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@@ -232,7 +232,7 @@ local function get_oracle_data(x, y, instance_loss, k, samples)
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local index = torch.LongTensor(instance_loss:size(1))
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local dummy = torch.Tensor(instance_loss:size(1))
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torch.topk(dummy, index, instance_loss, k, 1, true)
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- print("average loss: " ..instance_loss:mean() .. ", average oracle loss: " .. dummy:mean())
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+ print("MSE of all data: " ..instance_loss:mean() .. ", MSE of oracle data: " .. dummy:mean())
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local shuffle = torch.randperm(k)
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local x_s = x:size()
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local y_s = y:size()
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@@ -266,7 +266,7 @@ local function remove_small_image(x)
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collectgarbage()
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end
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end
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- print(string.format("removed %d small images", #x - #new_x))
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+ print(string.format("%d small images are removed", #x - #new_x))
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return new_x
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end
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@@ -374,7 +374,7 @@ local function train()
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if score.MSE < best_score then
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local test_image = image_loader.load_float(settings.test) -- reload
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best_score = score.MSE
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- print("* update best model")
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+ print("* Best model is updated")
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if settings.save_history then
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torch.save(settings.model_file_best, model:clearState(), "ascii")
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torch.save(string.format(settings.model_file, epoch, i), model:clearState(), "ascii")
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@@ -413,7 +413,7 @@ local function train()
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end
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end
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end
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- print("PSNR: " .. score.PSNR .. ", loss: " .. score.loss .. ", MSE: " .. score.MSE .. ", Minimum MSE: " .. best_score)
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+ print("Batch-wise PSNR: " .. score.PSNR .. ", loss: " .. score.loss .. ", MSE: " .. score.MSE .. ", Minimum MSE: " .. best_score)
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collectgarbage()
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end
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end
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nagadomi