def __init__(self, length=4, vocab_size=2, **kwargs):
self._domain = domains.FixedLengthDiscreteDomain(vocab_size=vocab_size,
length=length)
super(AlternatingChainIsingModel, self).__init__(**kwargs)
if vocab_size % 2:
raise ValueError('vocab_size must be even for '
'AlternatingChainIsingModel.')
potentials = np.zeros(shape=[vocab_size, vocab_size], dtype=np.float32)
for i in range(0, vocab_size, 2):
potentials[i][i + 1] = 1.
potentials[i + 1][i] = 1.
self.potentials = tf.tile(potentials[tf.newaxis, :, :],
[length - 1, 1, 1])
self._global_optima = self._get_global_optima()
def __init__(self,
length=8,
vocab_size=2,
seed=0,
fully_connected=False,
weight_variance=1.0,
**kwargs):
self._domain = domains.FixedLengthDiscreteDomain(vocab_size=vocab_size,
length=length)
super(IsingModel, self).__init__(**kwargs)
random = np.random.RandomState(seed=seed)
if fully_connected:
self.potentials = tf.constant(random.normal(
size=[length, length, vocab_size, vocab_size],
scale=weight_variance),
dtype=tf.float32)
else:
self.potentials = tf.constant(random.normal(
size=[length - 1, vocab_size, vocab_size],
scale=weight_variance),
dtype=tf.float32)
self._fully_connected = fully_connected
def build_domain(length=8, vocab_size=4, **kwargs):
"""Creates a `FixedLengthDiscreteDomain` with default arguments."""
return domains.FixedLengthDiscreteDomain(length=length,
vocab_size=vocab_size,
**kwargs)