PyTorch一小时掌握之神经网络分类篇

import torchvision
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

# 设置超参数
n_epochs = 3
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 10
random_seed = 1
torch.manual_seed(random_seed)

# 数据读取
train_loader = torch.utils.data.DataLoader(
 torchvision.datasets.MNIST('./data/', train=True, download=True,
transform=torchvision.transforms.Compose([
 torchvision.transforms.ToTensor(),
 torchvision.transforms.Normalize(
  (0.1307,), (0.3081,))
])),
 batch_size=batch_size_train, shuffle=True)
 
test_loader = torch.utils.data.DataLoader(
 torchvision.datasets.MNIST('./data/', train=False, download=True,
transform=torchvision.transforms.Compose([
 torchvision.transforms.ToTensor(),
 torchvision.transforms.Normalize(
  (0.1307,), (0.3081,))
])),
 batch_size=batch_size_test, shuffle=True)
examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
# 调试输出
print(example_targets)
print(example_data.shape)

# 画图 (前6个)
fig = plt.figure()
for i in range(6):
 plt.subplot(2, 3, i + 1)
 plt.tight_layout()
 plt.imshow(example_data[i][0], cmap='gray', interpolation='none')
 plt.title("Ground Truth: {}".format(example_targets[i]))
 plt.xticks([])
 plt.yticks([])
plt.show()

# 创建model
class Net(nn.Module):
 def __init__(self):
  super(Net, self).__init__()
  self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
  self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
  self.conv2_drop = nn.Dropout2d()
  self.fc1 = nn.Linear(320, 50)
  self.fc2 = nn.Linear(50, 10)
 def forward(self, x):
  x = F.relu(F.max_pool2d(self.conv1(x), 2))
  x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
  x = x.view(-1, 320)
  x = F.relu(self.fc1(x))
  x = F.dropout(x, training=self.training)
  x = self.fc2(x)
  return F.log_softmax(x)

network = Net()
optimizer = optim.SGD(network.parameters(), lr=learning_rate,
 momentum=momentum)

# 训练
train_losses = []
train_counter = []
test_losses = []
test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)]

def train(epoch):
 network.train()
 for batch_idx, (data, target) in enumerate(train_loader):
  optimizer.zero_grad()
  output = network(data)
  loss = F.nll_loss(output, target)
  loss.backward()
  optimizer.step()
  if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
 epoch, batch_idx * len(data), len(train_loader.dataset),
  100. * batch_idx / len(train_loader), loss.item()))
train_losses.append(loss.item())
train_counter.append(
 (batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset)))
torch.save(network.state_dict(), './model.pth')
torch.save(optimizer.state_dict(), './optimizer.pth')

def test():
 network.eval()
 test_loss = 0
 correct = 0
 with torch.no_grad():
  for data, target in test_loader:
output = network(data)
test_loss += F.nll_loss(output, target, size_average=False).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
 test_loss /= len(test_loader.dataset)
 test_losses.append(test_loss)
 print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
  test_loss, correct, len(test_loader.dataset),
  100. * correct / len(test_loader.dataset)))

for epoch in range(1, n_epochs + 1):
 train(epoch)
 test()

import torchvision
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
# 设置超参数
n_epochs = 3
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 100
random_seed = 1
torch.manual_seed(random_seed)
# 数据读取
train_loader = torch.utils.data.DataLoader(
 torchvision.datasets.MNIST('./data/', train=True, download=True,
transform=torchvision.transforms.Compose([
 torchvision.transforms.ToTensor(),
 torchvision.transforms.Normalize(
  (0.1307,), (0.3081,))
])),
 batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(
 torchvision.datasets.MNIST('./data/', train=False, download=True,
transform=torchvision.transforms.Compose([
 torchvision.transforms.ToTensor(),
 torchvision.transforms.Normalize(
  (0.1307,), (0.3081,))
])),
 batch_size=batch_size_test, shuffle=True)
examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
# 调试输出
print(example_targets)
print(example_data.shape)
# 画图 (前6个)
fig = plt.figure()
for i in range(6):
 plt.subplot(2, 3, i + 1)
 plt.tight_layout()
 plt.imshow(example_data[i][0], cmap='gray', interpolation='none')
 plt.title("Ground Truth: {}".format(example_targets[i]))
 plt.xticks([])
 plt.yticks([])
plt.show()

# 创建model
class Net(nn.Module):
 def __init__(self):
  super(Net, self).__init__()
  self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
  self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
  self.conv2_drop = nn.Dropout2d()
  self.fc1 = nn.Linear(320, 50)
  self.fc2 = nn.Linear(50, 10)
 def forward(self, x):
  x = F.relu(F.max_pool2d(self.conv1(x), 2))
  x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
  x = x.view(-1, 320)
  x = F.relu(self.fc1(x))
  x = F.dropout(x, training=self.training)
  x = self.fc2(x)
  return F.log_softmax(x)

network = Net()
optimizer = optim.SGD(network.parameters(), lr=learning_rate,
 momentum=momentum)
# 训练
train_losses = []
train_counter = []
test_losses = []
test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)]

def train(epoch):
 network.train()
 for batch_idx, (data, target) in enumerate(train_loader):
  optimizer.zero_grad()
  output = network(data)
  loss = F.nll_loss(output, target)
  loss.backward()
  optimizer.step()
  if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
 epoch, batch_idx * len(data), len(train_loader.dataset),
  100. * batch_idx / len(train_loader), loss.item()))
train_losses.append(loss.item())
train_counter.append(
 (batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset)))
torch.save(network.state_dict(), './model.pth')
torch.save(optimizer.state_dict(), './optimizer.pth')

def test():
 network.eval()
 test_loss = 0
 correct = 0
 with torch.no_grad():
  for data, target in test_loader:
output = network(data)
test_loss += F.nll_loss(output, target, size_average=False).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
 test_loss /= len(test_loader.dataset)
 test_losses.append(test_loss)
 print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
  test_loss, correct, len(test_loader.dataset),
  100. * correct / len(test_loader.dataset)))

for epoch in range(1, n_epochs + 1):
 train(epoch)
 test()