CRNN_with_CTC_in_Colab.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "CRNN_with_CTC_in_Colab.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YenLinWu/CRNN_with_CTC_Loss/blob/main/CRNN_with_CTC_in_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6tjkcN2jCUm"
      },
      "source": [
        "# Connect Google Colab with Google Drive  連接 Colab 與 drive "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-mI7tKNjAeI"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount( '/content/gdrive' )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-uJ7JqXgUR6"
      },
      "source": [
        "# Import Packages  匯入套件"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjrN0lSigUR8"
      },
      "source": [
        "import os\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt  \n",
        "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hu4djXcogUSB",
        "outputId": "29b26460-9484-46fe-f947-bfc7d1812965",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization, Flatten, Dense, Dropout, Reshape, Bidirectional, LSTM\n",
        "from tensorflow.keras.optimizers import *\n",
        "from tensorflow.keras.callbacks import *\n",
        "\n",
        "print( 'TensorFlow Version:', tf.__version__ )\n",
        "print( 'Keras Version:', tf.keras.__version__ ) "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow Version: 2.3.0\n",
            "Keras Version: 2.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyZZqqv7gUSI"
      },
      "source": [
        "# Captcha Images Dataset  驗證碼資料集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMTffAGLGdDh",
        "outputId": "3bcf5029-a934-4092-8f66-d3fce8486380",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "os.chdir( r\"/content/gdrive/My Drive\" )\n",
        "if not os.path.isdir( r\"CRNN_with_CTC_Loss\" ):     \n",
        "    print( 'Downloading the Captcha images dataset ...' )\n",
        "    !pip install patool\n",
        "    import patoolib\n",
        "    import glob\n",
        "    # Clone a GitHub repository to our Google Drive\n",
        "    !git clone https://github.com/YenLinWu/CRNN_with_CTC_Loss.git     \n",
        "    # Unzip the file with filename contains '.rar'\n",
        "    rar = glob.glob( r\"CRNN_with_CTC_Loss/*.rar\" )\n",
        "    for i in range(len(rar)):\n",
        "        patoolib.extract_archive( rar[i], outdir=r\"/content/gdrive/My Drive\" )\n",
        "\n",
        "else: print( 'Captcha images dataset is ready.' )"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading the Captcha images dataset ...\n",
            "Requirement already satisfied: patool in /usr/local/lib/python3.6/dist-packages (1.12)\n",
            "Cloning into 'CRNN_with_CTC_Loss'...\n",
            "remote: Enumerating objects: 60, done.\u001b[K\n",
            "remote: Counting objects: 100% (60/60), done.\u001b[K\n",
            "remote: Compressing objects: 100% (52/52), done.\u001b[K\n",
            "remote: Total 60 (delta 25), reused 33 (delta 8), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (60/60), done.\n",
            "patool: Extracting CRNN_with_CTC_Loss/CaptchaImages.rar ...\n",
            "patool: running /usr/bin/unrar x -- \"/content/gdrive/My Drive/CRNN_with_CTC_Loss/CaptchaImages.rar\"\n",
            "patool:     with cwd='/content/gdrive/My Drive'\n",
            "patool: ... CRNN_with_CTC_Loss/CaptchaImages.rar extracted to `/content/gdrive/My Drive'.\n",
            "patool: Extracting CRNN_with_CTC_Loss/TestingData.rar ...\n",
            "patool: running /usr/bin/unrar x -- \"/content/gdrive/My Drive/CRNN_with_CTC_Loss/TestingData.rar\"\n",
            "patool:     with cwd='/content/gdrive/My Drive'\n",
            "patool: ... CRNN_with_CTC_Loss/TestingData.rar extracted to `/content/gdrive/My Drive'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ySl68i_gUSJ",
        "outputId": "b1219a8c-0af5-48fd-b789-045aac07119d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Path to the dataset directory\n",
        "CaptchaImages = r\"/content/gdrive/My Drive/CaptchaImages\"\n",
        "data_dir = Path( CaptchaImages )\n",
        "\n",
        "print( 'Working directory:', data_dir )"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Working directory: /content/gdrive/My Drive/CaptchaImages\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCPw66j3gUSN",
        "outputId": "508099f9-9dbd-407e-8207-edd49b39a4a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Get list of all the captcha 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",
