STConvolution.py 文件源码

def seq3DCNN(n_flow=4, seq_len=3, map_height=32, map_width=32):
    model=Sequential()
    # model.add(ZeroPadding3D(padding=(0, 1, 1), input_shape=(n_flow, seq_len, map_height, map_width)))
    # model.add(Convolution3D(64, 2, 3, 3, border_mode='valid'))
    model.add(Convolution3D(64, 2, 3, 3, border_mode='same', input_shape=(n_flow, seq_len, map_height, map_width)))
    model.add(Activation('relu'))

    model.add(Convolution3D(128, 2, 3, 3, border_mode='same'))
    model.add(Activation('relu'))

    model.add(Convolution3D(64, 2, 3, 3, border_mode='same'))
    model.add(Activation('relu'))

    model.add(ZeroPadding3D(padding=(0, 1, 1)))
    model.add(Convolution3D(n_flow, seq_len, 3, 3, border_mode='valid'))
    # model.add(Convolution3D(n_flow, seq_len-2, 3, 3, border_mode='same'))
    model.add(Activation('tanh'))
    return model