net:evaluate()

local batch = batch or 64

local Nv = Xv:size(1)

local lloss = 0

for i =1,Nv,batch do

local j = math.min(i+batch-1,Nv)

local Xb = Xv[{{i,j}}]:cuda()

local Yb = Yv[{{i,j}}]:cuda()

local out = net:forward(Xb) -- N*k*C

local tmp,YYb = out:max(2)

lloss = lloss + YYb:eq(Yb):sum()

end

return (100*lloss/(Nv))

local x,dx = net:getParameters()

require 'optim'

local batch = batch or 64

local Nt = Xt:size(1)

print('parameters size ..')

print(#x)

for k=1,K do

local lloss = 0

net:training()

for i =1,Nt,batch do

dx:zero()

local j = math.min(i+batch-1,Nt)

local Xb = Xt[{{i,j}}]:cuda()

local Yb = Yt[{{i,j}}]:cuda()

local out = net:forward(Xb)

local loss = ct:forward(out,Yb)

local dout = ct:backward(out,Yb)

net:backward(Xb,dout)

dx:div(j-i+1)

function feval()

return loss,dx

end

local ltmp,tmp = optim.sgd(feval,x,sgd_config)

--print(loss)

lloss = lloss + loss

end

print('loss..'..lloss)

print('valid .. '.. train.accuracy(Xv,Yv,net,batch))

print('train .. '.. train.accuracy(Xt,Yt,net,batch))

end