        "Image_Height = set( tf.image.decode_png(tf.io.read_file(img_path)).shape[0] for img_path in Images )\n",
        "Image_Width = set( tf.image.decode_png(tf.io.read_file(img_path)).shape[1] for img_path in Images )\n",
        "\n",
        "# Maximum length of any captcha in the dataset\n",
        "Max_Length = max([ len(label) for label in Labels ])\n",
        "\n",
        "print( 'Number of images found: ', len(Images) )\n",
        "print( 'Number of labels found: ', len(Labels), '\\n' )\n",
        "print( 'Width of any captcha image:', next(iter(Image_Width)) )\n",
        "print( 'Height of any captcha image:', next(iter(Image_Height)) )\n",
        "print( 'Characters present: ', Characters ) \n",
        "print( 'Number of unique characters: ', len(Characters) )\n",
        "print( 'Maxium length of any captcha image: ', Max_Length )"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of images found:  1800\n",
            "Number of labels found:  1800 \n",
            "\n",
            "Width of any captcha image: 104\n",
            "Height of any captcha image: 24\n",
            "Characters present:  {'5', '0', '4', '7', '8', '2', '1', '6', '3', '9'}\n",
            "Number of unique characters:  10\n",
            "Maxium length of any captcha image:  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3wG1LxagUSQ"
      },
      "source": [
        "# Hyper-parameters  超參數"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDN_blf-gUSQ"
      },
      "source": [
        "# Desired image dimensions where img_width % 4==0 and img_hight % 16==0 \n",
        "img_width = 104\n",
        "img_height = 32\n",
        "\n",
        "# Number of passes of the entire training set the algorithm has completed\n",
        "Epochs = 100\n",
        "\n",
        "# Number of each batch\n",
        "Batch_Size = 32\n",
        "\n",
        "# Early Stopping\n",
        "'''\n",
        "min_delta: 容忍模型進步的最小幅度\n",
        "patience: 容忍訓練無再進步時的 Epoch 次數\n",
        "'''\n",
        "Early_Stopping_Patience = 10 \n",
        "Min_Delta = 0.0001"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3iw-cTygUSW"
      },
      "source": [
        "# Preprocessing  資料預處理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqFebZ6GgUSW"
      },
      "source": [
        "# Mapping characters to integers\n",
        "char_to_num = layers.experimental.preprocessing.StringLookup(\n",
        "                  vocabulary=sorted(list(Characters)), num_oov_indices=0, mask_token=None )\n",
        "\n",
        "# Mapping integers back to original characters\n",
        "num_to_char = layers.experimental.preprocessing.StringLookup(\n",
        "                  vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True )"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ys2j7UWKgUSb"
      },
      "source": [
        "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 dimension to correspond to the width of the image, \n",
        "    #    i.e., shape = (img_weight,img_height,1).\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 }"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjYjW4wCgUSe"
      },
      "source": [
        "# Training and Validation Sets  訓練集與驗證集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig-tUIxUgUSe",
        "outputId": "f382233e-fe6c-43aa-e75d-82e7e80099a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Splitting dataset into training and validation sets\n",
        "def split_data( Images, Labels, train_size=0.9, shuffle=True ):\n",
        "    # 1. Get the total amount 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.seed(42) \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",
        "x_train, x_valid, y_train, y_valid = split_data( np.array(Images), np.array(Labels) )\n",
        "\n",
        "print( 'Number of training set:', len(x_train) )\n",
        "print( 'Number of validation set:', len(x_valid) )"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training set: 1620\n",
            "Number of validation set: 180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRgTbvlrgUSh"
      },
      "source": [
        "# Create training and valodation dataset objects\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.experimental.AUTOTUNE\n",
        "    )\n",
        "    .batch(Batch_Size)\n",
        "    .prefetch(buffer_size=tf.data.experimental.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.experimental.AUTOTUNE\n",
