PyTorch 教程-图像识别神经网络的验证
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点击关注 👉约 6 分钟在训练部分，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=0.0val_correct=0.0
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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()))
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 torchimport matplotlib.pyplot as pltimport numpy as npimport torch.nn.functional as funcfrom torch import nnfrom torchvision import datasets,transformstransform1=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 imageclass 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 xmodel=classification1(784,125,65,10)criteron=nn.CrossEntropyLoss()optimizer=torch.optim.Adam(model.parameters(),lr=0.0001)epochs=12loss_history=[]correct_history=[]val_loss_history=[]val_correct_history=[]for e in range(epochs):loss=0.0correct=0.0val_loss=0.0val_correct=0.0for 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()
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