def main():
args = TrainData.parse_args(create_parser(usage))
inject_params(args.model)
save_params(args.model)
data = TrainData.from_both(args.db_file, args.db_folder, args.data_dir)
print('Data:', data)
(inputs, outputs), test_data = data.load(True, not args.no_validation)
print('Inputs shape:', inputs.shape)
print('Outputs shape:', outputs.shape)
if test_data:
print('Test inputs shape:', test_data[0].shape)
print('Test outputs shape:', test_data[1].shape)
if 0 in inputs.shape or 0 in outputs.shape:
print('Not enough data to train')
exit(1)
model = create_model(args.model, args.no_validation, args.extra_metrics)
model.summary()
from keras.callbacks import ModelCheckpoint
checkpoint = ModelCheckpoint(args.model, monitor=args.metric_monitor,
save_best_only=args.save_best)
try:
model.fit(inputs, outputs, 5000, args.epochs, validation_data=test_data,
callbacks=[checkpoint])
except KeyboardInterrupt:
print()
finally:
model.save(args.model)
def main():
args = create_parser(usage).parse_args()
import numpy as np
model_data = {
name: Stats.from_np_dict(data)
for name, data in np.load(args.input_file)['data'].item().items()
}
model_name = args.model_key or basename(splitext(args.model)[0])
if model_name not in model_data:
print("Could not find model '{}' in saved models in stats file: {}".
format(model_name, list(model_data)))
raise SystemExit(1)
stats = model_data[model_name]
save_spots = (stats.outputs != 0) & (stats.outputs != 1)
if save_spots.sum() == 0:
print('No data (or all NaN)')
return
stats.outputs = stats.outputs[save_spots]
stats.targets = stats.targets[save_spots]
inv = -np.log(1 / stats.outputs - 1)
pos = np.extract(stats.targets > 0.5, inv)
pos_mu = pos.mean().item()
pos_std = sqrt(np.mean((pos - pos_mu)**2)) * args.smoothing
print('Peak: {:.2f} mu, {:.2f} std'.format(pos_mu, pos_std))
pr = inject_params(args.model)
pr.__dict__.update(threshold_config=((pos_mu, pos_std), ))
save_params(args.model)
print('Saved params to {}.params'.format(args.model))
def __init__(self, parser=None):
parser = parser or ArgumentParser()
add_to_parser(parser, self.usage, True)
args = TrainData.parse_args(parser)
self.args = args = self.process_args(args) or args
if args.invert_samples and not args.samples_file:
parser.error(
'You must specify --samples-file when using --invert-samples')
if args.samples_file and not isfile(args.samples_file):
parser.error('No such file: ' +
(args.invert_samples or args.samples_file))
if not 0.0 <= args.sensitivity <= 1.0:
parser.error('sensitivity must be between 0.0 and 1.0')
output_folder = os.path.join(args.folder, splitext(args.model)[0])
if not os.path.exists(output_folder):
print('Creating output folder:', output_folder)
os.makedirs(output_folder)
args.model = os.path.join(output_folder, args.model)
inject_params(args.model)
save_params(args.model)
self.train, self.test = self.load_data(self.args)
set_loss_bias(1.0 - args.sensitivity)
params = ModelParams(skip_acc=args.no_validation,
extra_metrics=args.extra_metrics)
self.model = create_model(args.model, params)
self.model.summary()
from keras.callbacks import ModelCheckpoint, TensorBoard
checkpoint = ModelCheckpoint(args.model,
monitor=args.metric_monitor,
save_best_only=args.save_best)
epoch_file = splitext(args.model)[0]
epoch_file = os.path.join(epoch_file + '.epoch')
epoch_fiti = Fitipy(epoch_file)
self.epoch = epoch_fiti.read().read(0, int)
def on_epoch_end(a, b):
self.epoch += 1
epoch_fiti.write().write(self.epoch, str)
self.model_base = splitext(self.args.model)[0]
if args.samples_file:
self.samples, self.hash_to_ind = self.load_sample_data(
args.samples_file, self.train)
else:
self.samples = set()
self.hash_to_ind = {}
self.callbacks = [
checkpoint,
TensorBoard(log_dir=self.model_base + '.logs', ),
LambdaCallback(on_epoch_end=on_epoch_end)
]
def run(self):
"""Train the model on randomly generated batches"""
_, test_data = self.data.load(train=False, test=True)
try:
self.model.fit_generator(self.samples_to_batches(
self.generate_samples(), self.args.batch_size),
steps_per_epoch=self.args.steps_per_epoch,
epochs=self.epoch + self.args.epochs,
validation_data=test_data,
callbacks=self.callbacks,
initial_epoch=self.epoch)
finally:
self.model.save(self.args.model)
save_params(self.args.model)
def __init__(self, args):
super().__init__(args)
if args.invert_samples and not args.samples_file:
raise ValueError(
'You must specify --samples-file when using --invert-samples')
if args.samples_file and not isfile(args.samples_file):
raise ValueError('No such file: ' +
(args.invert_samples or args.samples_file))
if not 0.0 <= args.sensitivity <= 1.0:
raise ValueError('sensitivity must be between 0.0 and 1.0')
inject_params(args.model)
save_params(args.model)
params = ModelParams(skip_acc=args.no_validation,
extra_metrics=args.extra_metrics,
loss_bias=1.0 - args.sensitivity,
freeze_till=args.freeze_till)
self.model = create_model(args.model, params)
self.train, self.test = self.load_data(self.args)
from keras.callbacks import ModelCheckpoint, TensorBoard
checkpoint = ModelCheckpoint(args.model,
monitor=args.metric_monitor,
save_best_only=args.save_best)
epoch_fiti = Fitipy(splitext(args.model)[0] + '.epoch')
self.epoch = epoch_fiti.read().read(0, int)
def on_epoch_end(_a, _b):
self.epoch += 1
epoch_fiti.write().write(self.epoch, str)
self.model_base = splitext(self.args.model)[0]
if args.samples_file:
self.samples, self.hash_to_ind = self.load_sample_data(
args.samples_file, self.train)
else:
self.samples = set()
self.hash_to_ind = {}
self.callbacks = [
checkpoint,
TensorBoard(log_dir=self.model_base + '.logs', ),
LambdaCallback(on_epoch_end=on_epoch_end)
]