Pytorch学习系列(5):卷积神经网络(Convolutional Neural Network)
导包
# 包
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
超参数设置
# 设备配置
# 有cuda就用cuda
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 超参数设置
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001
MINIST 数据集加载
train_dataset = torchvision.datasets.MNIST(root='./data/minist',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='./data/minist',
train=False,
transform=transforms.ToTensor())
# 数据加载器(data loader)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
自定义 卷积神经网络
# 搭建卷积神经网络模型
# 两个卷积层
class ConvNet(nn.Module):
def __init__(self, num_classes=10):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
# 卷积层计算
nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
# 批归一化
nn.BatchNorm2d(16),
#ReLU激活函数
nn.ReLU(),
# 池化层:最大池化
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(7*7*32, num_classes)
# 定义前向传播顺序
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
# 实例化一个模型,并迁移至gpu
model = ConvNet(num_classes).to(device)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# 注意模型在GPU中,数据也要搬到GPU中
images = images.to(device)
labels = labels.to(device)
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
Epoch [1/5], Step [100/600], Loss: 0.0962
Epoch [1/5], Step [200/600], Loss: 0.0651
Epoch [1/5], Step [300/600], Loss: 0.0299
Epoch [1/5], Step [400/600], Loss: 0.1865
Epoch [1/5], Step [500/600], Loss: 0.0479
Epoch [1/5], Step [600/600], Loss: 0.1068
Epoch [2/5], Step [100/600], Loss: 0.0208
Epoch [2/5], Step [200/600], Loss: 0.0552
Epoch [2/5], Step [300/600], Loss: 0.0692
Epoch [2/5], Step [400/600], Loss: 0.0148
Epoch [2/5], Step [500/600], Loss: 0.0457
Epoch [2/5], Step [600/600], Loss: 0.1268
Epoch [3/5], Step [100/600], Loss: 0.0358
Epoch [3/5], Step [200/600], Loss: 0.0237
Epoch [3/5], Step [300/600], Loss: 0.0158
Epoch [3/5], Step [400/600], Loss: 0.0140
Epoch [3/5], Step [500/600], Loss: 0.0057
Epoch [3/5], Step [600/600], Loss: 0.0032
Epoch [4/5], Step [100/600], Loss: 0.0061
Epoch [4/5], Step [200/600], Loss: 0.0359
Epoch [4/5], Step [300/600], Loss: 0.1519
Epoch [4/5], Step [400/600], Loss: 0.0326
Epoch [4/5], Step [500/600], Loss: 0.0390
Epoch [4/5], Step [600/600], Loss: 0.0707
Epoch [5/5], Step [100/600], Loss: 0.0118
Epoch [5/5], Step [200/600], Loss: 0.0223
Epoch [5/5], Step [300/600], Loss: 0.0230
Epoch [5/5], Step [400/600], Loss: 0.0024
Epoch [5/5], Step [500/600], Loss: 0.0026
Epoch [5/5], Step [600/600], Loss: 0.0097
测试模型
# 切换成评估测试模式
# 这是因为在测试时,与训练时的dropout和batch normalization的操作是不同的
model.eval()
# 节省计算资源,不去计算梯度
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
Test Accuracy of the model on the 10000 test images: 98.88 %
保存模型
# 保存模型
torch.save(model.state_dict(), 'model.ckpt')