其他
PyTorch 教程-图像识别神经网络的验证
在训练部分，我们使用MNIST数据集（无穷数据集）对模型进行了训练，并且似乎达到了合理的损失和准确性。如果模型能够将其学到的知识应用于新数据并进行泛化，那么这将是其性能的真正体现。以下是验证模型的步骤：
步骤1：
validation_dataset=datasets.MNIST(root='./data',train=False,download=True,transform=transform1) 步骤2：
validation_loader=torch.utils.data.DataLoader(dataset=validation_dataset,batch_size=100,shuffle=False) 步骤3：
val_loss_history=[] val_correct_history=[]步骤4：
在接下来的步骤中，我们将验证模型。模型将在同一epoch中验证。在完成迭代整个训练集以训练数据之后，我们现在将在整个验证集上进行迭代，以测试我们的数据。
val_loss=0.0 val_correct=0.0 步骤5：
for val_input,val_labels in validation_loader:步骤6：
当我们迭代图像的批次时，我们必须将它们展平，并使用view方法进行形状变换。
注意：每个图像张量的形状是（1，28，28），这意味着总共有784个像素。
val_inputs=val_input.view(val_input.shape[0],-1)val_outputs=model(val_inputs)步骤7：
val_loss1=criteron(val_outputs,val_labels)with torch.no_grad():这将临时将所有requires_grad标志设置为False。
步骤8：
_,val_preds=torch.max(val_outputs,1) val_loss+=val_loss1.item() val_correct+=torch.sum(val_preds==val_labels.data)步骤9：
val_epoch_loss=val_loss/len(validation_loader) val_epoch_acc=correct.float()/len(validation_loader) val_loss_history.append(val_epoch_loss) val_correct_history.append(epoch_acc)步骤10：
print('validation_loss:{:.4f},{:.4f}'.format(val_epoch_loss,val_epoch_acc.item()))这将产生预期的结果，如下所示：
步骤11：
plt.plot(loss_history,label='Training Loss') plt.plot(val_loss_history,label='Validation Loss') plt.legend() plt.show()plt.plot(correct_history,label='Training accuracy') plt.plot(val_correct_history,label='Validation accuracy') plt.legend() plt.show()完整代码
import torch import matplotlib.pyplot as plt import numpy as np import torch.nn.functional as func from torch import nn from torchvision import datasets,transforms transform1=transforms.Compose([transforms.Resize((28,28)),transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,))]) training_dataset=datasets.MNIST(root='./data',train=True,download=True,transform=transform1) validation_dataset=datasets.MNIST(root='./data',train=False,download=True,transform=transform1) training_loader=torch.utils.data.DataLoader(dataset=training_dataset,batch_size=100,shuffle=True) validation_loader=torch.utils.data.DataLoader(dataset=validation_dataset,batch_size=100,shuffle=False) def im_convert(tensor): image=tensor.clone().detach().numpy() image=image.transpose(1,2,0) print(image.shape) image=image*(np.array((0.5,0.5,0.5))+np.array((0.5,0.5,0.5))) image=image.clip(0,1) return image class classification1(nn.Module): def __init__(self,input_layer,hidden_layer1,hidden_layer2,output_layer): super().__init__() self.linear1=nn.Linear(input_layer,hidden_layer1) self.linear2=nn.Linear(hidden_layer1,hidden_layer2) self.linear3=nn.Linear(hidden_layer2,output_layer) def forward(self,x): x=func.relu(self.linear1(x)) x=func.relu(self.linear2(x)) x=self.linear3(x) return x model=classification1(784,125,65,10) criteron=nn.CrossEntropyLoss() optimizer=torch.optim.Adam(model.parameters(),lr=0.0001) epochs=12 loss_history=[] correct_history=[] val_loss_history=[] val_correct_history=[] for e in range(epochs): loss=0.0 correct=0.0 val_loss=0.0 val_correct=0.0 for input,labels in training_loader: inputs=input.view(input.shape[0],-1) outputs=model(inputs) loss1=criteron(outputs,labels) optimizer.zero_grad() loss1.backward() optimizer.step() _,preds=torch.max(outputs,1) loss+=loss1.item() correct+=torch.sum(preds==labels.data) else: with torch.no_grad(): for val_input,val_labels in validation_loader: val_inputs=val_input.view(val_input.shape[0],-1) val_outputs=model(val_inputs) val_loss1=criteron(val_outputs,val_labels) _,val_preds=torch.max(val_outputs,1) val_loss+=val_loss1.item() val_correct+=torch.sum(val_preds==val_labels.data) epoch_loss=loss/len(training_loader) epoch_acc=correct.float()/len(training_loader) loss_history.append(epoch_loss) correct_history.append(epoch_acc) val_epoch_loss=val_loss/len(validation_loader) val_epoch_acc=correct.float()/len(validation_loader) val_loss_history.append(val_epoch_loss) val_correct_history.append(epoch_acc) print('training_loss:{:.4f},{:.4f}'.format(epoch_loss,epoch_acc.item())) print('validation_loss:{:.4f},{:.4f}'.format(val_epoch_loss,val_epoch_acc.item())) plt.plot(correct_history,label='Correct history ') plt.plot(val_correct_history,label='Validation correct history') plt.legend() plt.show() plt.plot(correct_history,label='Training accuracy') plt.plot(val_correct_history,label='Validation accuracy') plt.legend() plt.show()