听说pytorch使用比TensorFlow简单，加之pytorch现已支持windows，所以今天装了pytorch玩玩，第一件事还是写了个简单的CNN在MNIST上实验，初步体验的确比TensorFlow方便。

参考代码（在莫烦python的教程代码基础上修改）如下：

import torch import torch.nn as nn from torch.autograd import Variable import torch.utils.data as Data import torchvision import time#import matplotlib.pyplot as plt torch.manual_seed(1) EPOCH = 1 BATCH_SIZE = 50 LR = 0.001 DOWNLOAD_MNIST = False if_use_gpu = 1 # 获取训练集dataset training_data = torchvision.datasets.MNIST( root='./mnist/', # dataset存储路径 train=True, # True表示是train训练集，False表示test测试集 transform=torchvision.transforms.ToTensor(), # 将原数据规范化到（0,1）区间 download=DOWNLOAD_MNIST, ) # 打印MNIST数据集的训练集及测试集的尺寸 print(training_data.train_data.size()) print(training_data.train_labels.size()) # torch.Size([60000, 28, 28]) # torch.Size([60000]) #plt.imshow(training_data.train_data[0].numpy(), cmap='gray') #plt.title('%i' % training_data.train_labels[0]) #plt.show() # 通过torchvision.datasets获取的dataset格式可直接可置于DataLoader train_loader = Data.DataLoader(dataset=training_data, batch_size=BATCH_SIZE, shuffle=True) # 获取测试集dataset test_data = torchvision.datasets.MNIST( root='./mnist/', # dataset存储路径 train=False, # True表示是train训练集，False表示test测试集 transform=torchvision.transforms.ToTensor(), # 将原数据规范化到（0,1）区间 download=DOWNLOAD_MNIST, ) # 取前全部10000个测试集样本 test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1).float(), requires_grad=False)#test_x = test_x.cuda()## (~, 28, 28) to (~, 1, 28, 28), in range(0,1) test_y = test_data.test_labels#test_y = test_y.cuda() class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Sequential( # (1,28,28) nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2), # (16,28,28) # 想要con2d卷积出来的图片尺寸没有变化, padding=(kernel_size-1)/2 nn.ReLU(), nn.MaxPool2d(kernel_size=2) # (16,14,14) ) self.conv2 = nn.Sequential( # (16,14,14) nn.Conv2d(16, 32, 5, 1, 2), # (32,14,14) nn.ReLU(), nn.MaxPool2d(2) # (32,7,7) ) self.out = nn.Linear(32*7*7, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) # 将（batch，32,7,7）展平为（batch，32*7*7） output = self.out(x) return output cnn = CNN() if if_use_gpu: cnn = cnn.cuda()optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) loss_function = nn.CrossEntropyLoss() for epoch in range(EPOCH): start = time.time() for step, (x, y) in enumerate(train_loader): b_x = Variable(x, requires_grad=False) b_y = Variable(y, requires_grad=False) if if_use_gpu: b_x = b_x.cuda() b_y = b_y.cuda() output = cnn(b_x) loss = loss_function(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() if step % 100 == 0: print('Epoch:', epoch, '|Step:', step, '|train loss:%.4f'%loss.data[0]) duration = time.time() - start print('Training duation: %.4f'%duration) cnn = cnn.cpu()test_output = cnn(test_x) pred_y = torch.max(test_output, 1)[1].data.squeeze()accuracy = sum(pred_y == test_y) / test_y.size(0) print('Test Acc: %.4f'%accuracy)

以上这篇用Pytorch训练CNN(数据集MNIST,使用GPU的方法)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。