前言最近在学习过程中需要用到pytorch框架，简单学习了一下，写了一个简单的案例，记录一下pytorch中搭建一个识别网络基础的东西。对应一位博主写的tensorflow的识别mnist数据集，将其改为pytorch框架，也可以详细看到两个框架大体的区别。
Tensorflow版本转载来源（CSDN博主「兔八哥1024」）：https://www.zalou.cn/article/191157.htm
Pytorch实战mnist手写数字识别
代码语言：javascript复制#需要导入的包
import torch
import torch.nn as nn#用于构建网络层
import torch.optim as optim#导入优化器
from torch.utils.data import DataLoader#加载数据集的迭代器
from torchvision import datasets, transforms#用于加载mnsit数据集
#下载数据集
train_set = datasets.MNIST('./data', train=True, download=True,transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1037,), (0.3081,))
]))
test_set = datasets.MNIST('./data', train=False, download=True,transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1037,), (0.3081,))
]))
#构建网络（网络结构对应tensorflow的那一篇文章）
class Net(nn.Module):
def __init__(self, num_classes=10):
super(Net, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(kernel_size=2,stride=2),
nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(kernel_size=2,stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(3136, 7*7*64),
nn.Linear(3136, num_classes),
)
def forward(self,x):
x = self.features(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
net=Net()
net.cuda()#用GPU运行
#计算误差，使用adam优化器优化误差
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), 1e-2)
train_data = DataLoader(train_set, batch_size=128, shuffle=True)
test_data = DataLoader(test_set, batch_size=128, shuffle=False)
#训练过程
for epoch in range(1):
net.train() ##在进行训练时加上train()，测试时加上eval()
batch = 0
for batch_images, batch_labels in train_data:
average_loss = 0
train_acc = 0
##在pytorch0.4之后将Variable 与tensor进行合并，所以这里不需要进行Variable封装
if torch.cuda.is_available():
batch_images, batch_labels = batch_images.cuda(),batch_labels.cuda()
#前向传播
out = net(batch_images)
loss = criterion(out,batch_labels)
average_loss = loss
prediction = torch.max(out,1)[1]
# print(prediction)
train_correct = (prediction == batch_labels).sum()
##这里得到的train_correct是一个longtensor型，需要转换为float
train_acc = (train_correct.float()) / 128
optimizer.zero_grad() #清空梯度信息，否则在每次进行反向传播时都会累加
loss.backward() #loss反向传播
optimizer.step() ##梯度更新
batch =1
print("Epoch: %d/%d || batch:%d/%d average_loss: %.3f || train_acc: %.2f"
%(epoch, 20, batch, float(int(50000/128)), average_loss, train_acc))
# 在测试集上检验效果
net.eval() # 将模型改为预测模式
for idx,(im1, label1) in enumerate(test_data):
if torch.cuda.is_available():
im, label = im1.cuda(),label1.cuda()
out = net(im)
loss = criterion(out, label)
eval_loss = loss
pred = torch.max(out,1)[1]
num_correct = (pred == label).sum()
acc = (num_correct.float())/ 128
eval_acc = acc
print('EVA_Batch:{}, Eval Loss: {:.6f}, Eval Acc: {:.6f}'
.format(idx,eval_loss , eval_acc))运行结果：
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