class NADE_q():
def __init__(self, num_dims, num_hidden, temp=.1, hard=False):
self.nade = NADE(num_dims, num_hidden, temperature=temp, hard=hard)
# for visualization
def get_v(self, num_samples):
return self.nade.sample(num_samples).numpy()
def log_prob(self, num_samples):
v = self.nade.sample(num_samples)
logp_v = self.nade.logprobs(v)
return logp_v, v
def params(self):
return self.nade.params()
class NADEDEC():
def __init__(self,
dim_zh2,
dims_h2h1,
dims_h1v,
activation_fn=tf.nn.relu,
temp=.1,
hard=False,
train_pi=True):
self.prior_h2 = NADE(dim_zh2[1], dim_zh2[0], temp)
self.dec_h2h1 = DEC(dims_h2h1,
name='h2h1',
activation_fn=activation_fn,
temp=temp,
hard=hard)
self.dec_h1v = DEC(dims_h1v,
name='h1v',
activation_fn=activation_fn,
temp=temp,
hard=hard)
# for visualization
def get_v(self, num_samples):
h2 = self.prior_h2.sample(num_samples)
h1 = self.dec_h2h1.get_sample_hard(h2)
v = self.dec_h1v.get_sample_hard(h1)
return v.numpy()
# for training
def get_h2_h1_v(self, num_samples):
h2 = self.prior_h2.sample(num_samples)
h1 = self.dec_h2h1.get_sample_soft(h2)
v = self.dec_h1v.get_sample_soft(h1)
return h2, h1, v
def log_prob(self, num_samples):
h2 = self.prior_h2.sample(num_samples)
logp_h2 = self.prior_h2.logprobs(h2)
logp_h1_given_h2, h1 = self.dec_h2h1.log_conditional_prob(h2)
logp_v_given_h1, v = self.dec_h1v.log_conditional_prob(h1)
return logp_v_given_h1 + logp_h1_given_h2 + logp_h2, h2, h1, v
def params(self):
return self.prior_h2.params() + self.dec_h1v.params(
) + self.dec_h2h1.params()
class NADESBN():
def __init__(self,
dim_z2h,
dim_h2v,
activation_fn=tf.nn.relu,
temp=.1,
hard=False,
train_pi=True):
self.prior_h = NADE(dim_z2h[1], dim_z2h[0], temp)
self.dim_h2v = dim_h2v
self.w_h2v, self.b_h2v = [], []
if activation_fn == tf.nn.relu:
const = 1.0
else:
const = 4.0
assert len(self.dim_h2v) > 1
for d1, d2, i in zip(dim_h2v[:-1], dim_h2v[1:],
xrange(1, len(self.dim_h2v))):
self.w_h2v.append(
tfe.Variable(tf_xavier_init(d1, d2, const=const),
name='sbn.w_h2v.' + str(i)))
self.b_h2v.append(
tfe.Variable(tf.zeros([d2]),
dtype=tf.float32,
name='sbn.b_h2v.' + str(i)))
self.activation_fn = activation_fn
self.temp = temp
self.hard = hard
def h2v(self, h):
v = h
for w, b, i in zip(self.w_h2v, self.b_h2v, xrange(len(self.w_h2v))):
v = tf.matmul(v, w) + b
if i == len(self.w_h2v) - 1:
v = tf.nn.sigmoid(v)
else:
v = self.activation_fn(v)
return v
def get_h(self, num_samples):
h = self.prior_h.sample(num_samples)
return h.numpy()
# for visualization
def get_v(self, num_samples):
h = self.prior_h.sample(num_samples)
v = sample_from_bernoulli(self.h2v(h))
return v.numpy()
# for training
def get_h_and_v(self, num_samples):
h = self.prior_h.sample(num_samples)
v = gumbel_sigmoid_sample(self.h2v(h), self.temp, self.hard)
return h, v
def logprob_v_and_h(self, h, v):
logp_h = self.prior_h.logprobs(h)
logp_v_given_h = bernoulli_log_likelihood(v, self.h2v(h))
return logp_v_given_h + logp_h
def params(self):
return self.prior_h.params() + tuple(self.w_h2v + self.b_h2v)
