自定义的多层感知机模型对fashion_mnist数据进行分类

Question

将模型的输出信息：loss，train accuracy，test accuracy等，用matplotlib绘制成图像两幅图像

Answer 1

示例代码

import torch
from torch import nn
from torchvision import datasets, transforms
import matplotlib.pyplot as plt

# Define a MLP
class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.layers = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28 * 28, 512),
            nn.ReLU(),
            nn.Linear(512, 10)
        )

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

# Load data
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.5,), (0.5,))])

trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)

model = MLP()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Training
epochs = 10
train_losses, test_losses = [], []
for e in range(epochs):
    running_loss = 0
    for images, labels in trainloader:
        optimizer.zero_grad()
        
        output = model(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    else:
        test_loss = 0
        accuracy = 0
        
        # Turn off gradients for validation
        with torch.no_grad():
            for images, labels in testloader:
                output = model(images)
                test_loss += criterion(output, labels)
                
                ps = torch.exp(output)
                top_p, top_class = ps.topk(1, dim=1)
                equals = top_class == labels.view(*top_class.shape)
                accuracy += torch.mean(equals.type(torch.FloatTensor))
                
        train_losses.append(running_loss/len(trainloader))
        test_losses.append(test_loss/len(testloader))

        print("Epoch: {}/{}.. ".format(e+1, epochs),
              "Training Loss: {:.3f}.. ".format(train_losses[-1]),
              "Test Loss: {:.3f}.. ".format(test_losses[-1]),
              "Test Accuracy: {:.3f}".format(accuracy/len(testloader)))

# Plotting the losses
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(train_losses, label='Training loss')
plt.plot(test_losses, label='Validation loss')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(train_losses, label='Training loss')
plt.legend()

plt.show()

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