valid_itr.reset()
prev_h1, prev_h2, prev_h3 = [np_zeros((minibatch_size, n_hid))
for i in range(3)]
prev_kappa = np_zeros((minibatch_size, att_size))
prev_w = np_zeros((minibatch_size, n_chars))
bias = args.bias
if args.sample is not None:
predict_function = checkpoint_dict["predict_function"]
attention_function = checkpoint_dict["attention_function"]
sample_function = checkpoint_dict["sample_function"]
if args.write is not None:
sample_string = args.write
print("Sampling using sample string %s" % sample_string)
oh = dense_to_one_hot(
np.array([vocabulary[c] for c in sample_string]),
vocabulary_size)
c_mb = np.zeros(
(len(oh), minibatch_size, oh.shape[-1])).astype(c_mb.dtype)
c_mb[:len(oh), :, :] = oh[:, None, :]
c_mb = c_mb[:len(oh)]
c_mb_mask = np.ones_like(c_mb[:, :, 0])
if args.sample_length is None:
raise ValueError("Broken...")
# Automatic sampling stop as described in Graves' paper
# Assume an average of 30 timesteps per char
n_steps = 30 * c_mb.shape[0]
step_inc = n_steps
max_steps = 25000
max_steps_buf = max_steps + n_steps
X_mb, X_mb_mask, c_mb, c_mb_mask = next(valid_itr)
valid_itr.reset()
prev_h1, prev_h2, prev_h3 = [
np_zeros((minibatch_size, n_hid)) for i in range(3)
]
prev_kappa = np_zeros((minibatch_size, att_size))
prev_w = np_zeros((minibatch_size, n_chars))
if args.sample is not None:
predict_function = checkpoint_dict["predict_function"]
attention_function = checkpoint_dict["attention_function"]
sample_function = checkpoint_dict["sample_function"]
if args.write is not None:
sample_string = args.write
print("Sampling using sample string %s" % sample_string)
oh = dense_to_one_hot(
np.array([vocabulary[c] for c in sample_string]),
vocabulary_size)
c_mb = np.zeros(
(len(oh), minibatch_size, oh.shape[-1])).astype(c_mb.dtype)
c_mb[:len(oh), :, :] = oh[:, None, :]
c_mb = c_mb[:len(oh)]
c_mb_mask = np.ones_like(c_mb[:, :, 0])
if args.sample_length is None:
raise ValueError("NYI - use -sl or --sample_length ")
else:
fixed_steps = args.sample_length
completed = []
init_x = np.zeros_like(X_mb[0])
for i in range(fixed_steps):
rvals = sample_function(init_x, c_mb, c_mb_mask, prev_h1,
from theano.tensor.shared_randomstreams import RandomStreams
from kdllib import gradient_clipping, make_weights, make_biases
from kdllib import dense_to_one_hot, adam
from kdllib import fetch_fruitspeech_spectrogram
from kdllib import midiwrap, fetch_nottingham
import cPickle as pickle
midiread, midiwrite = midiwrap()
#Don't use a python long as this don't work on 32 bits computers.
np.random.seed(0xbeef)
rng = RandomStreams(seed=np.random.randint(1 << 30))
theano.config.warn.subtensor_merge_bug = False
key_range, dt, dataset = fetch_nottingham()
dataset = [dense_to_one_hot(d, n_classes=2) for d in dataset]
DEBUG = True
def fast_dropout(rng, x, debug=DEBUG):
'''
Multiply activations by N(1,1)
'''
if debug:
return x
else:
mask = rng.normal(size=x.shape, avg=1., dtype=theano.config.floatX)
return x * mask