多分类问题线性层和训练部分代码的构建
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如下图网络是一个十个输出（十分类问题）
首先建立三个线性层
import torch import torch.nn.functional as F # 先建立三个线性层 784=>200=>200=>10 w1, b1 = torch.randn(200, 784, requires_grad=True), \ torch.randn(200, requires_grad=True) # randn内的参数分别为(ch_out, ch_cn)，784=28*28，适用于常用的mnist数据集 w2, b2 = torch.randn(200, 200, requires_grad=True), \ torch.randn(200, requires_grad=True) w3, b3 = torch.randn(10, 200, requires_grad=True), \ torch.randn(10, requires_grad=True) # 第二层虽然维度和第一层一样，都是200，但是并不是没有作用，而是经历了特征变换 def forward(x): # 经过第一层 x = x@w1.t() + b1 x = F.relu(x) # 经过第二层 x = x@w2.t() + b2 x = F.relu(x) # 经过最后一层 x = x@w3.t() + b3 x = F.relu(x) return x # 这里返回的x没有经过sigmoid和softmax上面完成了tensor和forward的建立，下面介绍train的部分
# 训练过程首先要建立一个优化器，引入相关工具包 import torch.optim as optim import torch.nn as nn lr = 1e-3 # learning_rate # 优化器优化的目标是三个全连接层的变量 optimizer = optim.SGD([w1, b1, w2, b2, w3, b3], lr=lr) criteon = nn.CrossEntropyLoss() # 自带softmax,log,CrossEntropy 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_gradr() loss.backward() optimizer.step()这里先要求掌握以上代码的书写，后续需会讲解数据读取、结果验证等其他部分代码
下面给出全部代码
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 = x@w1.t() + b1 x = F.relu(x) x = x@w2.t() + b2 x = F.relu(x) x = x@w3.t() + 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)))
