% CNN-LSTM网络 layers = [ sequenceInputLayer(inputSize,MinLength=minLength) convolution2dLayer(filterSize,numFilters,Padding="same") batchNormalizationLayer reluLayer maxPooling2dLayer(2,Stride=2) convolution2dLayer(filterSize,numFilters,Padding="same") batchNormalizationLayer reluLayer maxPooling2dLayer([4 2],Stride=[4 2]) convolution2dLayer(filterSize,2numFilters,Padding="same") batchNormalizationLayer reluLayer maxPooling2dLayer([4 2],Stride=[4 2]) convolution2dLayer(filterSize,2numFilters,Padding="same") batchNormalizationLayer reluLayer maxPooling2dLayer([4 2],Stride=[4 2]) flattenLayer lstmLayer(numHiddenUnits,OutputMode="last") fullyConnectedLayer(numClasses) softmaxLayer classificationLayer];把它改成再改成这种形式的def C_LSTM_model(input_size): model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_size, 1))) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(LSTM(32)) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) return model的代码

你需要将原始的 MATLAB 代码转换为 Python 代码。下面是您需要的 Python 代码：

from keras.models import Sequential
from keras.layers import Conv1D, MaxPooling1D, Dropout, LSTM, Dense

def C_LSTM_model(input_size):
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_size, 1)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Dropout(0.5))
    model.add(LSTM(32))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    return model

此代码定义了一个使用 Conv1D、MaxPooling1D、Dropout、LSTM、Dense 层的序列模型。它接受输入大小为 input_size 的一维向量，输出一个二分类结果。您可以根据需要修改参数。