def test_03_model(self):
n = Net(Net.ALL,
training_epochs=25,
epochs_per_save=5,
epochs_per_archve=10,
save_m=True,
gen_steps=60,
kwargs={'hidden_size': 32})
# Should not load - no files
self.assertFalse(n.load())
# Should train and gen output
n.train()
y = n.gen()
filepath = os.path.join(test_output_dir, 'test1.wav')
utils.write_output(filepath, y)
# Build new model and load weights
m = Net(Net.ALL, build=False, gen_steps=60, kwargs={'hidden_size': 32})
self.assertTrue(m.load())
y = m.gen()
filepath = os.path.join(test_output_dir, 'test2.wav')
utils.write_output(filepath, y)
# Let us confirm our files were saved
files = filter(lambda e: e[0] != '.', os.listdir(test_output_dir))
self.assertEqual(len(files), 2)
def main(files=files):
"""
Generates music and saves to filenames set in command line args, or
in config.output.default_file.
Runs automatically from command line.
Arguments:
list:files -- List of files, taken from args when run on command line
"""
# Build net
print('Loading net...')
net = Net(Net.GEN)
print('Loading model...')
net.load()
# Generate
for filepath in filepaths:
y = net.gen()
print('Finished generating "{0}."'.format(filepath))
sclog('Generated "{0}."'.format(filepath))
utils.write_output(filepath, y)
if (cf.output.save_raw):
utils.save_array(os.path.splitext(filepath)[0], y)
def main():
# We set autosave to true even if it is False in config
net = Net(Net.TRAIN, autosave=True)
# Simplified parameters for setting epochs
for k in cf.flags.keys():
if utils.is_str_int(k):
net.set(training_epochs=int(k))
break
# Load weights unless new flag provided
if not cf.flags.get('new'):
net.load()
print('Completed {0} epochs. Training next {1}...'.format(
net.epochs, net.training_epochs))
net.train()
sclog('Finished training {0} epochs.'.format(net.training_epochs))