tensorflow_probability/examples/deep_exponential_family.py
def trainable_positive_deterministic(shape, min_loc=1e-3, name=None):
  """Learnable Deterministic distribution over positive reals."""
  with tf.compat.v1.variable_scope(
      None, default_name="trainable_positive_deterministic"):
    unconstrained_loc = tf.compat.v1.get_variable("unconstrained_loc", shape)
    loc = tf.maximum(tf.nn.softplus(unconstrained_loc), min_loc)
    rv = ed.Deterministic(loc=loc, name=name)
    return rv
def trainable_positive_deterministic(shape, min_loc=1e-3, name=None):
  with tf.compat.v1.variable_scope(
      None, default_name="trainable_positive_deterministic"):
    unconstrained_loc = tf.compat.v1.get_variable("unconstrained_loc", shape)
    loc = tf.maximum(tf.nn.softplus(unconstrained_loc), min_loc)
    rv = ed.Deterministic(loc=loc, name=name)
    return rv
def trainable_positive_deterministic ( shape , min_loc = 1e-3 , name = None ) : with tf . compat . v1 . variable_scope ( None , default_name = "trainable_positive_deterministic" ) : unconstrained_loc = tf . compat . v1 . get_variable ( "unconstrained_loc" , shape ) loc = tf . maximum ( tf . nn . softplus ( unconstrained_loc ) , min_loc ) rv = ed . Deterministic ( loc = loc , name = name ) return rv
Learnable Deterministic distribution over positive reals.
Learnable Deterministic distribution over positive reals .