18496       ['pool1[0][0]']                  \n",
            "                                                                                                  \n",
            " pool2 (MaxPooling2D)           (None, 50, 12, 64)   0           ['Conv2[0][0]']                  \n",
            "                                                                                                  \n",
            " reshape (Reshape)              (None, 50, 768)      0           ['pool2[0][0]']                  \n",
            "                                                                                                  \n",
            " dense1 (Dense)                 (None, 50, 64)       49216       ['reshape[0][0]']                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 50, 64)       0           ['dense1[0][0]']                 \n",
            "                                                                                                  \n",
            " bidirectional (Bidirectional)  (None, 50, 256)      197632      ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirectional  (None, 50, 128)     164352      ['bidirectional[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " label (InputLayer)             [(None, None)]       0           []                               \n",
            "                                                                                                  \n",
            " dense2 (Dense)                 (None, 50, 21)       2709        ['bidirectional_1[0][0]']        \n",
            "                                                                                                  \n",
            " ctc_loss (CTCLayer)            (None, 50, 21)       0           ['label[0][0]',                  \n",
            "                                                                  'dense2[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 432,725\n",
            "Trainable params: 432,725\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "\n",
        "class CTCLayer(layers.Layer):\n",
        "    def __init__(self, name=None):\n",
        "        super().__init__(name=name)\n",
        "        self.loss_fn = keras.backend.ctc_batch_cost\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        # Compute the training-time loss value and add it\n",
        "        # to the layer using `self.add_loss()`.\n",
        "        batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
        "        input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
        "        label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
        "\n",
        "        input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "\n",
        "        loss = self.loss_fn(y_true, y_pred, input_length, label_length)\n",
        "        self.add_loss(loss)\n",
        "\n",
        "        # At test time, just return the computed predictions\n",
        "        return y_pred\n",
        "\n",
        "\n",
        "def build_model():\n",
        "    # Inputs to the model\n",
        "    input_img = layers.Input(\n",
        "        shape=(img_width, img_height, 1), name=\"image\", dtype=\"float32\"\n",
        "    )\n",
        "    labels = layers.Input(name=\"label\", shape=(None,), dtype=\"float32\")\n",
        "\n",
        "    # First conv block\n",
        "    x = layers.Conv2D(\n",
        "        32,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv1\",\n",
        "    )(input_img)\n",
        "    x = layers.MaxPooling2D((2, 2), name=\"pool1\")(x)\n",
        "\n",
        "    # Second conv block\n",
        "    x = layers.Conv2D(\n",
        "        64,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv2\",\n",
        "    )(x)\n",
        "    x = layers.MaxPooling2D((2, 2), name=\"pool2\")(x)\n",
        "\n",
        "    # We have used two max pool with pool size and strides 2.\n",
        "    # Hence, downsampled feature maps are 4x smaller. The number of\n",
        "    # filters in the last layer is 64. Reshape accordingly before\n",
        "    # passing the output to the RNN part of the model\n",
        "    new_shape = ((img_width // 4), (img_height // 4) * 64)\n",
        "    x = layers.Reshape(target_shape=new_shape, name=\"reshape\")(x)\n",
        "    x = layers.Dense(64, activation=\"relu\", name=\"dense1\")(x)\n",
        "    x = layers.Dropout(0.2)(x)\n",
        "\n",
        "    # RNNs\n",
        "    x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)\n",
        "    x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)\n",
        "\n",
        "    # Output layer\n",
        "    x = layers.Dense(\n",
        "        len(char_to_num.get_vocabulary()) + 1, activation=\"softmax\", name=\"dense2\"\n",
        "    )(x)\n",
        "\n",
        "    # Add CTC layer for calculating CTC loss at each step\n",
        "    output = CTCLayer(name=\"ctc_loss\")(labels, x)\n",
        "\n",
        "    # Define the model\n",
        "    model = keras.models.Model(\n",
        "        inputs=[input_img, labels], outputs=output, name=\"ocr_model_v1\"\n",
        "    )\n",
        "    # Optimizer\n",
        "    opt = keras.optimizers.Adam()\n",
        "    # Compile the model and return\n",
        "    model.compile(optimizer=opt)\n",
        "    return model\n",
        "\n",
        "\n",
        "# Get the model\n",
        "model = build_model()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxqDMD_J4_6w"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "qaouofgk4_6w",
        "outputId": "aeb8a5cb-c94b-4a02-c4a2-d3738e669cca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "59/59 [==============================] - 32s 544ms/step - loss: 16.3369 - val_loss: 16.4330\n",
