Pytorch train without optim

train_without_optim

import torch
import torch.nn as nn
from torch.autograd import Variable as Var

# define the data
dt = torch.Tensor([[1,0],[0,1], [1,1], [0,0]])
labels = torch.LongTensor([1, 0, 1, 0])
learning_rate = 0.01
model = nn.Linear(2,2)
criterion = nn.CrossEntropyLoss()
dt = Var(dt)
labels = Var(labels)

for e in range(0, 1000):
    model.zero_grad()
    output = model(dt)
    loss = criterion(output, labels)
    loss.backward()
    for f in model.parameters():
        f.data.sub_(f.grad.data * learning_rate)
    print (loss)

# test

output = model(dt)
val, pred = torch.max(output, 1)
print (pred)