'''
定义包含一些可学习的参数(或者叫权重)神经网络模型；

在数据集上迭代；

通过神经网络处理输入；

计算损失(输出结果和正确值的差值大小)；

将梯度反向传播回网络的参数；

更新网络的参数，主要使用如下简单的更新原则：weight = weight - learning_rate * gradient
'''
#定义网络
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
CNN计算

(H - k +2 * P) / S + 1
(W - k +2 * P) / S + 1

LetNet-5 
input: 32*32*3

out_conv1 = (32-5)+1 = 28 
max_pool1 = 28 / 2 = 14
out_conv2 = (14 - 5) + 1 = 10
max_pool2 = 10 / 2 = 5

定义一个神经网络
https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py
'''
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        #  conv1层，输入的灰度图，所以 in_channels=1, out_channels=6 说明使用了6个滤波器/卷积核，
        # kernel_size=5卷积核大小5x5
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)
        # conv2层， 输入通道in_channels 要等于上一层的 out_channels
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        # an affine operarion: y = Wx + b
        # 全连接层fc1,因为32x32图像输入到fc1层时候，feature map为：5x5x16
        # 因此，全连接层的输入特征维度为16*5*5，  因为上一层conv2的out_channels=16
        # out_features=84,输出维度为84，代表该层为84个神经元
        self.fc1 = nn.Linear(16*5*5, 120)
        self.fc2 = nn.Linear(in_features=120, out_features=84)
        self.fc3 = nn.Linear(in_features=84, out_features=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)
        # 特征图转换为一个１维的向量
        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)

params = list(net.parameters())
print(params[0].size())

#在上面net模型是"Lenet"期望输入是：32*32
input = torch.randn(1,1,32,32)
out = net(input)
print(out)

#损失函数
#一个损失函数接受一对作为输入，计算一个值来评估网络的输出和目标值相差多少
output = net(input)
target = torch.rand(10)
target = target.view(1,-1)
cri = nn.MSELoss()
loss = cri(output,target)
print(loss)