import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense,LSTM,Dropout from keras.models import Sequential # 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据归一化 max = np.max(X) X = X / max Y = Y / max # 划分训练集、验证集、测试集 train_x = X[:1000] train_y = Y[:1000] val_x = X[1000:1150] val_y = Y[1000:1150] test_x = X[1150:] test_y = Y # 构建LSTM模型 model = Sequential() model.add(LSTM(units=64, input_shape=(5, 109))) model.add(Dropout(0.2)) model.add(Dense(units=109, activation='linear')) model.summary() # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False) # 评估模型 test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # 预测结果可视化 plt.figure(figsize=(20, 8)) plt.plot(train_y[-100:], label='true') plt.plot(train_predict[-100:], label='predict') plt.legend() plt.title('Training set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(val_y[-50:], label='true') plt.plot(val_predict[-50:], label='predict') plt.legend() plt.title('Validation set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(test_y[:50], label='true') plt.plot(test_predict[:50], label='predict') plt.legend() plt.title('Test set') plt.show()如何用返回序列修改这段程序

如果你想使用返回序列的LSTM来修改这段程序，需要在LSTM层中设置return_sequences参数为True，并修改Dense层的输出单元数。下面是修改后的代码：

import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Dense, LSTM, Dropout
from keras.models import Sequential

# 加载数据
X = np.load("X_od.npy")
Y = np.load("Y_od.npy")

# 数据归一化
max = np.max(X)
X = X / max
Y = Y / max

# 划分训练集、验证集、测试集
train_x = X[:1000]
train_y = Y[:1000]
val_x = X[1000:1150]
val_y = Y[1000:1150]
test_x = X[1150:]
test_y = Y

# 构建LSTM模型
model = Sequential()
model.add(LSTM(units=64, input_shape=(5, 109), return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(units=1, activation='linear'))
model.summary()

# 编译模型
model.compile(optimizer='adam', loss='mse')

# 训练模型
history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False)

# 评估模型
test_loss = model.evaluate(test_x, test_y)
print('Test loss:', test_loss)

# 模型预测
train_predict = model.predict(train_x)
val_predict = model.predict(val_x)
test_predict = model.predict(test_x)

# 预测结果可视化
plt.figure(figsize=(20, 8))
plt.plot(train_y[-100:], label='true')
plt.plot(train_predict[-100:], label='predict')
plt.legend()
plt.title('Training set')
plt.show()

plt.figure(figsize=(20, 8))
plt.plot(val_y[-50:], label='true')
plt.plot(val_predict[-50:], label='predict')
plt.legend()
plt.title('Validation set')
plt.show()

plt.figure(figsize=(20, 8))
plt.plot(test_y[:50], label='true')
plt.plot(test_predict[:50], label='predict')
plt.legend()
plt.title('Test set')
plt.show()

需要注意的是，由于返回序列的LSTM层输出的是一个序列，所以在Dense层中输出单元数应该为1而不是109。