        "    )\n",
        "    .batch(Batch_Size)\n",
        "    .prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x05ZUHS7gUSl",
        "outputId": "d3541ae3-ddc9-417e-d86e-f5968ed58440",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# Visualize the Training Dataset\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()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5IA6UY4gUSq"
      },
      "source": [
        "# Convolutional Recurrent Neural Network(CRNN)  卷積循環神經網絡  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEl3JxB1gUSq"
      },
      "source": [
        "# Build an endpoint layer for implementing CTC loss.\n",
        "class CTCLayer( layers.Layer ):\n",
        "    def __init__( self, name=None, **kwargs ):\n",
        "        super().__init__( name=name )\n",
        "        self.loss_fn = K.ctc_batch_cost\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        # Compute the training-time loss value and add it 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"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BawVHyXKgUSt",
        "outputId": "92d766a8-8409-4bb3-bcd8-2b8f6ebf6f45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "def Build_Model():\n",
        "    # Inputs to the model \n",
        "    input_tensor = Input( (img_width,img_height,1), dtype='float32', name='Input' )\n",
        "    labels = Input( shape=(None,), dtype='float32', name=\"Label\" )\n",
        "    \n",
        "    # First convolution block\n",
        "    x = Conv2D( 64, (3,3), activation='relu', padding='same', name='Conv_1' )(input_tensor)\n",
        "    x = MaxPooling2D( pool_size=(2,2), strides=2, name='MaxPooling_1' )(x)\n",
        "    \n",
        "    # Second convolution block\n",
        "    x = Conv2D( 128, (3,3), activation='relu', padding='same', name='Conv_2' )(x)\n",
        "    x = MaxPooling2D( pool_size=(2,2), strides=2, name='MaxPooling_2' )(x)\n",
        "    \n",
        "    # Third convolution block\n",
        "    x = Conv2D( 256, (3,3), activation='relu', padding='same', name='Conv_3' )(x)\n",
        "    x = BatchNormalization( name='BN_1' )(x)\n",
        "    \n",
        "    # Fourth convolution block\n",
        "    x = Conv2D( 512, (3,3), activation='relu', padding='same', name='Conv_4' )(x)  \n",
        "    x = MaxPooling2D( pool_size=(2,1), strides=2, name='MaxPooling_3' )(x)\n",
        "    \n",
        "    # Fifth convolution block\n",
        "    x = Conv2D( 512, (2,2), activation='relu', name='Conv_5' )(x)\n",
        "    \n",
        "    # Reshape accordingly before passing the output to the RNN part of the model,\n",
        "    # i.e., convert shape=(batch,feature_weight,feature_height,channel) to shape=(batch,feature_weight,channel*feature_height) \n",
        "    Conv_Shape = x.get_shape( )\n",
        "    x = Reshape( target_shape=(int(Conv_Shape[1]),int(Conv_Shape[2]*Conv_Shape[3])), name='Reshape' )(x)\n",
        "    x = Dense( 64, activation='relu', name='Dense' )(x)\n",
        "    x = Dropout( 0.25, name='Dropout' )(x)\n",
        "\n",
        "    # RNNs\n",
        "    x = Bidirectional( LSTM( 256, return_sequences=True, dropout=0.25 ), name='LSTM_1' )(x)\n",
        "    x = Bidirectional( LSTM( 256, return_sequences=True, dropout=0.25 ), name='LSTM_2' )(x)\n",
        "\n",
        "    # Output layer\n",
        "    x = Dense( len(Characters)+1, activation='softmax', name='Softmax' )(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 = Model( inputs=[input_tensor,labels], outputs=[output], name='CRNN_Model_with_CTC_LOSS' )    \n",
        "    \n",
        "    # Compile the model and return\n",
        "    model.compile( optimizer=Adam() )\n",
        "    \n",
        "    return model\n",
        "\n",
        "CRNN = Build_Model()\n",
