Use correct criterion
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parent
99e6dd1a57
commit
b96bc5d453
1 changed files with 11 additions and 9 deletions
20
train.lua
20
train.lua
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@ -58,8 +58,9 @@ local function make_validation_set(x, transformer, n, patches)
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data = new_data
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return data
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end
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local function validate(model, criterion, data, batch_size)
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local function validate(model, criterion, eval_metric, data, batch_size)
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local loss = 0
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local mse = 0
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local loss_count = 0
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local inputs_tmp = torch.Tensor(batch_size,
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data[1].x:size(1),
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@ -83,6 +84,7 @@ local function validate(model, criterion, data, batch_size)
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targets:copy(targets_tmp)
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local z = model:forward(inputs)
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loss = loss + criterion:forward(z, targets)
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mse = mse + eval_metric:forward(z, targets)
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loss_count = loss_count + 1
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if loss_count % 10 == 0 then
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xlua.progress(t, #data)
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@ -90,7 +92,7 @@ local function validate(model, criterion, data, batch_size)
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end
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end
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xlua.progress(#data, #data)
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return loss / loss_count
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return {loss = loss / loss_count, MSE = mse / loss_count, PSNR = 10 * math.log10(1 / (mse / loss_count))}
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end
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local function create_criterion(model)
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@ -247,7 +249,7 @@ local function train()
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return transformer(model, x, is_validation, n, offset)
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end
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local criterion = create_criterion(model)
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local eval_metric = nn.MSECriterion():cuda()
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local eval_metric = w2nn.ClippedMSECriterion(0, 1):cuda()
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local x = remove_small_image(torch.load(settings.images))
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local train_x, valid_x = split_data(x, math.max(math.floor(settings.validation_rate * #x), 1))
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local adam_config = {
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@ -312,16 +314,16 @@ local function train()
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print(train_score)
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model:evaluate()
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print("# validation")
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local score = validate(model, eval_metric, valid_xy, adam_config.xBatchSize)
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table.insert(hist_train, train_score.MSE)
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table.insert(hist_valid, score)
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local score = validate(model, criterion, eval_metric, valid_xy, adam_config.xBatchSize)
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table.insert(hist_train, train_score.loss)
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table.insert(hist_valid, score.loss)
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if settings.plot then
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plot(hist_train, hist_valid)
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end
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if score < best_score then
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if score.loss < best_score then
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local test_image = image_loader.load_float(settings.test) -- reload
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lrd_count = 0
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best_score = score
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best_score = score.loss
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print("* update best model")
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if settings.save_history then
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torch.save(string.format(settings.model_file, epoch, i), model:clearState(), "ascii")
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@ -356,7 +358,7 @@ local function train()
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lrd_count = 0
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end
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end
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print("PSNR: " .. 10 * math.log10(1 / score) .. ", MSE: " .. score .. ", Best MSE: " .. best_score)
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print("PSNR: " .. score.PSNR .. ", loss: " .. score.loss .. ", Minimum loss: " .. best_score)
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collectgarbage()
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end
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end
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