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

这段代码看起来是神经网络的预测过程。具体来说，predict方法接收一个输入x，然后调用forward方法，利用当前的权重和偏置项计算网络的输出，并将输出作为预测结果返回。预测过程与训练过程的区别在于，预测过程不需要计算误差项，也不需要更新权重和偏置项，因为预测过程只需要利用已经训练好的网络进行输入到输出的映射，不需要再进行参数的更新。

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def predict(self, x, train_flg=False): for layer in self.layers: if isinstance(layer, Dropout):#isinstance来判断是不是已知类型 x = layer.forward(x, train_flg) else: x = layer.forward(x) return x

这段代码是神经网络中的前向传播函数，用于计算输入数据在网络中的输出结果。它接受一个输入数据x和一个train_flg参数，train_flg用于指示当前是训练模式还是推理模式。在训练模式下，网络中的Dropout层会随机舍弃一部分神经元，以达到正则化的目的；在推理模式下，Dropout层会保留所有神经元。该函数通过遍历神经网络中的每一层，并调用每一层的forward方法，依次计算输出结果。最后返回输出结果x。

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()