def train_model(data_dir,
weights_file="weights.hdf5",
epochs=50,
batch_size=20):
#create data generator
train_data_path = os.path.join(data_dir, "train/samples")
train_label_path = os.path.join(data_dir, "train/labels")
val_data_path = os.path.join(data_dir, "val/samples")
val_label_path = os.path.join(data_dir, "val/labels")
num_train = utils.get_num_samples(train_data_path)
num_validate = utils.get_num_samples(val_data_path)
print(num_train)
train_gen = create_samples.generate_samples(train_data_path,
train_label_path, batch_size)
val_gen = create_samples.generate_samples(val_data_path, val_label_path,
batch_size)
first_batch = next(train_gen)
param_names = ["osc_wave", "master_val", "slave_val", "xfade_val"]
# Instantiate the model
model, serial_model = models.setup_model(first_batch[0],
param_names,
weights_file=weights_file)
save_best_only = True
checkpointer = multi_gpu.MultiGPUModelCheckpoint(
filepath=weights_file,
verbose=1,
save_best_only=save_best_only,
serial_model=serial_model,
period=1,
class_names=param_names)
#train the model
model.fit_generator(generator=train_gen,
validation_data=val_gen,
steps_per_epoch=num_train / batch_size,
validation_steps=num_validate / batch_size)
def __init__(self, path):
self.path = path
self.num_samples = utils.get_num_samples(BIN, path)
self.num_sectors = int(self.num_samples / 588)
if self.num_samples % 588 != 0:
msg = "%s not from CD (%i samples)\n" % (path, self.num_samples)
raise NotFromCDError(msg)
self.ar_entries = []
# key is offset, value is list of confidence levels
self.exact_matches = {}
self.possible_matches = {}
def __init__(self, path):
self.path = path
self.num_samples = utils.get_num_samples(BIN, path)
self.num_sectors = int(self.num_samples / 588)
if self.num_samples % 588 != 0:
msg = "%s not from CD (%i samples)\n" % \
(path, self.num_samples)
raise NotFromCDError(msg)
self.ar_entries = []
# key is offset, value is list of confidence levels
self.exact_matches = {}
self.possible_matches = {}
def main(options):
utils.check_dependencies(BIN, REQUIRED)
sources = [dict(path=p) for p in options.paths]
for s in sources:
s['num_samples'] = utils.get_num_samples(BIN, s['path'])
if s['num_samples'] % 588 != 0:
msg = "%s not from CD (%i samples)\n" % (s['path'],
s['num_samples'])
raise utils.NotFromCDError(msg)
output_dir = fix_offset(sources, options.offset, options.format,
options.verbose)
print_summary(sources, output_dir)
return 0
def main(options):
utils.check_dependencies(BIN, REQUIRED)
sources = [dict(path=p) for p in options.paths]
for s in sources:
s['num_samples'] = utils.get_num_samples(BIN, s['path'])
if s['num_samples'] % 588 != 0:
msg = "%s not from CD (%i samples)\n" % (s['path'],
s['num_samples'])
raise utils.NotFromCDError(msg)
output_dir = fix_offset(sources, options.offset, options.format,
options.verbose)
print_summary(sources, output_dir)
return 0
