attention_mul = Flatten()(attention_mul)
时间: 2023-09-08 20:14:08 浏览: 41
这是一个Keras中的层,用于将一个张量展平成一个向量。在这个语句中,它被用于将经过注意力机制处理后的张量展平成一个向量,以便后续的处理。具体来说,如果输入张量的形状为(batch_size, seq_len, hidden_size),则经过Flatten层处理后的形状为(batch_size, seq_len*hidden_size)。
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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。
def cnn_lstm_attention_model(n_input, n_out, n_features): inputs = Input(shape=(n_input, n_features)) x = Conv1D(filters=64, kernel_size=1, activation='relu')(inputs) # , padding = 'same' x = Dropout(0.3)(x) lstm_out = Bidirectional(LSTM(128, return_sequences=True))(x) lstm_out = Dropout(0.3)(lstm_out) attention_mul = attention_block(lstm_out, n_input) attention_mul = Flatten()(attention_mul)#扁平层,变为一维数据 output = Dense(n_out, activation='sigmoid')(attention_mul) model = Model(inputs=[inputs], outputs=output) model.summary() model.compile(loss="mse", optimizer='adam') return model 什莫意思
这段代码是一个深度学习模型的定义,用于创建一个具有卷积神经网络(CNN)、长短期记忆网络(LSTM)和注意力机制的模型。以下是对每个部分的解释:
- 输入层:定义输入的形状,包括时间步长(n_input)和特征数量(n_features)。
- 卷积层:使用卷积运算提取输入数据的空间特征,其中使用了64个过滤器和1维卷积核。
- Dropout层:随机丢弃一定比例的神经元,以防止过拟合。
- 双向LSTM层:使用128个隐藏单元的双向LSTM层,可以学习输入序列的长期依赖关系,并返回完整的序列。
- 注意力层:应用注意力机制,以加强对输入序列中重要部分的关注。
- 扁平层:将注意力层的输出扁平化为一维数据。
- 全连接层:使用sigmoid激活函数将扁平化的数据映射到输出空间。
- 模型编译和返回:定义模型的损失函数和优化器,并返回构建的模型。
这个模型可以用于处理序列数据,并在输出层进行二分类预测。
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