Multi-channel cnn+lstm+dense complex network test (with single-channel effect comparison)

For time series data or images, different filters, kernel_size, and strides mean that different high-dimensional features are output.
The same piece of data, how to enhance the expressive ability of the network. Can a wider network achieve better results than a deep network? This is the problem that this article needs to solve.

For data description, please refer to the previous article https://www.jianshu.com/p/21b96d597367

Network structure

# 定义多通道特征组合模型
def build_multi_cr_lstm_model(ts, fea_dim):

    # 定义输入
    inputs = Input(shape = (ts, fea_dim))

    # ########################################
    # cnn层&lstm层1
    cnn_left_out1 = Conv1D(filters=50, kernel_size=6, strides=3, kernel_initializer=he_normal(seed=3))(inputs)
    act_left_out1 = LeakyReLU()(cnn_left_out1)

    lstm_left_out1 = LSTM(64, activation='sigmoid', dropout=0.1, return_sequences=False, 
                         kernel_initializer=he_normal(seed=10))(act_left_out1)

    # #########################################
    # cnn层&lstm层2
    cnn_right_out1 = Conv1D(filters=50, kernel_size=12, strides=3, kernel_initializer=he_normal(seed=3))(inputs)
    act_right_out1 = LeakyReLU()(cnn_right_out1)

    lstm_right_out1 = LSTM(64, activation='sigmoid', dropout=0.1, return_sequences=False, 
                         kernel_initializer=he_normal(seed=10))(act_right_out1)
    
    # #########################################
    # cnn层&lstm层3
    cnn_mid_out1 = Conv1D(filters=50, kernel_size=6, strides=2, kernel_initializer=he_normal(seed=3))(inputs)
    act_mid_out1 = LeakyReLU()(cnn_mid_out1)

    lstm_mid_out1 = LSTM(64, activation='sigmoid', dropout=0.1, return_sequences=False, 
                         kernel_initializer=he_normal(seed=10))(act_mid_out1)

    # ############################################
    # 上层叠加新的dense层
    concat_output = Concatenate(axis=1)([lstm_left_out1, lstm_mid_out1, lstm_right_out1])
    outputs = Dense(1)(concat_output)
    model_func = Model(inputs=inputs, outputs=outputs)
    model_func.compile(loss='mse', optimizer=Adam(lr=0.002, decay=0.01), metrics=['mse'])
    
    return model_func

image.png

It can be seen that inputs are used as input data for the three networks. CNN1+LSMT1, CNN2+LSTM2, CNN3+LSTM3 obtained 3 high-dimensional features respectively, and finally connected to the dense layer to output the prediction results

Comparison of prediction results with the results in the above article

baseline effect:

The normalized mse and original mse are 0.00167 and 1606 respectively
Complex network cnn+lstm single channel effect:

The normalized mse and original mse are 0.0090666 and 869 respectively
Complex network cnn+lstm multi-channel effect:

The normalized mse and original mse are 0.0008297 and 795 respectively

It can be seen that the effect of the complex network is extremely improved, mse is reduced by a full 50%, but the improvement of multi-channel is relatively small compared with single-channel. If time performance is pursued, the single channel of cnn+lsmt+dense may already be able to meet the demand in most cases

Conjecture on the reason for the improvement

The cnn with different parameter settings obtains different high-dimensional feature expressions, which enriches the feature input of the model, and thus obtains a better prediction effect

Comparison of fitted curves

The first picture is the deep lstm network fitting diagram, the second picture is the cnn+lstm single-channel fitting diagram, and the third picture is the cnn+lstm multi-channel network fitting diagram.

image.png

image.png

Thinking

For different data, how to apply the same complex network is a question that needs to be considered in the next step. And the introduction of attention

Author: yangy_fly
link: https: //www.jianshu.com/p/c428efc6966e
Source: Jane books
are copyrighted by the author. For commercial reprints, please contact the author for authorization, and for non-commercial reprints, please indicate the source.

Multi-channel cnn+lstm+dense complex network test (with single-channel effect comparison)

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