用Pytorch训练CNN(数据集MNIST,使用GPU的方法)

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)

来源：https://blog.csdn.net/qq_32464407/article/details/79186526