1.3神经网络

import torch
# 神经网络的包,仅支持小批量样本的训练,不支持单个样本(可以input.unsqueeze(0)来模拟批量[1])
import torch.nn as nn 
import torch.nn.functional as F # 一些常用的函数


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 3) # 卷积核
        self.conv2 = nn.Conv2d(6, 16, 3) # 卷积核
        # Linear是简单的映射函数,第一个参数w的维度,第二个参数b的维度
        self.fc1 = nn.Linear(16 * 6 * 6, 120) # 6*6是图片的长宽
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10) # 最后输出10个分类

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # 最大二维池化层:2*2格子内取最大
        x = F.max_pool2d(F.relu(self.conv2(x)), 2) # 第一个参数的结果是笔直的向量
        x = x.view(-1, self.num_flat_features(x)) # -1表示自动计算,view是改变形状
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


    def num_flat_features(self, x): # 每个输入变平后的数量大小
        size = x.size()[1:] # 获得除了批量大小的所有维度
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net) # 获取当前模型的数据

Net(
  (conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1))
  (conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1))
  (fc1): Linear(in_features=576, out_features=120, bias=True)
  (fc2): Linear(in_features=120, out_features=84, bias=True)
  (fc3): Linear(in_features=84, out_features=10, bias=True)
)

params = list(net.parameters())
print(len(params)) # 自动学习参数的数量
print(params[0].size()) # 6*1*3*3 ,conv1的权重

10
torch.Size([6, 1, 3, 3])

print(params[1]) # conv1的偏移

Parameter containing:
tensor([ 0.0176,  0.2514,  0.1497,  0.1647, -0.2829,  0.0409],
       requires_grad=True)

input = torch.randn(1,1,32,32)
out = net(input)
print(out) # 输出,数值最大的是正确分类,但一开始没有训练是随机的

tensor([[ 6.9398e-03, -6.6113e-05, -6.9214e-02, -7.6395e-02,  3.9166e-02,
         -7.0978e-02,  1.2993e-02, -1.7690e-02, -1.4292e-02, -4.7204e-02]],
       grad_fn=<AddmmBackward>)

net.zero_grad() # 设置所有梯度为0
out.backward(torch.randn(1,10)) # 设置随机数为梯度

output = net(input)
target = torch.randn(10) # 假设一个假的结果
target = target.view(1, -1) # 变为[1,10]形状
criterion = nn.MSELoss() # 均方误差

loss = criterion(output, target)
print(loss)

tensor(0.7828, grad_fn=<MseLossBackward>)

"""
input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d
      -> view -> linear -> relu -> linear -> relu(3) -> linear(2)
      -> MSELoss(1)
      -> loss
"""
print(loss.grad_fn)  # MSELoss
print(loss.grad_fn.next_functions[0][0])  # Linear
print(loss.grad_fn.next_functions[0][0].next_functions[0][0])  # ReLU

<MseLossBackward object at 0x7fefaac1c8d0>
<AddmmBackward object at 0x7fefaac1ca90>
<AccumulateGrad object at 0x7fefaac1c8d0>

net.zero_grad()

print('conv1.bias.grad before backward') # 反向传播之前
print(net.conv1.bias.grad)

loss.backward()

print('conv1.bias.grad after backward') # 反向传播后
print(net.conv1.bias.grad)

conv1.bias.grad before backward
tensor([0., 0., 0., 0., 0., 0.])
conv1.bias.grad after backward
tensor([-0.0158, -0.0015, -0.0017,  0.0073,  0.0074, -0.0123])

# 更新权重
learning_rate = 0.01
for f in net.parameters():
    f.data.sub_(f.grad.data * learning_rate) # sub_是减等于

import torch.optim as optim # 优化算法包

optimizer = optim.SGD(net.parameters(), lr=0.01) # 创建优化算法器

optimizer.zero_grad() # 初始化梯度为0
output = net(input) # 前向传播
loss = criterion(output, target) # 计算损失
loss.backward() # 反向传播
optimizer.step() # 更新权重