            "Epoch 2/20\n",
            "59/59 [==============================] - 23s 386ms/step - loss: 16.3288 - val_loss: 16.4363\n",
            "Epoch 3/20\n",
            "59/59 [==============================] - 23s 388ms/step - loss: 16.3230 - val_loss: 16.4387\n",
            "Epoch 4/20\n",
            "59/59 [==============================] - 24s 399ms/step - loss: 16.3204 - val_loss: 16.4480\n",
            "Epoch 5/20\n",
            "59/59 [==============================] - 34s 578ms/step - loss: 16.3212 - val_loss: 16.4412\n",
            "Epoch 6/20\n",
            "59/59 [==============================] - 23s 386ms/step - loss: 16.3130 - val_loss: 16.4340\n",
            "Epoch 7/20\n",
            "59/59 [==============================] - 23s 398ms/step - loss: 16.3026 - val_loss: 16.4313\n",
            "Epoch 8/20\n",
            "59/59 [==============================] - 23s 387ms/step - loss: 16.2957 - val_loss: 16.4323\n",
            "Epoch 9/20\n",
            "59/59 [==============================] - 23s 395ms/step - loss: 16.2859 - val_loss: 16.4268\n",
            "Epoch 10/20\n",
            "59/59 [==============================] - 23s 397ms/step - loss: 16.2766 - val_loss: 16.4328\n",
            "Epoch 11/20\n",
            "59/59 [==============================] - 23s 386ms/step - loss: 16.2542 - val_loss: 16.4285\n",
            "Epoch 12/20\n",
            "59/59 [==============================] - 23s 386ms/step - loss: 16.2108 - val_loss: 16.3642\n",
            "Epoch 13/20\n",
            "59/59 [==============================] - 23s 386ms/step - loss: 16.1468 - val_loss: 16.2480\n",
            "Epoch 14/20\n",
            "59/59 [==============================] - 23s 387ms/step - loss: 16.0623 - val_loss: 16.0686\n",
            "Epoch 15/20\n",
            "59/59 [==============================] - 23s 387ms/step - loss: 15.8662 - val_loss: 15.8330\n",
            "Epoch 16/20\n",
            "59/59 [==============================] - 23s 388ms/step - loss: 15.6027 - val_loss: 15.7658\n",
            "Epoch 17/20\n",
            "59/59 [==============================] - 23s 389ms/step - loss: 15.3818 - val_loss: 15.4152\n",
            "Epoch 18/20\n",
            "59/59 [==============================] - 23s 387ms/step - loss: 14.9975 - val_loss: 14.9603\n",
            "Epoch 19/20\n",
            "59/59 [==============================] - 23s 387ms/step - loss: 14.5267 - val_loss: 14.4524\n",
            "Epoch 20/20\n",
            "59/59 [==============================] - 23s 387ms/step - loss: 14.1460 - val_loss: 14.0870\n"
          ]
        }
      ],
      "source": [
        "\n",
        "epochs = 20\n",
        "early_stopping_patience = 10\n",
        "# Add early stopping\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\", patience=early_stopping_patience, restore_best_weights=True\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    validation_data=validation_dataset,\n",
        "    epochs=epochs,\n",
        "    callbacks=[early_stopping],\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUhQGgWN4_6w"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "fIjbHFpL4_6w",
        "outputId": "37c4bcc2-abd3-429b-ae9f-8869aa77b7f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " image (InputLayer)          [(None, 200, 50, 1)]      0         \n",
            "                                                                 \n",
            " Conv1 (Conv2D)              (None, 200, 50, 32)       320       \n",
            "                                                                 \n",
            " pool1 (MaxPooling2D)        (None, 100, 25, 32)       0         \n",
            "                                                                 \n",
            " Conv2 (Conv2D)              (None, 100, 25, 64)       18496     \n",
            "                                                                 \n",
            " pool2 (MaxPooling2D)        (None, 50, 12, 64)        0         \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 50, 768)           0         \n",
            "                                                                 \n",
            " dense1 (Dense)              (None, 50, 64)            49216     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 50, 64)            0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 50, 256)          197632    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 50, 128)          164352    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense2 (Dense)              (None, 50, 21)            2709      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 432,725\n",
            "Trainable params: 432,725\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "# Get the prediction model by extracting layers till the output layer\n",
        "prediction_model = keras.models.Model(\n",
        "    model.get_layer(name=\"image\").input, model.get_layer(name=\"dense2\").output\n",
        ")\n",
        "prediction_model.summary()\n",
        "\n",
        "# A utility function to decode the output of the network\n",
        "def decode_batch_predictions(pred):\n",
        "    input_len = np.ones(pred.shape[0]) * pred.shape[1]\n",
        "    # Use greedy search. For complex tasks, you can use beam search\n",
        "    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][\n",
        "        :, :max_length\n",
        "    ]\n",
        "    # Iterate over the results and get back the text\n",
        "    output_text = []\n",
        "    for res in results:\n",
        "        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode(\"utf-8\")\n",
        "        output_text.append(res)\n",
        "    return output_text\n",
