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
代码语言：javascript复制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)
# 第二层虽然纬度和第一层一样，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
代码语言：javascript复制# 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
代码语言：javascript复制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)))
