plt.plot(value1.index[-181:-1], y_test_scaled, color='red')

这行代码使用了 Matplotlib 库中的 plot() 函数来画出一条红色的线图。其中 value1.index[-181:-1] 表示 x 轴的数据，y_test_scaled 表示 y 轴的数据。这段代码的作用是将 y_test_scaled 数据以线图的形式展示出来，其中 x 轴的数据为 value1 中倒数第 181 到倒数第 2 个数据。

相关问题

加载数据 X = data.iloc[:, :-1].values y = data.iloc[:, -1:].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练模型 input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.0016 num_epochs=2000 loss_history=nn.train(X, y, learning_rate, num_epochs) plt.plot(loss_history) plt.title('loss') plt.xlabel('Epoch') plt.ylabel('MSE') X_pred = data_pred.iloc[:, :-1].values y_pred = data_pred.iloc[:, -1:].values y_pred = nn.predict(X_pred)报错NameError: name 'data_pred' is not defined解决代码

你需要先定义 `data_pred` 变量并赋值，再执行 `X_pred = data_pred.iloc[:, :-1].values` 和 `y_pred = data_pred.iloc[:, -1:].values` 这两行代码。例如：

import pandas as pd

# 加载数据
data = pd.read_csv('train_data.csv')
data_pred = pd.read_csv('predict_data.csv')

X = data.iloc[:, :-1].values
y = data.iloc[:, -1:].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
input_dim = 13
hidden_dim = 25
output_dim = 1
nn = NeuralNetwork(input_dim, hidden_dim, output_dim)
learning_rate = 0.0016
num_epochs = 2000
loss_history = nn.train(X, y, learning_rate, num_epochs)
plt.plot(loss_history)
plt.title('loss')
plt.xlabel('Epoch')
plt.ylabel('MSE')

X_pred = data_pred.iloc[:, :-1].values
y_pred = data_pred.iloc[:, -1:].values
y_pred = nn.predict(X_pred)

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。