Deep-Learning-with-PyTorch

What does an ideal dataset look like?
341
Listing 12.8
dsets.py:280, LunaDataset.__len__
def __len__(self):
if self.ratio_int:
return 200000
else:
return len(self.candidateInfo_list)
We’re no longer tied to a specific number of samples, and presenting “a full epoch”
doesn’t really make sense when we would have to repeat positive samples many, many
times to present a balanced training set. By picking 200,000 samples, we reduce the
time between starting a training run and seeing results (faster feedback is always
nice!), and we give ourselves a nice, clean number of samples per epoch. Feel free to
adjust the length of an epoch to meet your needs.
For completeness, we also add a command-line parameter.
Listing 12.9
training.py:31, class LunaTrainingApp
class LunaTrainingApp:
def __init__(self, sys_argv=None):
# ... line 52
parser.add_argument('--balanced',
help="Balance the training data to half positive, half negative.",
action='store_true',
default=False,
)
Then we pass that parameter into the LunaDataset constructor.
Listing 12.10
training.py:137, LunaTrainingApp.initTrainDl
def initTrainDl(self):
train_ds = LunaDataset(
val_stride=10,
isValSet_bool=False,
ratio_int=int(self.cli_args.balanced),
)
Here we rely on python’s True
being convertible to a 1.
We’re all set. Let’s run it!
12.4.2 Contrasting training with a balanced LunaDataset to previous
runs
As a reminder, our unbalanced training run had results like these:
$ python -m p2ch12.training
...
E1 LunaTrainingApp
E1 trn 0.0185 loss, 99.7% correct, 0.0000 precision, 0.0000 recall,
➥ nan f1 score