def init_model(save_path, args):
# Search for an existing model in the save directory
if miscfuncs.file_check('model.json', save_path) and args.load_model:
print('existing model file found, loading network')
model_data = miscfuncs.json_load('model', save_path)
# assertions to check that the model.json file is for the right neural network architecture
try:
assert model_data['model_data']['unit_type'] == args.unit_type
assert model_data['model_data']['input_size'] == args.input_size
assert model_data['model_data']['hidden_size'] == args.hidden_size
assert model_data['model_data']['output_size'] == args.output_size
except AssertionError:
print(
"model file found with network structure not matching config file structure"
)
network = networks.load_model(model_data)
# If no existing model is found, create a new one
else:
print('no saved model found, creating new network')
network = networks.SimpleRNN(input_size=args.input_size,
unit_type=args.unit_type,
hidden_size=args.hidden_size,
output_size=args.output_size,
skip=args.skip_con)
network.save_state = False
network.save_model('model', save_path)
return network
def test_forward(self):
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type="LSTM",
hidden_size=32,
skip=1,
bias_fl=True,
num_layers=1)
run_net(network)
network = networks.SimpleRNN(input_size=2,
output_size=4,
unit_type="GRU",
hidden_size=16,
skip=0,
bias_fl=False,
num_layers=1)
run_net(network)
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type="GRU",
hidden_size=16,
skip=0,
bias_fl=False,
num_layers=1)
run_net(network)
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type="GRU",
hidden_size=1,
skip=0,
bias_fl=False,
num_layers=1)
run_net(network)
network = networks.SimpleRNN(input_size=1,
output_size=2,
unit_type="RNN",
hidden_size=16,
skip=1,
bias_fl=True,
num_layers=2)
run_net(network)
def test_save_model(self):
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type='LSTM',
hidden_size=8,
skip=1)
model_save_test(network)
del network
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type='LSTM',
hidden_size=8,
skip=0)
model_save_test(network)
del network
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type='GRU',
num_layers=2,
skip=1)
model_save_test(network)
del network
network = networks.SimpleRNN(input_size=1,
output_size=1,
unit_type='GRU',
num_layers=2,
skip=1,
bias_fl=False)
model_save_test(network)
del network
network = networks.SimpleRNN(input_size=5,
output_size=2,
unit_type='GRU',
num_layers=2,
skip=2)
model_save_test(network)
del network
dataset = dataset.DataSet(data_dir=args.data_location)
dataset.create_subset('train', frame_len=args.seg_len)
dataset.create_subset('val')
dataset.create_subset('test')
#dataset = dataset.DataSet('data')
#dataset.load_file('train/BehPhaserToneoffSingles1', 'train')
#dataset.load_file('val/BehPhaserToneoffSingles1', 'val')
#dataset.load_file('test/BehPhaserToneoffSingles1', 'test')
dataset.load_file(args.file_name, ['train', 'val', 'test'],
[0.75, 0.125, 0.125])
# Create instance of Network.RNN class
network = networks.SimpleRNN(hidden_size=args.hidden_size,
num_layers=args.num_layers,
unit_type=args.unit_type,
input_size=args.in_size)
# Otherwise create directory for output
if not args.cur_epoch:
try:
os.mkdir(save_path)
except FileExistsError:
shutil.rmtree(save_path)
os.mkdir(save_path)
if cuda:
network = network.cuda()
optimiser = torch.optim.Adam(network.parameters(), lr=args.learn_rate)
loss_functions = training.LossWrapper(args.loss_fcns, args.pre_filt)