CodeMirror/waifu2x
1
0
Fork 0
mirror of synced 2024-05-18 11:52:17 +12:00
Code Issues Releases Wiki Activity

Minimize the weighted huber loss instead of the weighted mean square error

Browse source 
Huber loss is less sensitive to outliers(i.e. noise) in data than the squared error loss.
This commit is contained in:
nagadomi 2015-10-31 21:56:20 +09:00
parent 243d8821be
commit 490eb33a6b
2 changed files with 19 additions and 17 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
lib/minibatch_adam.lua
View file

@ -21,7 +21,6 @@ local function minibatch_adam(model, criterion,
input_size[1], input_size[2], input_size[3])
local targets_tmp = torch.Tensor(batch_size,
target_size[1] * target_size[2] * target_size[3])
for t = 1, #train_x do
xlua.progress(t, #train_x)
local xy = transformer(train_x[shuffle[t]], false, batch_size)
@ -31,7 +30,6 @@ local function minibatch_adam(model, criterion,
end
inputs:copy(inputs_tmp)
targets:copy(targets_tmp)
local feval = function(x)
if x ~= parameters then
parameters:copy(x)
@ -53,7 +51,7 @@ local function minibatch_adam(model, criterion,
end
xlua.progress(#train_x, #train_x)
return { mse = sum_loss / count_loss}
return { loss = sum_loss / count_loss}
end
return minibatch_adam

32
train.lua
View file

@ -35,18 +35,19 @@ local function split_data(x, test_size)
end
return train_x, valid_x
end
local function make_validation_set(x, transformer, n)
local function make_validation_set(x, transformer, n, batch_size)
n = n or 4
local data = {}
for i = 1, #x do
for k = 1, math.max(n / 8, 1) do
local xy = transformer(x[i], true, 8)
for k = 1, math.max(n / batch_size, 1) do
local xy = transformer(x[i], true, batch_size)
local tx = torch.Tensor(batch_size, xy[1][1]:size(1), xy[1][1]:size(2), xy[1][1]:size(3))
local ty = torch.Tensor(batch_size, xy[1][2]:size(1), xy[1][2]:size(2), xy[1][2]:size(3))
for j = 1, #xy do
local x = xy[j][1]
local y = xy[j][2]
table.insert(data, {x = x:reshape(1, x:size(1), x:size(2), x:size(3)),
y = y:reshape(1, y:size(1), y:size(2), y:size(3))})
tx[j]:copy(xy[j][1])
ty[j]:copy(xy[j][2])
end
table.insert(data, {x = tx, y = ty})
end
xlua.progress(i, #x)
collectgarbage()
@ -58,11 +59,12 @@ local function validate(model, criterion, data)
for i = 1, #data do
local z = model:forward(data[i].x:cuda())
loss = loss + criterion:forward(z, data[i].y:cuda())
xlua.progress(i, #data)
if i % 10 == 0 then
if i % 100 == 0 then
xlua.progress(i, #data)
collectgarbage()
end
end
xlua.progress(#data, #data)
return loss / #data
end
@ -71,10 +73,10 @@ local function create_criterion(model)
local offset = reconstruct.offset_size(model)
local output_w = settings.crop_size - offset * 2
local weight = torch.Tensor(3, output_w * output_w)
weight[1]:fill(0.299 * 3) -- R
weight[2]:fill(0.587 * 3) -- G
weight[3]:fill(0.114 * 3) -- B
return w2nn.WeightedMSECriterion(weight):cuda()
weight[1]:fill(0.29891 * 3) -- R
weight[2]:fill(0.58661 * 3) -- G
weight[3]:fill(0.11448 * 3) -- B
return w2nn.WeightedHuberCriterion(weight, 0.1):cuda()
else
return nn.MSECriterion():cuda()
end
@ -151,7 +153,9 @@ local function train()
end
local best_score = 100000.0
print("# make validation-set")
local valid_xy = make_validation_set(valid_x, pairwise_func, settings.validation_crops)
local valid_xy = make_validation_set(valid_x, pairwise_func,
settings.validation_crops,
settings.batch_size)
valid_x = nil
collectgarbage()