# -*- coding: utf-8 -*-
"""
Created on Sun Oct 24 15:56:23 2021
@author: Lenovo
"""
# 神經(jīng)網(wǎng)絡
# 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個輸入，6個輸出，5*5的卷積
        # 內(nèi)核
        self.conv1=nn.Conv2d(1,6,5)
        self.conv2=nn.Conv2d(6,16,5)
        # 映射函數(shù)：線性——y=Wx+b
        self.fc1=nn.Linear(16*5*5,120)#輸入特征值：16*5*5，輸出特征值：120
        self.fc2=nn.Linear(120,84)
        self.fc3=nn.Linear(84,10)
    def forward(self,x):
        x=F.max_pool2d(F.relu(self.conv1(x)),(2,2))
        # 如果其尺寸是一個square只能指定一個數(shù)字
        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:]
        num_features=1
        for s in size:
            num_features *= s
        return num_features               
net=Net()
print(net)

# 查看模型可訓練的參數(shù)
params=list(net.parameters())
print(len(params))
print(params[0].size())# conv1 的權重weight

input=torch.randn(1, 1, 32,32)
print(input)
out=net(input)
print(out)

# 調(diào)用反向傳播
net.zero_grad()
out.backward(torch.randn(1, 10))

如果跟隨損失到反向傳播路徑，可以使用他的.grad_fn屬性，將會看到一個計算圖

# 在調(diào)用loss.backward()時候，整個圖都會微分，而且所有的圖中的requires_grad=True的張量將會讓他們的grad張量累計梯度
#跟隨以下步驟反向傳播
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

# 調(diào)用loss.backward()然后看一下con1的偏置項在反向傳播之前和之后的變化
net.zero_grad()
print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)
loss.backward()#反向傳播
print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)

# =============================================================================
# # 最簡單的更新規(guī)則就是隨機梯度下降:weight=weight-learning_rate*gradient
# learning_rate=0.01
# for f in net.parameters():
#     f.data.sub_(f.grad.data*learning_rate)#f.data=f.data-learning_rate*gradient
#  =============================================================================

# =============================================================================
# import torch.optim as optim
# optimizer=optim.SGD(net.parameters(), lr=0.01)
# # 在迭代訓練過程中
# optimizer.zero_grad()#將現(xiàn)存梯度置零
# output=net(input)
# loss=criterion(output,target)
# loss.backward()#反向傳遞
# optimizer.step()#更新網(wǎng)絡參數(shù)
# =============================================================================