def __init__(self,
mean,
u_tril,
v_tril,
group_ndims=0,
is_reparameterized=True,
use_path_derivative=False,
check_numerics=False,
**kwargs):
self._check_numerics = check_numerics
self._mean = tf.convert_to_tensor(mean)
self._mean = assert_rank_at_least(
self._mean, 2, 'MatrixVariateNormalCholesky.mean')
self._n_row = get_shape_at(self._mean, -2)
self._n_col = get_shape_at(self._mean, -1)
self._u_tril = tf.convert_to_tensor(u_tril)
self._u_tril = assert_rank_at_least(
self._u_tril, 2, 'MatrixVariateNormalCholesky.u_tril')
self._v_tril = tf.convert_to_tensor(v_tril)
self._v_tril = assert_rank_at_least(
self._v_tril, 2, 'MatrixVariateNormalCholesky.v_tril')
# Static shape check
expected_u_shape = self._mean.get_shape()[:-1].concatenate(
[self._n_row if isinstance(self._n_row, int) else None])
self._u_tril.get_shape().assert_is_compatible_with(expected_u_shape)
expected_v_shape = self._mean.get_shape()[:-2].concatenate(
[self._n_col if isinstance(self._n_col, int) else None] * 2)
self._v_tril.get_shape().assert_is_compatible_with(expected_v_shape)
# Dynamic
expected_u_shape = tf.concat(
[tf.shape(self._mean)[:-1], [self._n_row]], axis=0)
actual_u_shape = tf.shape(self._u_tril)
msg = ['MatrixVariateNormalCholesky.u_tril should have compatible '
'shape with mean. Expected', expected_u_shape, ' got ',
actual_u_shape]
assert_u_ops = tf.assert_equal(expected_u_shape, actual_u_shape, msg)
expected_v_shape = tf.concat(
[tf.shape(self._mean)[:-2], [self._n_col, self._n_col]], axis=0)
actual_v_shape = tf.shape(self._v_tril)
msg = ['MatrixVariateNormalCholesky.v_tril should have compatible '
'shape with mean. Expected', expected_v_shape, ' got ',
actual_v_shape]
assert_v_ops = tf.assert_equal(expected_v_shape, actual_v_shape, msg)
with tf.control_dependencies([assert_u_ops, assert_v_ops]):
self._u_tril = tf.identity(self._u_tril)
self._v_tril = tf.identity(self._v_tril)
dtype = assert_same_float_dtype(
[(self._mean, 'MatrixVariateNormalCholesky.mean'),
(self._u_tril, 'MatrixVariateNormalCholesky.u_tril'),
(self._v_tril, 'MatrixVariateNormalCholesky.v_tril')])
super(MatrixVariateNormalCholesky, self).__init__(
dtype=dtype,
param_dtype=dtype,
is_continuous=True,
is_reparameterized=is_reparameterized,
use_path_derivative=use_path_derivative,
group_ndims=group_ndims,
**kwargs)
def test_get_shape_at(self):
with self.test_session(use_gpu=True):
ph = tf.placeholder(tf.float32, [2, None])
# Static
self.assertEqual(get_shape_at(ph, 0), 2)
# Dynamic
fd = {ph: np.ones([2, 9])}
self.assertEqual(get_shape_at(ph, 1).eval(fd), 9)
def __init__(self,
temperature,
logits,
group_ndims=0,
is_reparameterized=True,
use_path_derivative=False,
check_numerics=False,
**kwargs):
self._logits = tf.convert_to_tensor(logits)
self._temperature = tf.convert_to_tensor(temperature)
param_dtype = assert_same_float_dtype(
[(self._logits, 'Concrete.logits'),
(self._temperature, 'Concrete.temperature')])
self._logits = assert_rank_at_least_one(
self._logits, 'Concrete.logits')
self._n_categories = get_shape_at(self._logits, -1)
self._temperature = assert_scalar(
self._temperature, 'Concrete.temperature')
self._check_numerics = check_numerics
super(Concrete, self).__init__(
dtype=param_dtype,
param_dtype=param_dtype,
is_continuous=True,
is_reparameterized=is_reparameterized,
use_path_derivative=use_path_derivative,
group_ndims=group_ndims,
**kwargs)
def __init__(self,
logits,
normalize_logits=True,
dtype=tf.int32,
group_ndims=0,
**kwargs):
self._logits = tf.convert_to_tensor(logits)
param_dtype = assert_same_float_dtype(
[(self._logits, 'UnnormalizedMultinomial.logits')])
assert_dtype_is_int_or_float(dtype)
self._logits = assert_rank_at_least_one(
self._logits, 'UnnormalizedMultinomial.logits')
self._n_categories = get_shape_at(self._logits, -1)
self.normalize_logits = normalize_logits
super(UnnormalizedMultinomial, self).__init__(
dtype=dtype,
param_dtype=param_dtype,
is_continuous=False,
is_reparameterized=False,
group_ndims=group_ndims,
**kwargs)
def __init__(self,
logits,
n_experiments,
dtype=None,
group_ndims=0,
**kwargs):
self._logits = tf.convert_to_tensor(logits)
param_dtype = assert_same_float_dtype([(self._logits,
'Multinomial.logits')])
if dtype is None:
dtype = tf.int32
assert_same_float_and_int_dtype([], dtype)
self._logits = assert_rank_at_least_one(self._logits,
'Multinomial.logits')
self._n_categories = get_shape_at(self._logits, -1)
self._n_experiments = assert_positive_int32_integer(
n_experiments, 'Multinomial.n_experiments')
super(Multinomial, self).__init__(dtype=dtype,
param_dtype=param_dtype,
is_continuous=False,
is_reparameterized=False,
group_ndims=group_ndims,
**kwargs)
def __init__(self,
mean,
cov_tril,
group_ndims=0,
is_reparameterized=True,
use_path_derivative=False,
check_numerics=False,
**kwargs):
self._check_numerics = check_numerics
self._mean = tf.convert_to_tensor(mean)
self._mean = assert_rank_at_least_one(
self._mean, 'MultivariateNormalCholesky.mean')
self._n_dim = get_shape_at(self._mean, -1)
self._cov_tril = tf.convert_to_tensor(cov_tril)
self._cov_tril = assert_rank_at_least(
self._cov_tril, 2, 'MultivariateNormalCholesky.cov_tril')
# Static shape check
expected_shape = self._mean.get_shape().concatenate(
[self._n_dim if isinstance(self._n_dim, int) else None])
self._cov_tril.get_shape().assert_is_compatible_with(expected_shape)
# Dynamic
expected_shape = tf.concat([tf.shape(self._mean), [self._n_dim]],
axis=0)
actual_shape = tf.shape(self._cov_tril)
msg = [
'MultivariateNormalCholesky.cov_tril should have compatible '
'shape with mean. Expected', expected_shape, ' got ', actual_shape
]
assert_ops = [tf.assert_equal(expected_shape, actual_shape, msg)]
with tf.control_dependencies(assert_ops):
self._cov_tril = tf.identity(self._cov_tril)
dtype = assert_same_float_dtype([
(self._mean, 'MultivariateNormalCholesky.mean'),
(self._cov_tril, 'MultivariateNormalCholesky.cov_tril')
])
super(MultivariateNormalCholesky,
self).__init__(dtype=dtype,
param_dtype=dtype,
is_continuous=True,
is_reparameterized=is_reparameterized,
use_path_derivative=use_path_derivative,
group_ndims=group_ndims,
**kwargs)