        "CRNN.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"CRNN_Model_with_CTC_LOSS\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "Input (InputLayer)              [(None, 104, 32, 1)] 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 104, 32, 64)  640         Input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "MaxPooling_1 (MaxPooling2D)     (None, 52, 16, 64)   0           Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Conv_2 (Conv2D)                 (None, 52, 16, 128)  73856       MaxPooling_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "MaxPooling_2 (MaxPooling2D)     (None, 26, 8, 128)   0           Conv_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Conv_3 (Conv2D)                 (None, 26, 8, 256)   295168      MaxPooling_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "BN_1 (BatchNormalization)       (None, 26, 8, 256)   1024        Conv_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Conv_4 (Conv2D)                 (None, 26, 8, 512)   1180160     BN_1[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "MaxPooling_3 (MaxPooling2D)     (None, 13, 4, 512)   0           Conv_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Conv_5 (Conv2D)                 (None, 12, 3, 512)   1049088     MaxPooling_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "Reshape (Reshape)               (None, 12, 1536)     0           Conv_5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Dense (Dense)                   (None, 12, 64)       98368       Reshape[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Dropout (Dropout)               (None, 12, 64)       0           Dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "LSTM_1 (Bidirectional)          (None, 12, 512)      657408      Dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "LSTM_2 (Bidirectional)          (None, 12, 512)      1574912     LSTM_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Label (InputLayer)              [(None, None)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Softmax (Dense)                 (None, 12, 11)       5643        LSTM_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "CTC_Loss (CTCLayer)             (None, 12, 11)       0           Label[0][0]                      \n",
            "                                                                 Softmax[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 4,936,267\n",
            "Trainable params: 4,935,755\n",
            "Non-trainable params: 512\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KB1iRh35gUSw"
      },
      "source": [
        "# Callbacks  回調函數"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYnLD6NcgUSx"
      },
      "source": [
        "# Model Check Point\n",
        "Check_Point = ModelCheckpoint( 'CRNN.h5',   # Filepath\n",
        "                               monitor='val_loss',\n",
        "                               save_best_only=True,\n",
        "                               verbose=1,\n",
        "                               mode='auto',\n",
        "                               save_weights_only=False,\n",
        "                               save_freq='epoch' )"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVAUnvTegUS0"
      },
      "source": [
        "# Add early stopping\n",
        "Early_Stopping = EarlyStopping( monitor='val_loss',\n",
        "                                min_delta=Min_Delta,\n",
        "                                patience=Early_Stopping_Patience,\n",
        "                                verbose=1,\n",
        "                                mode='auto',\n",
        "                                baseline=None,\n",
        "                                restore_best_weights=True )"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q30PUm5xgUS5"
      },
      "source": [
        "# Training Model  訓練模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5dNrypggUS6",
        "outputId": "bad788ce-3158-4707-e4f2-ab7a264b67bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "History = CRNN.fit( train_dataset, validation_data=validation_dataset, epochs=Epochs, \n",
        "                    callbacks=[ Check_Point, Early_Stopping ] )                      "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 11.0727\n",
            "Epoch 00001: val_loss improved from inf to 10.76941, saving model to CRNN.h5\n",
            "51/51 [==============================] - 7s 136ms/step - loss: 11.0727 - val_loss: 10.7694\n",
            "Epoch 2/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 10.6506\n",
            "Epoch 00002: val_loss did not improve from 10.76941\n",
            "51/51 [==============================] - 4s 70ms/step - loss: 10.6516 - val_loss: 10.7863\n",
            "Epoch 3/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 9.7245\n",
            "Epoch 00003: val_loss did not improve from 10.76941\n",
            "51/51 [==============================] - 4s 70ms/step - loss: 9.6999 - val_loss: 16.8079\n",
            "Epoch 4/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 4.5913\n",
