LRCN_keras.py 文件源码

def load_model():
    # use simple CNN structure
    in_shape = (SequenceLength, IMSIZE[0], IMSIZE[1], 3)
    model = Sequential()
    model.add(ConvLSTM2D(32, kernel_size=(7, 7), padding='valid', return_sequences=True, input_shape=in_shape))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))
    model.add(ConvLSTM2D(64, kernel_size=(5, 5), padding='valid', return_sequences=True))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))
    model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True))
    model.add(Activation('relu'))
    model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True))
    model.add(Activation('relu'))
    model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))
    model.add(Dense(320))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))

    out_shape = model.output_shape
    # print('====Model shape: ', out_shape)
    model.add(Reshape((SequenceLength, out_shape[2] * out_shape[3] * out_shape[4])))
    model.add(LSTM(64, return_sequences=False))
    model.add(Dropout(0.5))
    model.add(Dense(N_CLASSES, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])

    # model structure summary
    print(model.summary())

    return model