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losses.txt
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losses.txt
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Epoch = 2 train loss = 0.821888185599271
Total time predicting in infodump = 0.00022268295288085938
Total time predicting in training = 0.23949456214904785
Total time predicting in loss = 3.8736283779144287
Total time predicting in metrics = 3.109222888946533
Total time predicting in total_time_spent_on_backward = 3.3597347736358643
Total time predicting in total_time_spent_on_backward = 9.225403785705566
Training finished in 21.680946350097656 seconds
batch size = 8
Epoch = 0 train loss = 0.7698096201491
Total time predicting in infodump = 0.0002243518829345703
Total time predicting in training = 1.9864366054534912
Total time predicting in loss = 2.308164596557617
Total time predicting in metrics = 4.019528388977051
Total time predicting in backward = 2.2023801803588867
Total time predicting in step = 12.238869667053223
Training finished in 29.74395251274109 seconds
params = {'input_space': 'RGB', 'input_range': [0, 1],
'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}
preprocess input = mean = [0.485, 0.456, 0.406],
std = [0.229, 0.224, 0.225], input_space = RGB, input_range = [0, 1],
Epoch = 0 valid loss = 0.9585258811712265
Epoch = 0 train loss = 0.9234215424341314
Training finished in 22.421659231185913 seconds
Epoch = 1 valid loss = 0.8815984427928925
Epoch = 1 train loss = 0.8420484048478744
Training finished in 21.718680143356323 seconds
Epoch = 2 valid loss = 0.8239574730396271
Epoch = 2 train loss = 0.8006125320406521
Training finished in 21.63101315498352 seconds
Epoch = 0 valid loss = 0.9585258811712265
Epoch = 0 train loss = 0.9234761069802677
Training finished in 18.43318772315979 seconds
Epoch = 1 valid loss = 0.8800710886716843
Epoch = 1 train loss = 0.8408477394019856
Training finished in 18.412322282791138 seconds
Epoch = 2 valid loss = 0.8303760588169098
Epoch = 2 train loss = 0.800149901824839
Training finished in 18.041751384735107 seconds
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