def normalize(x):
return np.hstack((x[:, 0][:, None], (x[:, 1:] - X_mean) / (X_std)))
X = np.array([normalize(x).astype(theano.config.floatX) for x in X])
y = np.array([x[1:] for x in X])
X = np.array([x[:-1] for x in X])
train_end = int(.8 * len(X))
random_state = np.random.RandomState(1999)
minibatch_size = 20
n_hid = 300
rnn_dim = 1200
train_itr = list_iterator([X, y], minibatch_size, axis=1, make_mask=True,
stop_index=train_end)
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)
train_itr.reset()
datasets_list = [X_mb, X_mb_mask, y_mb, y_mb_mask]
names_list = ["X", "X_mask", "y", "y_mask"]
graph = OrderedDict()
X_sym, X_mask_sym, y_sym, y_mask_sym = add_datasets_to_graph(
datasets_list, names_list, graph)
l1 = relu_layer([X_sym], graph, 'l1', proj_dim=n_hid,
random_state=random_state)
h = lstm_recurrent_layer([l1], X_mask_sym, rnn_dim, graph, 'l1_rec',
random_state=random_state)
l2 = relu_layer([h], graph, 'l2', proj_dim=n_hid,
random_state=random_state)
X_std = np.sqrt(X_sqr - X_mean ** 2)
def normalize(x):
return np.hstack((x[:, 0][:, None], (x[:, 1:] - X_mean) / (X_std)))
def unnormalize(x):
return np.hstack((x[:, 0][:, None], (x[:, 1:] * X_std) + X_mean))
X = np.array([normalize(x).astype(theano.config.floatX) for x in X])
y = np.array([x[1:] for x in X])
X = np.array([x[:-1] for x in X])
minibatch_size = 20 # Size must match size in training, same for above preproc
train_itr = list_iterator([X, y], minibatch_size, axis=1, make_mask=True,
stop_index=1000)
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)
running_mb = X_mb[:1] * 0
running_mask = X_mb_mask[:1] * 0
def gen_sample(rval, random_state, idx=-1):
# binary
# coeffs
# mus
# log_sigmas
# corr
binary, coeffs, mus, sigmas, corr = rval
binary = binary[idx, :, 0]
coeffs = coeffs[idx, :, :]
def normalize(x):
return np.hstack((x[:, 0][:, None], (x[:, 1:] - X_mean) / (X_std)))
def unnormalize(x):
return np.hstack((x[:, 0][:, None], (x[:, 1:] * X_std) + X_mean))
X = np.array([normalize(x).astype(theano.config.floatX) for x in X])
y = np.array([x[1:] for x in X])
X = np.array([x[:-1] for x in X])
minibatch_size = 20 # Size must match size in training, same for above preproc
train_itr = list_iterator([X, y],
minibatch_size,
axis=1,
make_mask=True,
stop_index=1000)
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)
running_mb = X_mb[:1] * 0
running_mask = X_mb_mask[:1] * 0
def gen_sample(rval, random_state, idx=-1):
# binary
# coeffs
# mus
# log_sigmas
# corr
binary, coeffs, mus, sigmas, corr = rval