CNN、LSTM、Attention、RESnet基于tensorflow实现时序预测的完整代码
时间: 2023-07-10 11:23:17 浏览: 69
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。