import torch
import torch.nn as nn
import torch.nn.functional as F
nn.Linear(16 * 5 * 5, 120)
nn.Conv2d(1,6,5)
class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1,6,5)
        self.conv2 = nn.Conv2d(6,16,5)
        #an affline y=wx+b
        self.fc1 = nn.Linear(16*5*5,120)
        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))
        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)
params = list(net.parameters())
print(len(params))
print(params[0].size())  # conv1's .weight
print(params[1].size())  # conv1's .weight
print(params[2].size())  # conv1's .weight
print(params[3].size())  # conv1's .weight
print(params[4].size())  # conv1's .weight
print(params[5].size())  # conv1's .weight
print(params[6].size())  # conv1's .weight
print(params[7].size())  # conv1's .weight
print(params[8].size())  # conv1's .weight

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

net.zero_grad()
out.backward(torch.randn(1,10))
output = net(input)
target = torch.randn(10)  # a dummy target, for example
target = target.view(1, -1)  # make it the same shape as output
criterion = nn.MSELoss()

loss = criterion(output, target)
print(loss)
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

net.zero_grad()     # zeroes the gradient buffers of all parameters

print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)

loss.backward()

print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)

learning_rate = 0.01
for f in net.parameters():
    f.data.sub_(f.grad.data * learning_rate)

import torch.optim as optim

optimizer = optim.SGD(net.parameters(),lr=0.01)
optimizer.zero_grad()
output = net(input)
loss = criterion(output,target)
loss.backward()
optimizer.step()
print(net.conv1.bias.grad)