        "\n",
        "\n",
        "#  Let's check results on some validation samples\n",
        "for batch in validation_dataset.take(1):\n",
        "    batch_images = batch[\"image\"]\n",
        "    batch_labels = batch[\"label\"]\n",
        "\n",
        "    preds = prediction_model.predict(batch_images)\n",
        "    pred_texts = decode_batch_predictions(preds)\n",
        "\n",
        "    orig_texts = []\n",
        "    for label in batch_labels:\n",
        "        label = tf.strings.reduce_join(num_to_char(label)).numpy().decode(\"utf-8\")\n",
        "        orig_texts.append(label)\n",
        "\n",
        "    _, ax = plt.subplots(4, 4, figsize=(15, 5))\n",
        "    for i in range(len(pred_texts)):\n",
        "        img = (batch_images[i, :, :, 0] * 255).numpy().astype(np.uint8)\n",
        "        img = img.T\n",
        "        title = f\"Prediction: {pred_texts[i]}\"\n",
        "        ax[i // 4, i % 4].imshow(img, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(title)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface-hub\n",
        "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n",
        "!sudo apt-get install git-lfs\n",
        "!git-lfs install"
      ],
      "metadata": {
        "id": "xUxwWxds5I11",
        "outputId": "b527233d-6085-40d9-d1f8-eb2cc48b8dfb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[?25l\r\u001b[K     |█████▎                          | 10 kB 24.8 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 20 kB 31.9 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 30 kB 26.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 40 kB 21.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 51 kB 8.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 61 kB 9.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61 kB 505 kB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.4.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.62.3)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.13)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.8.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub) (3.6.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2.10)\n",
            "Installing collected packages: huggingface-hub\n",
            "Successfully installed huggingface-hub-0.2.1\n",
            "Detected operating system as Ubuntu/bionic.\n",
            "Checking for curl...\n",
            "Detected curl...\n",
            "Checking for gpg...\n",
            "Detected gpg...\n",
            "Running apt-get update... done.\n",
            "Installing apt-transport-https... done.\n",
            "Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n",
            "Importing packagecloud gpg key... done.\n",
            "Running apt-get update... done.\n",
            "\n",
            "The repository is setup! You can now install packages.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  git-lfs\n",
            "0 upgraded, 1 newly installed, 0 to remove and 62 not upgraded.\n",
            "Need to get 6,526 kB of archives.\n",
            "After this operation, 14.7 MB of additional disk space will be used.\n",
            "Get:1 https://packagecloud.io/github/git-lfs/ubuntu bionic/main amd64 git-lfs amd64 3.0.2 [6,526 kB]\n",
            "Fetched 6,526 kB in 0s (16.3 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package git-lfs.\n",
            "(Reading database ... 155226 files and directories currently installed.)\n",
            "Preparing to unpack .../git-lfs_3.0.2_amd64.deb ...\n",
            "Unpacking git-lfs (3.0.2) ...\n",
            "Setting up git-lfs (3.0.2) ...\n",
            "Git LFS initialized.\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Git LFS initialized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "j445bZtt5MPp",
        "outputId": "591913bc-72ed-4b07-92be-29f89c06d525",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
            "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from huggingface_hub.keras_mixin import push_to_hub_keras\n",
        "push_to_hub_keras(model = model, repo_url = \"https://huggingface.co/keras-io/ocr-for-captcha\", organization = \"keras-io\")"
      ],
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      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/keras-io/ocr-for-captcha into local empty directory.\n",
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_4_layer_call_fn while saving (showing 5 of 20). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: ocr-for-captcha/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: ocr-for-captcha/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7ff062772d50> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7ff062746dd0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7ff0625a8e10> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7ff062580790> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "59d988d171984beb8145a3a1145a1466",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Upload file saved_model.pb:   1%|          | 32.0k/4.68M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "f1e7466046904ca7b94122ea715de736",
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              "Upload file keras_metadata.pb:  89%|########9 | 32.0k/35.9k [00:00<?, ?B/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/keras-io/ocr-for-captcha\n",
            "   b5e4722..f3be0db  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/keras-io/ocr-for-captcha\n",
            "   b5e4722..f3be0db  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 14
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_aU-6Rg0-GOA"
      },
      "execution_count": null,
      "outputs": []
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