ImageCaption/task1.py at master · songdongqing/ImageCaption · GitHub

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from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D


def generate_vgg16():
    """
    搭建VGG16网络结构
    :return: VGG16网络
    """
    input_shape = (224, 224, 3)
    model = Sequential([

        Conv2D(64, (3, 3), input_shape=input_shape, padding='same', activation='relu'),
        Conv2D(64, (3, 3), padding='same', activation='relu'),
        MaxPooling2D(pool_size=(2,2), strides=(2,2)),

        Conv2D(128, (3, 3), padding='same', activation='relu'),
        Conv2D(128, (3, 3), padding='same', activation='relu'),
        MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),

        Conv2D(256, (3, 3), padding='same', activation='relu'),
        Conv2D(256, (3, 3), padding='same', activation='relu'),
        Conv2D(256, (3, 3), padding='same', activation='relu'),
        MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),

        Conv2D(512, (3, 3), padding='same', activation='relu'),
        Conv2D(512, (3, 3), padding='same', activation='relu'),
        Conv2D(512, (3, 3), padding='same', activation='relu'),
        MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),

        Conv2D(512, (3, 3), padding='same', activation='relu'),
        Conv2D(512, (3, 3), padding='same', activation='relu'),
        Conv2D(512, (3, 3), padding='same', activation='relu'),
        MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),

        Flatten(),
        Dense(4096, activation='relu'),
        Dense(4096, activation='relu'),
        Dense(1000, activation='softmax')
    ])

    return model

if __name__ == '__main__':
    model = generate_vgg16()
    model.summary()
    # model.compile(loss='categorical_crossentropy', optimizer='adam')
    # model.fit()