torch_example.lua

require 'torch'
require 'nn'
require 'optim'

-- Build a model as a sequence of layers, and a loss function
local model = nn.Sequential()
model:add(nn.Linear(500, 50))
model:add(nn.ReLU())
model:add(nn.Linear(50, 5))
local loss_fn = nn.CrossEntropyCriterion()

local x = torch.randn(32, 500)
local y = torch.Tensor(32):random(5)
local weights, grad_weights = model:getParameters()

-- Define callback
local function f(w)
  assert(w == weights)
  -- Forward Pass
  local stores = model:forward(x)
  local loss = loss_fn:forward(scores, y)
  
  -- Backward Pass
  grad_weights:zero()
  local grad_scores = loss_fn:backward(scores,y)
  local grad_x = model:backward(x, grad_scores)
  
  return loss, grad_weights
end

-- Pass callback to optimizer
local state = {learningRate=1e-3}
for t = 1, 25 do
  optim.adam(f, weights, state)
end