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(): # 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))