attention = Dense(1, activation='tanh')(concatenate([flatten1, flatten2]))含义
时间: 2024-06-03 13:12:14 浏览: 8
这行代码是将两个输入的张量通过 `concatenate` 进行拼接,然后将拼接后的张量输入到一个全连接层中(即 `Dense(1)`)进行处理,激活函数为双曲正切函数(`tanh`)。这个全连接层的作用是为了计算两个输入张量的权重之和,用于加权求和。因为 `Dense(1)` 只有一个神经元,所以它的输出是一个标量值。最后,双曲正切函数将输出值映射到 [-1, 1] 的范围内。
相关问题
请把下面单步预测改变多步预测 : concat = concatenate([lstm_out1,lstm_out2]) # 增加一个TimeDistributed层,以便对每个时间步进行相同的处理 td = TimeDistributed(Dense(128, activation='relu'))(concat) td = TimeDistributed(Dropout(0.2))(td) lstm_out = LSTM(64, return_sequences=False)(td) # 加入LSTM层 lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程,为预测添加线性成份,同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维 保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) """ highway_window = config.highway_window z = input1[:, -highway_window:, :] z = Flatten()(K.permute_dimensions(z, (0, 2, 1))) z = Dense(trainY.shape[1])(z) """ res = add([res,z]) res = Activation('sigmoid')(res) model = Model(inputs=[input_data1,input_data2], outputs=res) return model
修改后的代码如下:
```
concat = concatenate([lstm_out1, lstm_out2]) # 增加一个TimeDistributed层,以便对每个时间步进行相同的处理
td = TimeDistributed(Dense(128, activation='relu'))(concat)
td = TimeDistributed(Dropout(0.2))(td)
lstm_out = LSTM(64, return_sequences=True)(td) # 加入LSTM层,并设置return_sequences=True,以便进行多步预测
lstm_out = Dense(32, activation='relu')(lstm_out)
lstm_out = Dense(16, activation='tanh')(lstm_out)
res = Dense(trainY.shape[1])(lstm_out)
#highway 使用Dense模拟AR自回归过程,为预测添加线性成份,同时使输出可以响应输入的尺度变化。
highway_window = config.highway_window
# 截取近3个窗口的时间维 保留了所有的输入维度
z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1)
z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z)
z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z)
z = Dense(trainY.shape[1])(z)
# 多步预测
for i in range(config.num_steps-1):
z = concatenate([z, res]) # 把上一步的预测结果作为输入
z = Dense(trainY.shape[1])(z)
res = add([res, z])
res = Activation('sigmoid')(res)
model = Model(inputs=[input_data1, input_data2], outputs=res)
return model
```
主要修改的部分是在加入LSTM层后,将return_sequences设置为True,以便进行多步预测。然后,在高速公路网络的部分,我们使用一个for循环来进行多步预测。在每个时间步,我们将上一步的预测结果与输入连接起来,并使用Dense层进行预测。最后,我们将所有预测结果相加,并使用sigmoid激活函数进行输出。
CNN、LSTM、Attention、RESnet基于tensorflow实现时序预测的完整代码
以下是一个使用CNN、LSTM、Attention和RESnet模型进行时序预测的完整代码示例:
``` python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, Dense, Flatten, LSTM, Dropout, Activation, Reshape, Lambda, Multiply, Add, Concatenate, BatchNormalization
from tensorflow.keras.models import Model
# 定义CNN模型
def cnn_model(input_shape, output_shape):
input_layer = Input(shape=input_shape)
conv1 = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')(input_layer)
pool1 = MaxPooling1D(pool_size=2)(conv1)
conv2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(pool1)
pool2 = MaxPooling1D(pool_size=2)(conv2)
fc1 = Flatten()(pool2)
fc1 = Dense(64, activation='relu')(fc1)
output_layer = Dense(output_shape)(fc1)
model = Model(inputs=input_layer, outputs=output_layer)
return model
# 定义LSTM模型
def lstm_model(input_shape, output_shape):
input_layer = Input(shape=input_shape)
lstm1 = LSTM(units=64, return_sequences=True)(input_layer)
lstm2 = LSTM(units=64)(lstm1)
fc1 = Dense(64, activation='relu')(lstm2)
output_layer = Dense(output_shape)(fc1)
model = Model(inputs=input_layer, outputs=output_layer)
return model
# 定义Attention模型
def attention_model(input_shape, output_shape):
input_layer = Input(shape=input_shape)
lstm1 = LSTM(units=64, return_sequences=True)(input_layer)
lstm2 = LSTM(units=64, return_sequences=True)(lstm1)
attention = Dense(1, activation='tanh')(lstm2)
attention = Flatten()(attention)
attention = Activation('softmax')(attention)
attention = RepeatVector(64)(attention)
attention = Permute([2, 1])(attention)
attended = Multiply()([lstm2, attention])
output_layer = Lambda(lambda x: K.sum(x, axis=1))(attended)
model = Model(inputs=input_layer, outputs=output_layer)
return model
# 定义RESnet模型
def resnet_model(input_shape, output_shape):
input_layer = Input(shape=input_shape)
conv1 = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')(input_layer)
conv2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(conv1)
res1 = Add()([conv1, conv2])
conv3 = Conv1D(filters=128, kernel_size=3, padding='same', activation='relu')(res1)
conv4 = Conv1D(filters=256, kernel_size=3, padding='same', activation='relu')(conv3)
res2 = Add()([conv3, conv4])
fc1 = Flatten()(res2)
fc1 = Dense(64, activation='relu')(fc1)
output_layer = Dense(output_shape)(fc1)
model = Model(inputs=input_layer, outputs=output_layer)
return model
# 定义训练数据和标签
train_data = ...
train_labels = ...
# 定义模型输入和输出的形状
input_shape = (train_data.shape[1], train_data.shape[2])
output_shape = train_labels.shape[1]
# 创建并编译CNN模型
cnn = cnn_model(input_shape, output_shape)
cnn.compile(loss='mse', optimizer='adam')
# 创建并编译LSTM模型
lstm = lstm_model(input_shape, output_shape)
lstm.compile(loss='mse', optimizer='adam')
# 创建并编译Attention模型
attention = attention_model(input_shape, output_shape)
attention.compile(loss='mse', optimizer='adam')
# 创建并编译RESnet模型
resnet = resnet_model(input_shape, output_shape)
resnet.compile(loss='mse', optimizer='adam')
# 训练模型
cnn.fit(train_data, train_labels, epochs=100, batch_size=64)
lstm.fit(train_data, train_labels, epochs=100, batch_size=64)
attention.fit(train_data, train_labels, epochs=100, batch_size=64)
resnet.fit(train_data, train_labels, epochs=100, batch_size=64)
# 使用模型进行预测
test_data = ...
cnn_pred = cnn.predict(test_data)
lstm_pred = lstm.predict(test_data)
attention_pred = attention.predict(test_data)
resnet_pred = resnet.predict(test_data)
```
注意,以上代码只是一个示例,实际应用中需要根据具体的数据和任务进行调整。
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