Python实战小项目之Mnist手写数字识别(训练数据)

程序流程分析图：

传播过程：

代码展示：

创建环境

使用<pip install+包名>来下载torch，torchvision包

准备数据集

设置一次训练所选取的样本数Batch_Sized的值为512，训练此时Epochs的值为8

BATCH_SIZE = 512
EPOCHS = 8
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

下载数据集

Normalize()数字归一化，转换使用的值0.1307和0.3081是MNIST数据集的全局平均值和标准偏差，这里我们将它们作为给定值。model

train_loader = torch.utils.data.DataLoader(
 datasets.MNIST('data', train=True, download=True,
 transform=transforms.Compose([.
  transforms.ToTensor(),
  transforms.Normalize((0.1307,), (0.3081,))
 ])),
 batch_size=BATCH_SIZE, shuffle=True)

下载测试集

test_loader = torch.utils.data.DataLoader(
 datasets.MNIST('data', train=False,
 transform=transforms.Compose([
  transforms.ToTensor(),
  transforms.Normalize((0.1307,), (0.3081,))
 ])),
 batch_size=BATCH_SIZE, shuffle=True)

绘制图像

我们可以使用matplotlib来绘制其中的一些图像

examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
print(example_targets)
print(example_data.shape)
print(example_data)
 
import matplotlib.pyplot as plt
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()

搭建神经网络

这里我们构建全连接神经网络，我们使用三个全连接(或线性)层进行前向传播。

class linearNet(nn.Module):
 def __init__(self):
  super().__init__()
  self.fc1 = nn.Linear(784, 128)
  self.fc2 = nn.Linear(128, 64)
  self.fc3 = nn.Linear(64, 10)
 def forward(self, x):
  x = x.view(-1, 784)
  x = self.fc1(x)
  x = F.relu(x)
  x = self.fc2(x)
  x = F.relu(x)
  x = self.fc3(x)
  x = F.log_softmax(x, dim=1)
  return x

训练模型

首先，我们需要使用optimizer.zero_grad()手动将梯度设置为零，因为PyTorch在默认情况下会累积梯度。然后，我们生成网络的输出(前向传递)，并计算输出与真值标签之间的负对数概率损失。现在，我们收集一组新的梯度，并使用optimizer.step()将其传播回每个网络参数。

def train(model, device, train_loader, optimizer, epoch):
 model.train()
 for batch_idx, (data, target) in enumerate(train_loader):
 
  data, target = data.to(device), target.to(device)
  optimizer.zero_grad()
  output = model(data)
  loss = F.nll_loss(output, target)
  loss.backward()
  optimizer.step()
  if (batch_idx) % 30 == 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()))

测试模型

def test(model, device, test_loader):
 model.eval()
 test_loss = 0
 correct = 0
 with torch.no_grad():
  for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # 将一批的损失相加
pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标
correct += pred.eq(target.view_as(pred)).sum().item()
 
 test_loss /= len(test_loader.dataset)
 print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
  test_loss, correct, len(test_loader.dataset),
  100. * correct / len(test_loader.dataset)))

将训练次数进行循环

if __name__ == '__main__':
 model = linearNet()
 optimizer = optim.Adam(model.parameters())
 
 for epoch in range(1, EPOCHS + 1):
  train(model, device, train_loader, optimizer, epoch)
  test(model, device, test_loader)