for x inrange(4): for i,data inenumerate(train_dataset): optimizer.zero_grad() (inputs,labels) = data inputs = pt.autograd.Variable(inputs).cuda() labels = pt.autograd.Variable(labels).cuda() outputs = model(inputs) loss = lossfunc(outputs,labels) loss.backward() optimizer.step() if i % 100 == 0: print(i,":",AccuarcyCompute(outputs,labels))
MLP (
(fc1): Linear (784 -> 512)
(norm1): BatchNorm1d(512, eps=1e-05, momentum=0.5, affine=True)
(fc2): Linear (512 -> 128)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.5, affine=True)
(fc3): Linear (128 -> 10)
)
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for x inrange(6): for i,data inenumerate(train_dataset): optimizer.zero_grad() (inputs,labels) = data inputs = pt.autograd.Variable(inputs).cuda() labels = pt.autograd.Variable(labels).cuda() outputs = model_norm(inputs) loss = lossfunc(outputs,labels) loss.backward() optimizer.step() if i % 200 == 0: print(i,":",AccuarcyCompute(outputs,labels))
MLP (
(fc1): Linear (784 -> 512)
(drop1): Dropout (p = 0.6)
(fc2): Linear (512 -> 128)
(drop2): Dropout (p = 0.6)
(fc3): Linear (128 -> 10)
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
for x inrange(6): for i,data inenumerate(train_dataset): optimizer.zero_grad() (inputs,labels) = data inputs = pt.autograd.Variable(inputs).cuda() labels = pt.autograd.Variable(labels).cuda() outputs = model_drop(inputs) loss = lossfunc(outputs,labels) loss.backward() optimizer.step() if i % 200 == 0: print(i,":",AccuarcyCompute(outputs,labels))