深度学习-VGG19网络识别手写汉字（基于HWDB1.1trn_gnt）

训练集地址：http://www.nlpr.ia.ac.cn/databases/download/feature_data/HWDB1.1trn_gnt.zip
测试集地址：http://www.nlpr.ia.ac.cn/databases/download/feature_data/HWDB1.1tst_gnt.zip

def read_from_gnt_dir(gnt_dir):
    def one_file(f):
        header_size = 10
        while True:
            header = np.fromfile(f, dtype='uint8', count=header_size)
            if not header.size: break
            sample_size = header[0] + (header[1] << 8) + (header[2] << 16) + (header[3] << 24)
            tagcode = header[5] + (header[4] << 8)
            width = header[6] + (header[7] << 8)
            height = header[8] + (header[9] << 8)
            if header_size + width * height != sample_size:
                break
            image = np.fromfile(f, dtype='uint8', count=width * height).reshape((height, width))
            yield image, tagcode

    f_list=os.listdir(gnt_dir)
    np.random.shuffle(f_list)
    for file_name in f_list:
        if file_name.endswith('.gnt'):
            file_path = os.path.join(gnt_dir, file_name)
            with open(file_path, 'rb') as f:
                for image, tagcode in one_file(f):
                    skip = np.random.randint(0, 2)
                    if skip == 0:
                        continue
                    yield image, tagcode

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
from keras.layers import Dropout

def VGG19(input_shape=(224, 224, 3), num_classes=1000, dropout_rate=0.5):
    # 定义输入层
    inputs = Input(shape=input_shape)

    # 第1段卷积-池化层
    x = Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu")(inputs)
    x = Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)

    # 第2段卷积-池化层
    x = Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)

    # 第3段卷积-池化层
    x = Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)

    # 第4段卷积-池化层
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)

    # 第5段卷积-池化层
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu")(x)
    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)

    # 全连接层
    x = Flatten()(x)
    x = Dense(units=4096, activation="relu")(x)
    x = Dropout(rate=dropout_rate)(x)
    x = Dense(units=4096, activation="relu")(x)
    x = Dropout(rate=dropout_rate)(x)
    outputs = Dense(units=num_classes, activation="softmax")(x)

    # 定义模型
    model = Model(inputs=inputs, outputs=outputs, name="VGG19")

    return model

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def trainning():
    set = test_generator_64(test_data_dir)
    print('loading model。。。')
    model=cnn_model(len(char_set))
    print('end loading')
    global batch_size

    model.fit_generator(generator=generator_data(),
                        steps_per_epoch=math.ceil(897758/batch_size)+1,
                        epochs=2,
                        validation_data=set,
                        callbacks=[generatorCallback()])
    print('训练完成')