test_pred = pred.detach().cpu().numpy().reshape((7, 2)) test_label = test_label_tensor.cpu().numpy().reshape((7, 2))什么意思

这段代码是将PyTorch张量(pred和test_label_tensor)转换为NumPy数组，并且重新调整形状为(7,2)的二维数组。

首先，`detach()`函数是将一个张量从计算图中分离出来，返回一个新的张量，该新张量的requires_grad属性为False，这样可以避免对原始张量进行计算梯度。

接着，`cpu()`函数将张量从GPU转移到CPU上，因为NumPy数组只能在CPU上使用。

最后，`numpy()`函数将张量转换为NumPy数组，`reshape()`函数将数组重新调整为7行2列的二维数组，以便后续处理。

相关问题

plot_size = 730 plt.figure(figsize=(12, 8)) plt.plot(scaler.inverse_transform((model(x_train_tensor).detach().numpy()[: plot_size]).reshape(-1, 1)), "b") plt.plot(scaler.inverse_transform(y_train_tensor.detach().numpy().reshape(-1, 1)[: plot_size]), "r") plt.legend() plt.show() y_test_pred = model(x_test_tensor) plt.figure(figsize=(12, 8)) plt.plot(scaler.inverse_transform(y_test_pred.detach().numpy()[: plot_size]), "b") plt.plot(scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)[: plot_size]), "r") plt.legend() plt.show()这段代码后加上图片保存到文件夹A中，且图片分别以文件名1、文件名2命名

可以在代码中添加如下代码，实现保存图片到文件夹A中，并分别以文件名1、文件名2命名：

# 保存训练集预测结果图片
plt.figure(figsize=(12, 8))
plt.plot(scaler.inverse_transform((model(x_train_tensor).detach().numpy()[: plot_size]).reshape(-1, 1)), "b")
plt.plot(scaler.inverse_transform(y_train_tensor.detach().numpy().reshape(-1, 1)[: plot_size]), "r")
plt.legend()
plt.savefig('./A/文件名1.png')

# 保存测试集预测结果图片
y_test_pred = model(x_test_tensor)
plt.figure(figsize=(12, 8))
plt.plot(scaler.inverse_transform(y_test_pred.detach().numpy()[: plot_size]), "b")
plt.plot(scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)[: plot_size]), "r")
plt.legend()
plt.savefig('./A/文件名2.png')

其中，`plt.savefig()`函数可以将图片保存到指定路径和文件名下。需要注意的是，如果`A`文件夹不存在，需要先创建该文件夹，可以使用`os.makedirs()`函数来创建。

import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码，并写出预测值和真实值的代码

以下是适当修改后的代码以及预测值和真实值的代码：

import numpy as np 
import matplotlib.pyplot as plt 
import math 
import torch 
from torch import nn 
from torch.utils.data import DataLoader, Dataset 
import os 

os.environ['KMP_DUPLICATE_LIB_OK']='True' 

dataset = [] 
for data in np.arange(0, 3, .01): 
    data = math.sin(data * math.pi) 
    dataset.append(data) 

dataset = np.array(dataset) 
dataset = dataset.astype('float32') 

max_value = np.max(dataset) 
min_value = np.min(dataset) 

scalar = max_value - min_value 
print(scalar) 

dataset = list(map(lambda x: x / scalar, dataset)) 

def create_dataset(dataset, look_back=3): 
    dataX, dataY = [], [] 
    for i in range(len(dataset) - look_back): 
        a = dataset[i:(i + look_back)] 
        dataX.append(a) 
        dataY.append(dataset[i + look_back]) 
    return np.array(dataX), np.array(dataY) 

data_X, data_Y = create_dataset(dataset) 
train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] 
test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] 

train_X = train_X.reshape(-1, 1, 3).astype('float32') 
train_Y = train_Y.reshape(-1, 1, 1).astype('float32') 
test_X = test_X.reshape(-1, 1, 3).astype('float32') 

train_X = torch.from_numpy(train_X) 
train_Y = torch.from_numpy(train_Y) 
test_X = torch.from_numpy(test_X) 

class RNN(nn.Module): 
    def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): 
        super(RNN, self).__init__() 
        self.input_size = input_size 
        self.hidden_size = hidden_size 
        self.output_size = output_size 
        self.num_layer = num_layer 
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) 
        self.linear = nn.Linear(hidden_size, output_size) 

    def forward(self, x): 
        out, h = self.rnn(x) 
        out = self.linear(out[:, -1, :]) 
        return out 

net = RNN(3, 20) 

criterion = nn.MSELoss(reduction='mean') 
optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) 

train_loss = [] 
test_loss = [] 

for e in range(1000): 
    pred = net(train_X) 
    loss = criterion(pred, train_Y) 
    optimizer.zero_grad() 
    loss.backward() 
    optimizer.step() 
    if (e + 1) % 100 == 0: 
        print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) 
    train_loss.append(loss.item()) 

plt.plot(train_loss, label='train_loss') 
plt.legend() 
plt.show() 

# 预测值和真实值的代码
net.eval() 
pred_test = net(test_X) 
pred_test = pred_test.detach().numpy() * scalar 
test_Y = test_Y.reshape(-1, 1) * scalar 
plt.plot(pred_test, label='predict') 
plt.plot(test_Y, label='true') 
plt.legend() 
plt.show()