Pytorch学习系列(4):前馈神经网络(Feedforward 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)
自定义前馈神经网络
# 定义:有一个隐藏层的全连接的神经网络
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
# 加载(实例化)一个网络模型
# to(device)可以用来将模型放在GPU上训练
model = NeuralNet(input_size, hidden_size, num_classes).to(device)
# 定义损失函数和优化器
# 再次,损失函数CrossEntropyLoss适合用于分类问题,因为它自带SoftMax功能
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):
# 将tensor移动到配置好的设备上(GPU)
images = images.reshape(-1, 28*28).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() # 还是要注意此处,每次迭代训练都需要清空梯度缓存
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: 0.0211
Epoch [1/5], Step [200/600], Loss: 0.0045
Epoch [1/5], Step [300/600], Loss: 0.0107
Epoch [1/5], Step [400/600], Loss: 0.0058
Epoch [1/5], Step [500/600], Loss: 0.0077
Epoch [1/5], Step [600/600], Loss: 0.0347
Epoch [2/5], Step [100/600], Loss: 0.0031
Epoch [2/5], Step [200/600], Loss: 0.0098
Epoch [2/5], Step [300/600], Loss: 0.0231
Epoch [2/5], Step [400/600], Loss: 0.0157
Epoch [2/5], Step [500/600], Loss: 0.0047
Epoch [2/5], Step [600/600], Loss: 0.0514
Epoch [3/5], Step [100/600], Loss: 0.0275
Epoch [3/5], Step [200/600], Loss: 0.0041
Epoch [3/5], Step [300/600], Loss: 0.0041
Epoch [3/5], Step [400/600], Loss: 0.0067
Epoch [3/5], Step [500/600], Loss: 0.0167
Epoch [3/5], Step [600/600], Loss: 0.0028
Epoch [4/5], Step [100/600], Loss: 0.0009
Epoch [4/5], Step [200/600], Loss: 0.0037
Epoch [4/5], Step [300/600], Loss: 0.0051
Epoch [4/5], Step [400/600], Loss: 0.0177
Epoch [4/5], Step [500/600], Loss: 0.0104
Epoch [4/5], Step [600/600], Loss: 0.0074
Epoch [5/5], Step [100/600], Loss: 0.0181
Epoch [5/5], Step [200/600], Loss: 0.0031
Epoch [5/5], Step [300/600], Loss: 0.0028
Epoch [5/5], Step [400/600], Loss: 0.0029
Epoch [5/5], Step [500/600], Loss: 0.0166
Epoch [5/5], Step [600/600], Loss: 0.0012
测试模型
# 测试阶段为提高效率,可以不计算梯度
# 使用with torch.no_grad()函数
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
outputs = model(images)
# 统计预测概率最大的下标
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
Accuracy of the network on the 10000 test images: 98.22 %
保存模型
# 保存模型
torch.save(model.state_dict(), 'model.ckpt')