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目录程序流程分析图:传播过程:代码展示:创建环境准备数据集下载数据集下载测试集绘制图像搭建神经网络训练模型测试模型保存训练模型运行结果展示:程序流程分析图: 传播过程: 代码展
使用<pip install+包名>来下载torch,torchvision包
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")
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)
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)
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()
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
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()))
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)
torch.save(model, 'MNIST.pth')
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