batch_size = 10
nlabel = 105
debug = 1
model = Model()
train_data = Music(name='train', path=data_path, nlabel=nlabel)
valid_data = Music(name='valid', path=data_path, nlabel=nlabel)
# Choose the random initialization method
init_W = InitCell('randn')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
x, y, mask = train_data.theano_vars()
# You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb
if debug:
x.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
y.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
mask.tag.test_value = np.ones((10, batch_size), dtype=np.float32)
h1 = LSTM(name='h1',
parent=['x'],
parent_dim=[105],
nout=50,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
model = Model()
train_data = Music(name='train',
path=data_path,
nlabel=nlabel)
valid_data = Music(name='valid',
path=data_path,
nlabel=nlabel)
# Choose the random initialization method
init_W = InitCell('randn')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
x, y, mask = train_data.theano_vars()
# You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb
if debug:
x.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
y.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
mask.tag.test_value = np.ones((10, batch_size), dtype=np.float32)
h1 = LSTM(name='h1',
parent=['x'],
parent_dim=[105],
nout=50,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
debug = 0
model = Model()
trdata = Music(name='train',
path=data_path,
nlabel=nlabel)
valdata = Music(name='valid',
path=data_path,
nlabel=nlabel)
# Choose the random initialization method
init_W = InitCell('randn')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
model.inputs = trdata.theano_vars()
x, y, mask = model.inputs
# You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb
if debug:
x.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
y.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
mask.tag.test_value = np.ones((10, batch_size), dtype=np.float32)
inputs = [x, y, mask]
inputs_dim = {'x':nlabel}
h1 = LSTM(name='h1',
parent=['x'],
batch_size=batch_size,
nout=50,
unit='tanh',
