CreateVggGraphWeights.py 文件源码

def VGG_16_graph():
    model = Graph()
    model.add_input(name='input', input_shape=(3, 224, 224))
    model.add_node(ZeroPadding2D((1,1)), name='pad1', input='input')
    model.add_node(Convolution2D(64, 3, 3, activation='relu'), name='relu1', input='pad1') # weights=sequence_model.layers[1].W.container
    model.add_node(ZeroPadding2D((1,1)), name='pad2', input='relu1')
    model.add_node(Convolution2D(64, 3, 3, activation='relu'), name='relu2', input='pad2')
    model.add_node(MaxPooling2D((2,2), strides=(2,2)), name='pool1', input='relu2')

    model.add_node(ZeroPadding2D((1,1)), name='1', input='pool1')
    model.add_node(Convolution2D(128, 3, 3, activation='relu'), name='2', input='1')
    model.add_node(ZeroPadding2D((1,1)), name='3', input='2')
    model.add_node(Convolution2D(128, 3, 3, activation='relu'), name='4', input='3')
    model.add_node(MaxPooling2D((2,2), strides=(2,2)), name='5', input='4')

    model.add_node(ZeroPadding2D((1,1)), name='6', input='5')
    model.add_node(Convolution2D(256, 3, 3, activation='relu'), name='7', input='6')
    model.add_node(ZeroPadding2D((1,1)), name='8', input='7')
    model.add_node(Convolution2D(256, 3, 3, activation='relu'), name='9', input='8')
    model.add_node(ZeroPadding2D((1,1)), name='10', input='9')
    model.add_node(Convolution2D(256, 3, 3, activation='relu'), name='11', input='10')
    model.add_node(MaxPooling2D((2,2), strides=(2,2)), name='12', input='11')

    model.add_node(ZeroPadding2D((1,1)), name='13', input='12')
    model.add_node(Convolution2D(512, 3, 3, activation='relu'), name='14', input='13')
    model.add_node(ZeroPadding2D((1,1)), name='15', input='14')
    model.add_node(Convolution2D(512, 3, 3, activation='relu'), name='16', input='15')
    model.add_node(ZeroPadding2D((1,1)), name='17', input='16')
    model.add_node(Convolution2D(512, 3, 3, activation='relu'), name='18', input='17')
    model.add_node(MaxPooling2D((2,2), strides=(2,2)), name='19', input='18')

    model.add_node(ZeroPadding2D((1,1)), name='20', input='19')
    model.add_node(Convolution2D(512, 3, 3, activation='relu'), name='21', input='20')
    model.add_node(ZeroPadding2D((1,1)), name='22', input='21')
    model.add_node(Convolution2D(512, 3, 3, activation='relu'), name='23', input='22')
    model.add_node(ZeroPadding2D((1,1)), name='24', input='23')
    model.add_node(Convolution2D(512, 3, 3, activation='relu'), name='25', input='24')
    model.add_node(MaxPooling2D((2,2), strides=(2,2)), name='26', input='25')

    model.add_node(Flatten(), name='27', input='26')
    model.add_node(Dense(4096, activation='relu'), name='28', input='27')
    model.add_node(Dropout(0.5), name='29', input='28')
    model.add_node(Dense(4096, activation='relu'), name='30', input='29')
    model.add_node(Dropout(0.5), name='31', input='30')
    model.add_node(Dense(1000, activation='softmax'), name='32', input='31')

    model.add_output(input='32', name='output')
    return model