3_Neural_networks.py

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

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # 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, *input):
        # Max pooling over a (2, 2) window
        print(input)
        print(len(input))
        x = F.max_pool2d(F.relu(self.conv1(input[0])), (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 weights

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) # A dummy target, for example
target = target.view(1, -1)
criterion = nn.MSELoss()

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

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)

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

# in your training loop:
optimizer.zero_grad()
output = net(input)
loss = criterion(output, target)
loss.backward()
optimizer.step() # Does the update