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__()
		self.conv1 = nn.Conv2d(1,6,3)
		self.conv2 = nn.Conv2d(6,16,3)

		self.fc1 = nn.Linear(16*6*6, 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())

input = torch.randn(2,1,32,32)
#out = net(input)
#print(out)

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


output = net(input)
target = torch.randn(20)
target  = target.view(2,-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)
"""
optimizer = optim.SGD(net.parameters(), lr=0.01)

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