请加入卷积给我的例子 ： # 定义输入张量 input_data1 = Input(shape=(time_steps1, input_dim1)) #lstm1 = input_data1 # 对于维的注意力机制 attention_mul1 = attention_3d_block(input_data1, 'dim_reduction1' ,'attention_vec1' ) attention_mul1 = Dense(64, activation='relu')(attention_mul1) # 定义时间步注意力机制 attention_time1 = TimeDistributed(Dense(1, activation='tanh'))(attention_mul1) attention_time1 = Activation('softmax')(attention_time1) context_time1 = Dot(axes=1)([attention_time1, attention_mul1]) # 定义维度注意力机制 attention_dim1 = Dense(input_dim1, activation='tanh')(context_time1) attention_dim1 = Activation('softmax')(attention_dim1) context_dim1 = Dot(axes=1)([attention_dim1, context_time1]) # 定义第二层时间步注意力机制 attention_time2 = TimeDistributed(Dense(1, activation='tanh'))(context_dim1) attention_time2 = Activation('softmax')(attention_time2) context_time2 = Dot(axes=1)([attention_time2, context_dim1]) # 定义第二层维度注意力机制 attention_dim2 = Dense(input_dim1, activation='tanh')(context_time2) attention_dim2 = Activation('softmax')(attention_dim2) context_dim2 = Dot(axes=1)([attention_dim2, context_time2]) lstm1 = LSTM(128, return_sequences=True)(context_dim2) # 加入LSTM层 lstm_out1 = Dense(64, activation='relu')(lstm1) lstm_out1 = Dense(32, activation='tanh')(lstm_out1) lstm_out1 = Dense(16, activation='softmax')(lstm_out1)

好的，我来为您添加卷积层的代码，如下所示：

from keras.layers import Conv1D, MaxPooling1D, Flatten

# 定义输入张量
input_data1 = Input(shape=(time_steps1, input_dim1))
# 添加卷积层
conv1d_1 = Conv1D(filters=64, kernel_size=3, activation='relu')(input_data1)
# 对于维的注意力机制
attention_mul1 = attention_3d_block(conv1d_1, 'dim_reduction1' ,'attention_vec1' )
attention_mul1 = Dense(64, activation='relu')(attention_mul1)
# 定义时间步注意力机制
attention_time1 = TimeDistributed(Dense(1, activation='tanh'))(attention_mul1)
attention_time1 = Activation('softmax')(attention_time1)
context_time1 = Dot(axes=1)([attention_time1, attention_mul1])
# 定义维度注意力机制
attention_dim1 = Dense(input_dim1, activation='tanh')(context_time1)
attention_dim1 = Activation('softmax')(attention_dim1)
context_dim1 = Dot(axes=1)([attention_dim1, context_time1])
# 定义第二层时间步注意力机制
attention_time2 = TimeDistributed(Dense(1, activation='tanh'))(context_dim1)
attention_time2 = Activation('softmax')(attention_time2)
context_time2 = Dot(axes=1)([attention_time2, context_dim1])
# 定义第二层维度注意力机制
attention_dim2 = Dense(input_dim1, activation='tanh')(context_time2)
attention_dim2 = Activation('softmax')(attention_dim2)
context_dim2 = Dot(axes=1)([attention_dim2, context_time2])
# 加入LSTM层
lstm1 = LSTM(128, return_sequences=True)(context_dim2)
lstm_out1 = Dense(64, activation='relu')(lstm1)
lstm_out1 = Dense(32, activation='tanh')(lstm_out1)
lstm_out1 = Dense(16, activation='softmax')(lstm_out1)

这里在输入张量上增加了一个1D卷积层，卷积核大小为3，过滤器数为64，激活函数为ReLU。卷积层输出的张量被送入注意力机制模块中进行处理。之后的代码与原来的代码完全相同，只是在输入张量和注意力机制之间增加了一个卷积层。