plt.scatter(test_target_tensor.numpy(), final_output.numpy())

这行代码使用了Matplotlib库中的scatter函数，用于画散点图。其中，test_target_tensor和final_output都是Numpy数组，分别表示测试集的目标值和模型预测值。通过将这两个数组作为函数的输入，可以画出目标值和预测值之间的散点图。

import matplotlib.pyplot as plt

# 画散点图
plt.scatter(test_target_tensor.numpy(), final_output.numpy())

# 设置图表标题和轴标签
plt.title('目标值 vs 预测值')
plt.xlabel('目标值')
plt.ylabel('预测值')

# 显示图表
plt.show()

在这个例子中，我们首先使用scatter函数画出散点图，然后设置了图表标题和轴标签，并用show函数显示图表。

相关问题

import torch import matplotlib.pyplot as plt def features(x): x = x.unsqueeze(1) return torch.cat([x ** i for i in range(1, 5)], 1) x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1) b = torch.Tensor([0.512]) def target(x): return x.mm(x_weight) + b.item() def get_batch_data(batch_size): batch_x = torch.rand(batch_size) features_x = features(batch_x) target_y = target(features_x) return features_x, target_y class PR(torch.nn.Module): def __init__(self): super(PR, self).__init__() self.poly = torch.nn.Linear(4, 1) def forward(self, x): return self.poly(x) epochs = 10000 batch_size = 32 model = PR() criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), 0.01) for i in range(epochs): batch_x, batch_y = get_batch_data(batch_size) out = model(batch_x) loss = criterion(out, batch_y) optimizer.zero_grad() loss.backward() optimizer.step() if(i % 100 == 0): print(f"Epoch:{i+1}/{epochs}, loss:{loss.item}") if(i%1000 == 0): predict = model(features(batch_x)) plt.plot(batch_x.data.numpy(), predict.squeeze().data.numpy(), "r") loss = criterion(predict, batch_y) plt.title(f"Loss:{loss.item()}") plt.xlabel("X") plt.ylabel("Y") plt.scatter(batch_x, batch_y) plt.show()哪里出错了

代码有一个小错误，第26行应该是`print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}")`，需要加上括号。另外，在第34行，应该是`loss = criterion(out, batch_y)`，而不是`loss = criterion(predict, batch_y)`。正确的代码如下所示：

import torch 
import matplotlib.pyplot as plt 

def features(x): 
    x = x.unsqueeze(1) 
    return torch.cat([x ** i for i in range(1, 5)], 1) 

x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1) 
b = torch.Tensor([0.512]) 

def target(x): 
    return x.mm(x_weight) + b.item() 

def get_batch_data(batch_size): 
    batch_x = torch.rand(batch_size) 
    features_x = features(batch_x) 
    target_y = target(features_x) 
    return features_x, target_y 

class PR(torch.nn.Module): 
    def __init__(self): 
        super(PR, self).__init__() 
        self.poly = torch.nn.Linear(4, 1) 

    def forward(self, x): 
        return self.poly(x) 

epochs = 10000 
batch_size = 32 
model = PR() 
criterion = torch.nn.MSELoss() 
optimizer = torch.optim.SGD(model.parameters(), 0.01) 

for i in range(epochs): 
    batch_x, batch_y = get_batch_data(batch_size) 
    out = model(batch_x) 
    loss = criterion(out, batch_y) 
    optimizer.zero_grad() 
    loss.backward() 
    optimizer.step() 

    if(i % 100 == 0): 
        print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}") 

    if(i%1000 == 0): 
        predict = model(features(batch_x)) 
        plt.plot(batch_x.data.numpy(), predict.squeeze().data.numpy(), "r") 
        loss = criterion(out, batch_y) 
        plt.title(f"Loss:{loss.item()}") 
        plt.xlabel("X") 
        plt.ylabel("Y") 
        plt.scatter(batch_x, batch_y) 
        plt.show()

import torch import matplotlib.pyplot as plt def features(x): x = x.unsqueeze(1) return torch.cat([x ** i for i in range(1, 5)], 1) x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1) b = torch.Tensor([0.512]) def target(x): return x.mm(x_weight) + b.item() def get_batch_data(batch_size): batch_x = torch.randon(batch_size) features_x = features(batch_x) target_y = target(features_x) return features_x, target_y class PR(torch.nn.Module): def __init__(self): super(PR, self).__init__() self.poly = torch.nn.Linear(4, 1) def forward(self, x): return self.poly(x) epochs = 10000 batch_size = 32 model = PR() criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), 0.01) for i in range(epochs): batch_x, batch_y = get_batch_data(batch_size) out = model(batch_x) loss = criterion(out, batch_y) optimizer.zero_grad() loss.backward() optimizer.step() if(epochs % 100 == 0): print(f"Epoch:{i+1}/{epochs}, loss:{loss.item}") if(i%1000 == 0): predict = model(features(batch_x)) plt.plot(batch_x.data.numpy(), predict.squeeze(1).data.numpy(), c="r") loss = criterion(predict, batch_y) plt.title(f"Loss:{loss.item}") plt.xlabel("X") plt.ylabel("Y") plt.scatter(batch_x, batch_y) plt.show()哪里要改

在代码中，有一些小错误：

1. `torch.randon`应该是`torch.rand`，生成随机数的函数名称写错了。

2. `loss.item` 应该是`loss.item()`，需要调用这个方法来得到损失值。

3. 在if语句中，应该是`i % 100 == 0`而不是`epochs % 100 == 0`，因为epochs是循环次数，不会改变，而i是当前循环的次数。

修改后的代码如下：

import torch 
import matplotlib.pyplot as plt 

def features(x):
    x = x.unsqueeze(1)
    return torch.cat([x ** i for i in range(1, 5)], 1)

x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1)
b = torch.Tensor([0.512])

def target(x):
    return x.mm(x_weight) + b.item()

def get_batch_data(batch_size):
    batch_x = torch.rand(batch_size) # 将 torch.randon 改为 torch.rand
    features_x = features(batch_x)
    target_y = target(features_x)
    return features_x, target_y

class PR(torch.nn.Module):
    def __init__(self):
        super(PR, self).__init__()
        self.poly = torch.nn.Linear(4, 1)
      
    def forward(self, x):
        return self.poly(x)

epochs = 10000
batch_size = 32
model = PR()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), 0.01)

for i in range(epochs):
    batch_x, batch_y = get_batch_data(batch_size)
    out = model(batch_x)
    loss = criterion(out, batch_y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if(i % 100 == 0): # 将 epochs % 100 == 0 改为 i % 100 == 0
        print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}") 
        
    if(i % 1000 == 0):
        predict = model(features(batch_x))
        plt.plot(batch_x.data.numpy(), predict.squeeze(1).data.numpy(), c="r")
        loss = criterion(predict, batch_y)
        plt.title(f"Loss:{loss.item()}")
        plt.xlabel("X")
        plt.ylabel("Y")
        plt.scatter(batch_x, batch_y)
        plt.show()