pytorch-多分类实战之手写数字识别

1. 网络设计

输入是手写数字图片28x28，输出是10个分类0~9，有两个隐藏层，如下图所示：

2. 代码实现

2.1 网络代码

第一层将784降维到200，第二次使用200不降维，输出层200降维到10，每一层之后加一个激活函数relu，每一层都需要梯度信息所以requires_grad=True；

forward函数最后不要加softmax，因为后面CrossEntropyLoss中包含了softmax操作。

2.2 train

优化目标是w1、b1、w2、b2、w3、b3，使用SGD优化器，使用CrossEntropyLoss计算loss

3. 完整代码

python 复制代码

import  torch
import  torch.nn as nn
import  torch.nn.functional as F
import  torch.optim as optim
from    torchvision import datasets, transforms


batch_size=200
learning_rate=0.01
epochs=10

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)



w1, b1 = torch.randn(200, 784, requires_grad=True),\
         torch.zeros(200, requires_grad=True)
w2, b2 = torch.randn(200, 200, requires_grad=True),\
         torch.zeros(200, requires_grad=True)
w3, b3 = torch.randn(10, 200, requires_grad=True),\
         torch.zeros(10, requires_grad=True)

# torch.nn.init.kaiming_normal_(w1)
# torch.nn.init.kaiming_normal_(w2)
# torch.nn.init.kaiming_normal_(w3)


def forward(x):
    x = [email protected]() + b1
    x = F.relu(x)
    x = [email protected]() + b2
    x = F.relu(x)
    x = [email protected]() + b3
    x = F.relu(x)
    return x



optimizer = optim.SGD([w1, b1, w2, b2, w3, b3], lr=learning_rate)
criteon = nn.CrossEntropyLoss()

for epoch in range(epochs):

    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.view(-1, 28*28)

        logits = forward(data)
        loss = criteon(logits, target)

        optimizer.zero_grad()
        loss.backward()
        # print(w1.grad.norm(), w2.grad.norm())
        optimizer.step()

        if batch_idx % 100 == 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()))


    test_loss = 0
    correct = 0
    for data, target in test_loader:
        data = data.view(-1, 28 * 28)
        logits = forward(data)
        test_loss += criteon(logits, target).item()

        pred = logits.data.max(1)[1]
        correct += pred.eq(target.data).sum()

    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)))

如下图：

未使用torch.nn.init.kaiming_normal_(w1)初始化参数的情况，可以看出Loss在2.302585后就不下降了。

如下图：使用了torch.nn.init.kaiming_normal_(w1)初始化参数的情况下，Loss下降还是比较快的。

因此使用好的初始化参数对网络的训练起到至关重要的作用