2019-08-29pytorch学习（二）网络

小白的自我救赎

Neural networks can be constructed using the torch.nn package
An nn.Module contains layers, and a method forward(input)that returns the output即两部分组成：构建网络层，已经输入接口forward函数，用于前向传播
Define a network：

import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):#pythorch固有形式
      def __init__(self):#__init__方法的第一个参数永远是self，表示创建的实例本身。因此，在__init__方法内部，就可以把各种属性绑定到self，因为self就指向创建的实例本身。
      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)#以上代码即为构建网络层部分
#**注意：参数只涉及到网络中卷积核数目、大小等参数，不涉及输入图像大小
#以下代码为forward函数、前向传播部分
      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))#拉成向量
      x = F.relu(self.fc1(x))
      x = F.relu(self.fc2(x))
      x = self.fc3(x)
      return x
  def num_flat_features(self, x):#定义子函数计算batch维度之外的向量参数个数
        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)

Out:
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))
print(params[0].size())  # conv1's .weight

Out:
10
torch.Size([6, 1, 3, 3])
下一步为了防止每次反向传播，梯度累加，需要梯度初始化为0

net.zero_grad()#所有参数梯度清零
out.backward(torch.randn(1, 10))#反向传播的过程只需要调用loss.backgrad()函数即可

注意：
torch.nn only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample.即不支持单张图像输入训练
例如,nn.Conv2d只接受4维的张量:
[nSamples ,nChannels, Height ,Width]
如果只有单个样本,那么使用input.unsqueeze(0)来增加假的batch维度.