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请你帮我写一个pytorch框架下的图像分类模型的训练代码,使用pytorch中的resnet50预训练模型作为模型主体,使用pytorch中的mnist数据集作为训练数据,使用crossentropy作为loss函数,并且用acc作为评价指标。请将batchsize设置为32,epoch数设置为10,使用adam优化器进行优化。最后请画出训练过程的acc曲线以及loss曲线。用tqdm显示每个epoch中的iterations进度,并且在gpu上进行训练。并且在transformer中请注意对图像特征图深度的适配。
支持21、22年,期间发行的pytorch版本。但是准确率只能到0.8多。
matplotlib如果报错可以百度解决,最近可能会有更新。
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from tqdm import tqdmdevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# Define transformations for the dataset
transform = transforms.Compose([transforms.ToTensor(),transforms.Lambda(lambda x: torch.cat([x, x, x], 0)), # 将灰度图像转换为三通道图像transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])# Load the MNIST dataset
trainset = torchvision.datasets.MNIST(root='./data', train=True,download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,shuffle=True, num_workers=0)# Load the pre-trained ResNet50 model
model = torchvision.models.resnet50(pretrained=True)# Freeze all the layers except the final fully-connected layer
for param in model.parameters():param.requires_grad = False
model.fc.requires_grad = True# Replace the final fully-connected layer with a new one that outputs 10 classes
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 10)model = model.to(device)
# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.01)# Train the model
num_epochs = 10
train_loss = []
train_acc = []
for epoch in range(num_epochs):running_loss = 0.0running_corrects = 0with tqdm(total=len(trainloader), desc=f'Epoch {epoch + 1}/{num_epochs}', unit='batch', dynamic_ncols=True) as t:for i, data in enumerate(trainloader, 0):# Get the inputsinputs, labels = data[0].to(device), data[1].to(device)# Zero the parameter gradientsoptimizer.zero_grad()# Forward passoutputs = model(inputs)loss = criterion(outputs, labels)# Backward pass and optimizeloss.backward()optimizer.step()# Update statistics_, preds = torch.max(outputs, 1)running_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)# Update tqdmt.update()# Calculate statistics for the epochepoch_loss = running_loss / len(trainset)epoch_acc = (running_corrects.double() / len(trainset)).cpu()# Print statistics for the epochprint('Epoch [{}/{}], Loss: {:.4f}, Acc: {:.4f}'.format(epoch+1, num_epochs, epoch_loss, epoch_acc))# Save statistics for the epochtrain_loss.append(epoch_loss)train_acc.append(epoch_acc)# Plot the training loss and accuracy curves
plt.figure()
plt.plot(train_loss)
plt.title('Training Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig('train_loss.png')plt.figure()
plt.plot(train_acc)
plt.title('Training Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.savefig('train_acc.png')