            "Epoch 00004: val_loss improved from 10.76941 to 2.41807, saving model to CRNN.h5\n",
            "51/51 [==============================] - 4s 75ms/step - loss: 4.5590 - val_loss: 2.4181\n",
            "Epoch 5/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 1.2545\n",
            "Epoch 00005: val_loss did not improve from 2.41807\n",
            "51/51 [==============================] - 4s 69ms/step - loss: 1.2449 - val_loss: 14.9764\n",
            "Epoch 6/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.2744\n",
            "Epoch 00006: val_loss improved from 2.41807 to 0.36598, saving model to CRNN.h5\n",
            "51/51 [==============================] - 3s 68ms/step - loss: 0.2744 - val_loss: 0.3660\n",
            "Epoch 7/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.2322\n",
            "Epoch 00007: val_loss improved from 0.36598 to 0.21166, saving model to CRNN.h5\n",
            "51/51 [==============================] - 5s 103ms/step - loss: 0.2301 - val_loss: 0.2117\n",
            "Epoch 8/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.1295\n",
            "Epoch 00008: val_loss did not improve from 0.21166\n",
            "51/51 [==============================] - 3s 65ms/step - loss: 0.1295 - val_loss: 3.1154\n",
            "Epoch 9/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.1428\n",
            "Epoch 00009: val_loss did not improve from 0.21166\n",
            "51/51 [==============================] - 3s 68ms/step - loss: 0.1425 - val_loss: 26.2837\n",
            "Epoch 10/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0939\n",
            "Epoch 00010: val_loss did not improve from 0.21166\n",
            "51/51 [==============================] - 3s 63ms/step - loss: 0.0931 - val_loss: 0.4017\n",
            "Epoch 11/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0446\n",
            "Epoch 00011: val_loss improved from 0.21166 to 0.10659, saving model to CRNN.h5\n",
            "51/51 [==============================] - 4s 73ms/step - loss: 0.0446 - val_loss: 0.1066\n",
            "Epoch 12/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0482\n",
            "Epoch 00012: val_loss did not improve from 0.10659\n",
            "51/51 [==============================] - 3s 65ms/step - loss: 0.0482 - val_loss: 0.1393\n",
            "Epoch 13/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0436\n",
            "Epoch 00013: val_loss improved from 0.10659 to 0.10045, saving model to CRNN.h5\n",
            "51/51 [==============================] - 4s 72ms/step - loss: 0.0432 - val_loss: 0.1005\n",
            "Epoch 14/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0223\n",
            "Epoch 00014: val_loss improved from 0.10045 to 0.09723, saving model to CRNN.h5\n",
            "51/51 [==============================] - 5s 105ms/step - loss: 0.0221 - val_loss: 0.0972\n",
            "Epoch 15/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0194\n",
            "Epoch 00015: val_loss improved from 0.09723 to 0.09127, saving model to CRNN.h5\n",
            "51/51 [==============================] - 4s 73ms/step - loss: 0.0193 - val_loss: 0.0913\n",
            "Epoch 16/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0234\n",
            "Epoch 00016: val_loss did not improve from 0.09127\n",
            "51/51 [==============================] - 3s 63ms/step - loss: 0.0232 - val_loss: 0.0987\n",
            "Epoch 17/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0285\n",
            "Epoch 00017: val_loss did not improve from 0.09127\n",
            "51/51 [==============================] - 3s 65ms/step - loss: 0.0285 - val_loss: 1.5756\n",
            "Epoch 18/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.1652\n",
            "Epoch 00018: val_loss did not improve from 0.09127\n",
            "51/51 [==============================] - 3s 64ms/step - loss: 0.1652 - val_loss: 34.2499\n",
            "Epoch 19/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.1620\n",
            "Epoch 00019: val_loss did not improve from 0.09127\n",
            "51/51 [==============================] - 3s 65ms/step - loss: 0.1603 - val_loss: 2.1720\n",
            "Epoch 20/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0418\n",
            "Epoch 00020: val_loss improved from 0.09127 to 0.08707, saving model to CRNN.h5\n",
            "51/51 [==============================] - 4s 76ms/step - loss: 0.0418 - val_loss: 0.0871\n",
