<reponame>sidgairo18/Pytorch-Tensorflow-Practice
# Part of Pytorch Tutorial 60 Minute blitz
# Link: https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

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 affine operation: 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):
        #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))
        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
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


if __name__ == "__main__":

    net = Net()
    print(net)
    params = list(net.parameters())
    print(len(params))
    print(params[0].size()) #conv1's .weight

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

    net.zero_grad()
    out.backward(torch.randn(1,10))


    output = net(input)
    target = torch.randn(10)
    target = target.view(1,-1)
    criterion = nn.MSELoss()

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

    print (loss.grad_fn)
    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()

    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)

    optimizer = optim.SGD(net.parameters(), lr =0.01)

    optimizer.zero_grad()
    output = net(input)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

        
