#%%
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
#%%
# 下载训练集
train_dataset = datasets.MNIST(root='./',
train=True,
transform=transforms.ToTensor(),
download=True)
# 下载测试集
test_dataset = datasets.MNIST(root='./',
train=False,
transform=transforms.ToTensor(),
download=True)
#%%
# 批次大小
batch_size = 64
# 装载训练集
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
# 装载测试集
test_loader = DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=True)
#%%
for i, data in enumerate(train_loader):
# 获得数据和对应的标签
inputs, labels = data
print(inputs.shape)
print(labels.shape)
break
#%%
# 定义网络结构
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(1, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2, 2))
self.conv2 = nn.Sequential(nn.Conv2d(32, 64, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2, 2))
self.fc1 = nn.Sequential(nn.Linear(64 * 7 * 7, 1000), nn.Dropout(p=0.4), nn.ReLU())
self.fc2 = nn.Sequential(nn.Linear(1000, 10), nn.Softmax(dim=1))
def forward(self, x):
# ([64, 1, 28, 28])
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
return x
#%%
LR = 0.0003
# 定义模型
model = Net()
# 定义代价函数
entropy_loss = nn.CrossEntropyLoss()
# 定义优化器
optimizer = optim.Adam(model.parameters(), LR)
#%%
def train():
model.train()
for i, data in enumerate(train_loader):
# 获得数据和对应的标签
inputs, labels = data
# 获得模型预测结果,(64,10)
out = model(inputs)
# 交叉熵代价函数out(batch,C),labels(batch)
loss = entropy_loss(out, labels)
# 梯度清0
optimizer.zero_grad()
# 计算梯度
loss.backward()
# 修改权值
optimizer.step()
def test():
model.eval()
correct = 0
for i, data in enumerate(test_loader):
# 获得数据和对应的标签
inputs, labels = data
# 获得模型预测结果
out = model(inputs)
# 获得最大值,以及最大值所在的位置
_, predicted = torch.max(out, 1)
# 预测正确的数量
correct += (predicted == labels).sum()
print("Test acc: {0}".format(correct.item() / len(test_dataset)))
correct = 0
for i, data in enumerate(train_loader):
# 获得数据和对应的标签
inputs, labels = data
# 获得模型预测结果
out = model(inputs)
# 获得最大值,以及最大值所在的位置
_, predicted = torch.max(out, 1)
# 预测正确的数量
correct += (predicted == labels).sum()
print("Train acc: {0}".format(correct.item() / len(train_dataset)))
#%%
for epoch in range(0, 20):
print('epoch:',epoch)
train()
test()
#%%