cnn-lstm注意力机制代码

以下是使用Keras实现CNN-LSTM注意力机制的代码示例：

from keras.layers import Input, Dense, Dropout, Conv2D, MaxPooling2D, Flatten, LSTM, concatenate, Permute, Multiply, Reshape
from keras.models import Model

# input layer
inputs = Input(shape=(timesteps, height, width, channels))

# CNN layers
cnn = Conv2D(64, (3,3), activation='relu')(inputs)
cnn = MaxPooling2D(pool_size=(2, 2))(cnn)
cnn = Conv2D(64, (3,3), activation='relu')(cnn)
cnn = MaxPooling2D(pool_size=(2, 2))(cnn)
cnn = Flatten()(cnn)
cnn = Dropout(0.5)(cnn)
cnn = Dense(128, activation='relu')(cnn)

# LSTM layers
lstm = LSTM(128, return_sequences=True)(inputs)
lstm = LSTM(128)(lstm)

# attention mechanism
attention = concatenate([cnn, lstm], axis=1)
attention = Dense(64, activation='tanh')(attention)
attention = Dense(1, activation='softmax')(attention)
attention = Permute((2, 1))(attention)
attention = Multiply()([lstm, attention])
attention = Reshape((128,))(attention)

# output layer
outputs = Dense(num_classes, activation='softmax')(attention)

# compile the model
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

在上面的代码中，我们首先定义了输入层，然后将其传递到CNN和LSTM层中。 CNN层用于提取特征，而LSTM层用于处理序列数据。接下来，我们将CNN和LSTM的输出连接起来，并添加注意力机制以引导模型关注重要的特征。最后，我们定义了一个输出层，用于预测类别。