            "Epoch 21/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0216\n",
            "Epoch 00021: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 63ms/step - loss: 0.0216 - val_loss: 0.0953\n",
            "Epoch 22/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0193\n",
            "Epoch 00022: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 64ms/step - loss: 0.0192 - val_loss: 0.0905\n",
            "Epoch 23/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0116\n",
            "Epoch 00023: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 65ms/step - loss: 0.0114 - val_loss: 0.0944\n",
            "Epoch 24/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0143\n",
            "Epoch 00024: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 66ms/step - loss: 0.0142 - val_loss: 0.1075\n",
            "Epoch 25/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0111\n",
            "Epoch 00025: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 68ms/step - loss: 0.0111 - val_loss: 0.1054\n",
            "Epoch 26/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0109\n",
            "Epoch 00026: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 64ms/step - loss: 0.0109 - val_loss: 0.0945\n",
            "Epoch 27/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0139\n",
            "Epoch 00027: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 68ms/step - loss: 0.0139 - val_loss: 0.0958\n",
            "Epoch 28/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0106\n",
            "Epoch 00028: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 63ms/step - loss: 0.0106 - val_loss: 0.1008\n",
            "Epoch 29/100\n",
            "50/51 [============================>.] - ETA: 0s - loss: 0.0146\n",
            "Epoch 00029: val_loss did not improve from 0.08707\n",
            "51/51 [==============================] - 3s 68ms/step - loss: 0.0145 - val_loss: 0.1010\n",
            "Epoch 30/100\n",
            "51/51 [==============================] - ETA: 0s - loss: 0.0133\n",
            "Epoch 00030: val_loss did not improve from 0.08707\n",
            "Restoring model weights from the end of the best epoch.\n",
            "51/51 [==============================] - 3s 65ms/step - loss: 0.0133 - val_loss: 0.4279\n",
            "Epoch 00030: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RjYD9rrgUS-",
        "outputId": "b3255515-1ecb-4949-e2ed-02bc6e0c3654",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "Train_Loss = History.history['loss']\n",
        "Val_Loss = History.history['val_loss']\n",
        "epochs_range = range(Epochs)\n",
        "\n",
        "plt.subplot( )\n",
        "plt.plot( range( len(Train_Loss) ), Train_Loss, label='Train'+\"{:10.4f}\".format( min(History.history['loss']) ) )\n",
        "plt.plot( range( len(Val_Loss) ), Val_Loss, label='Valid'+\"{:10.4f}\".format( min(History.history['val_loss']) ) )\n",
        "plt.legend( loc='upper right' )\n",
        "plt.title( 'Loss of CRNN')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySd5FIPBgUTB"
      },
      "source": [
        "# Inference  預測"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaR_7IM4gUTC",
        "outputId": "1c24ea56-1b1e-4966-ef43-00534b64a953",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Get the prediction model by extracting layers till the output layer\n",
        "prediction_model = Model( CRNN.get_layer( name='Input' ).input, CRNN.get_layer( name='Softmax' ).output, name='Prediction' )\n",
        "prediction_model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"Prediction\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Input (InputLayer)           [(None, 104, 32, 1)]      0         \n",
            "_________________________________________________________________\n",
            "Conv_1 (Conv2D)              (None, 104, 32, 64)       640       \n",
            "_________________________________________________________________\n",
            "MaxPooling_1 (MaxPooling2D)  (None, 52, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "Conv_2 (Conv2D)              (None, 52, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "MaxPooling_2 (MaxPooling2D)  (None, 26, 8, 128)        0         \n",
            "_________________________________________________________________\n",
            "Conv_3 (Conv2D)              (None, 26, 8, 256)        295168    \n",
            "_________________________________________________________________\n",
