if __name__ == '__main__':
args = parse_args()
print(vars(args))
lr = args.lr
data = Data(args.standardize, args.output, args.maxNorm)
dirpath = os.path.join(args.directory, args.input, args.output)
if not os.path.exists(dirpath):
os.makedirs(dirpath)
with open(os.path.join(dirpath, "args.json"), "w") as outfile:
json.dump(vars(args), outfile)
for f in range(5):
print("Training fold " + str(f + 1))
if args.input != "all":
trnX, trnY, valX, valY = data.get_trn_val(f + 1, key=args.input)
else:
trnX, trnY, valX, valY = data.get_m_trn_val(f + 1)
testingModel = get_model(
args.filters, args.kernelSize, args.regularizationSize, args.fully,
args.activation, args.input, args.output)
if f == 0:
testingModel.summary()
early_stop = EarlyStopping(
monitor='val_loss', patience=80, mode='min', restore_best_weights=True)
lrate = LearningRateScheduler(step_decay, verbose=0)
callbacks_list = [early_stop, lrate]
history = testingModel.fit(
trnX, trnY, epochs=2000, batch_size=args.batchSize,
