Pytroch training example

pytorch_weight_update.py

import torch.optim as optim
import torch.nn as nn
# instantiate your network that should be defined by you
net = Net()
# create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)
# define your criterion for optimization
criterion = nn.MSELoss()
# dat_set comes from somewhere
for data in data_set:
  # zero the gradient buffers
  optimizer.zero_grad()  
  # Passes the data through your network
  output = net.forward(data)
  # calculates the loss
  loss = criterion(output, target)
  # Propagates the loss back
  loss.backward()
  # Updates all the weights of the network
  optimizer.step()