            "BN_1 (BatchNormalization)    (None, 26, 8, 256)        1024      \n",
            "_________________________________________________________________\n",
            "Conv_4 (Conv2D)              (None, 26, 8, 512)        1180160   \n",
            "_________________________________________________________________\n",
            "MaxPooling_3 (MaxPooling2D)  (None, 13, 4, 512)        0         \n",
            "_________________________________________________________________\n",
            "Conv_5 (Conv2D)              (None, 12, 3, 512)        1049088   \n",
            "_________________________________________________________________\n",
            "Reshape (Reshape)            (None, 12, 1536)          0         \n",
            "_________________________________________________________________\n",
            "Dense (Dense)                (None, 12, 64)            98368     \n",
            "_________________________________________________________________\n",
            "Dropout (Dropout)            (None, 12, 64)            0         \n",
            "_________________________________________________________________\n",
            "LSTM_1 (Bidirectional)       (None, 12, 512)           657408    \n",
            "_________________________________________________________________\n",
            "LSTM_2 (Bidirectional)       (None, 12, 512)           1574912   \n",
            "_________________________________________________________________\n",
            "Softmax (Dense)              (None, 12, 11)            5643      \n",
            "=================================================================\n",
            "Total params: 4,936,267\n",
            "Trainable params: 4,935,755\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IreKLxvgUTF",
        "outputId": "1410b4f8-b4bc-4fa6-abe1-f5fcdfbe26a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        }
      },
      "source": [
        "# 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 = K.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][:,:Max_Length]\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",
        "#  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( 8, 4, figsize=(10,12) )\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()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 32 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zsDMd64gUTJ"
      },
      "source": [
        "# Programming Guides  程式語法指南     \n",
        "\n",
        "> ## (1) Image Preprocessing  圖片預處理  \n",
        "\n",
        "- ### [Module: tf.image](https://www.tensorflow.org/api_docs/python/tf/image)、[tf.image.convert_image_dtype](https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype)、[tf.image.resize](https://www.tensorflow.org/api_docs/python/tf/image/resize)  \n",
        "- ### [tf.io.read_file](https://www.tensorflow.org/api_docs/python/tf/io/read_file)、[tf.io.decode_png](https://www.tensorflow.org/api_docs/python/tf/io/decode_png)\n",
        "\n",
        "> ## (2) Modeling  建模  \n",
        "  \n",
        "- ### [Making new layers and models via subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/)\n",
        "- ### [tf.keras.layers.experimental.preprocessing.StringLookup](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/StringLookup)\n",
        "- ### [tf.Tensor](https://www.tensorflow.org/api_docs/python/tf/Tensor)、[tf.reshape](https://www.tensorflow.org/api_docs/python/tf/reshape)、[tf.cast](https://www.tensorflow.org/api_docs/python/tf/cast)、[tf.nn.ctc_loss](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss)、[tf.keras.backend.ctc_batch_cost](https://www.tensorflow.org/api_docs/python/tf/keras/backend/ctc_batch_cost)    \n",
        "\n",
        "- ### [tf.keras.layers.Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D)、[tf.keras.layers.MaxPool2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D)、[tf.keras.layers.Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)、<br>[tf.keras.layers.Bidirectional](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional)、[tf.keras.layers.LSTM](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM)\n",
        "\n",
        "- ### [tf.keras.optimizers.Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam)  \n",
        "- ### [tf.keras.callbacks.ModelCheckpoint](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint)、[tf.keras.callbacks.EarlyStopping](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping)"
      ]
    }
  ]
}