import torch
import torch.nn as nn
import torch.nn.functional as F
"""
Neural networks can be constructed using the torch.nn package.
nn depends on autograd to define models and differentiate them.
An nn.Module contains layers, and a method forward(input)that returns the output.
"""


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)
        print(self.conv1.weight.data)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        print(self.conv2.weight.data)
        # an affine仿射 operation: y = Wx + b ;
        # class torch.nn.Linear(in_features, out_features, bias=True)
        # in_features - 每个输入样本的大小 out_features - 每个输出样本的大小
        self.fc1 = nn.Linear(16*5*5, 120)                          # 为啥是16*5*5
        print(self.fc1.weight.data, self.fc1.weight.size())
        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, 2))
        print(x)
        x = x.view(-1, self.num_flat_features(x))  # 代表what
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):           # 特征展开
        print(x.size(), "************")
        size = x.size()[1:]
        print("--------%s", size)
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net)

self.conv1.weight.size()

self.conv1.weight.data

self.conv1.bias.size()

self.conv2.weight.size()

self.conv2.bias.size()