神经网络 (NEURAL NETWORK)
神经网络可以通过 torch.nn
包来构建
上节课已经学习了 autograd
,nn
是在 autograd
的基础上定义和区分模型。一个 nn.Module
包含了层,和一个 forward(input)
来返回 output
。
以典型 LetNet-5 网络举例:
这是一个简单的前馈(feed-forward)网络。具有输入,将输入馈送到一层接一层,最后输出。
结构详解参考:Fly~~~
一个典型的神经网络训练过程包含以下几个方面:
- 定义神经网络的学习参数
- 迭代输入数据
- 通过网络处理输入数据
- 计算损失函数,也就是输出距离整理的距离
- 传递梯度反馈到网络的参数
- 更新网络的参数,典型更新规则是
weight = weight - learning_rate * gradient
定义网络 (Define the network)
让我们定义这个网络
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 3x3 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 6 * 6, 120) # 6*6 from image dimension
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
# If the size is a square you can only specify a single number
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
net = Net()
print(net)
输出:
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)
)