CNN_LSTM.py 文件源码

def build_CNN_LSTM(channels, width, height, lstm_output_size, nb_classes):
    model = Sequential()
    # 1 conv
    model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu', 
        input_shape=(channels, height, width)))
    model.add(BatchNormalization(mode=0, axis=1))
    # 2 conv
    model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
    model.add(BatchNormalization(mode=0, axis=1))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
    # 3 conv
    model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
    model.add(BatchNormalization(mode=0, axis=1))
    # 4 conv
    model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
    model.add(BatchNormalization(mode=0, axis=1))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
    # flaten
    a = model.add(Flatten())
    # 1 dense
    model.add(Dense(512, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    # 2 dense
    model.add(Dense(512, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    # lstm
    model.add(RepeatVector(lstm_output_size))
    model.add(LSTM(512, return_sequences=True))
    model.add(TimeDistributed(Dropout(0.5)))
    model.add(TimeDistributed(Dense(nb_classes, activation='softmax')))
    model.summary()
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=[categorical_accuracy_per_sequence],
                  sample_weight_mode='temporal'
                  )

    return model