train.py

'''=================================================
@IDE    ：Pycharm
@Author ：Qingyong Li
@Date   ：2019/11/22

=================================================='''
import torch

from torch import nn, optim

from torch.autograd import Variable

from torch.utils.data import DataLoader

from torchvision import datasets, transforms

import cnn


# 定义一些超参数

batch_size = 64

learning_rate = 0.02

num_epoches = 20



# 数据预处理。transforms.ToTensor()将图片转换成PyTorch中处理的对象Tensor,并且进行标准化（数据在0~1之间）

# transforms.Normalize()做归一化。它进行了减均值，再除以标准差。两个参数分别是均值和标准差

# transforms.Compose()函数则是将各种预处理的操作组合到了一起

data_tf = transforms.Compose(

    [transforms.ToTensor(),

     transforms.Normalize([0.5], [0.5])
     ])



# 数据集的下载器

train_dataset = datasets.MNIST(

    root='./data', train=True, transform=data_tf, download=True)

test_dataset = datasets.MNIST(root='./data', train=False, transform=data_tf)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)



# 选择模型

model = cnn.CNN()

# model = net.Activation_Net(28 * 28, 300, 100, 10)

# model = net.Batch_Net(28 * 28, 300, 100, 10)

if torch.cuda.is_available():

    model = model.cuda()



# 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()

optimizer = optim.SGD(model.parameters(), lr=learning_rate)



# 训练模型

epoch = 0

for data in train_loader:

    img, label = data

    if torch.cuda.is_available():

        img = img.cuda()

        label = label.cuda()

    else:

        img = Variable(img)

        label = Variable(label)

    out = model(img)

    loss = criterion(out, label)

    print_loss = loss.data.item()

    optimizer.zero_grad()

    loss.backward()

    optimizer.step()

    epoch+=1

    if epoch%50 == 0:

        print('epoch: {}, loss: {:.4}'.format(epoch, loss.data.item()))


# 保存和加载整个模型

torch.save(model, 'CNN_for_MNIST.pth')




# 模型评估

model.eval()

eval_loss = 0

eval_acc = 0

for data in test_loader:

    img, label = data
    img = Variable(img)

    if torch.cuda.is_available():

        img = img.cuda()
        label = label.cuda()

    out = model(img)
    loss = criterion(out, label)
    eval_loss += loss.data.item()*label.size(0)
    _, pred = torch.max(out, 1)
    num_correct = (pred == label).sum()
    eval_acc += num_correct.item()

print('Test Loss: {:.6f}, Acc: {:.6f}'.format(

    eval_loss / (len(test_dataset)),

    eval_acc / (len(test_dataset))

))