tensorflow_probability/python/layers/distribution_layer.py
def new(params, event_shape=(), dtype=None, validate_args=False, name=None):
    """Create the distribution instance from a `params` vector."""
    with tf.compat.v1.name_scope(name, 'IndependentBernoulli',
                                 [params, event_shape]):
      params = tf.convert_to_tensor(value=params, name='params')
      event_shape = dist_util.expand_to_vector(
          tf.convert_to_tensor(
              value=event_shape, name='event_shape', dtype_hint=tf.int32),
          tensor_name='event_shape')
      new_shape = tf.concat([
          tf.shape(input=params)[:-1],
          event_shape,
      ], axis=0)
      dist = tfd.Independent(
          tfd.Bernoulli(
              logits=tf.reshape(params, new_shape),
              dtype=dtype or params.dtype.base_dtype,
              validate_args=validate_args),
          reinterpreted_batch_ndims=tf.size(input=event_shape),
          validate_args=validate_args)
      dist._logits = dist.distribution._logits  # pylint: disable=protected-access
      dist._probs = dist.distribution._probs  # pylint: disable=protected-access
      dist.logits = tfd.Bernoulli.logits
      dist.probs = tfd.Bernoulli.probs
      return dist
def new(params, event_shape=(), dtype=None, validate_args=False, name=None):
    """Create the distribution instance from a `params` vector."""
    with tf.compat.v1.name_scope(name, 'IndependentBernoulli',
                                 [params, event_shape]):
      params = tf.convert_to_tensor(value=params, name='params')
      event_shape = dist_util.expand_to_vector(
          tf.convert_to_tensor(
              value=event_shape, name='event_shape', dtype_hint=tf.int32),
          tensor_name='event_shape')
      new_shape = tf.concat([
          tf.shape(input=params)[:-1],
          event_shape,
      ], axis=0)
      dist = tfd.Independent(
          tfd.Bernoulli(
              logits=tf.reshape(params, new_shape),
              dtype=dtype or params.dtype.base_dtype,
              validate_args=validate_args),
          reinterpreted_batch_ndims=tf.size(input=event_shape),
          validate_args=validate_args)
      dist._logits = dist.distribution._logits  # pylint: disable=protected-access
      dist._probs = dist.distribution._probs  # pylint: disable=protected-access
      dist.logits = tfd.Bernoulli.logits
      dist.probs = tfd.Bernoulli.probs
      return dist
def new ( params , event_shape = ( ) , dtype = None , validate_args = False , name = None ) : with tf . compat . v1 . name_scope ( name , 'IndependentBernoulli' , [ params , event_shape ] ) : params = tf . convert_to_tensor ( value = params , name = 'params' ) event_shape = dist_util . expand_to_vector ( tf . convert_to_tensor ( value = event_shape , name = 'event_shape' , dtype_hint = tf . int32 ) , tensor_name = 'event_shape' ) new_shape = tf . concat ( [ tf . shape ( input = params ) [ : - 1 ] , event_shape , ] , axis = 0 ) dist = tfd . Independent ( tfd . Bernoulli ( logits = tf . reshape ( params , new_shape ) , dtype = dtype or params . dtype . base_dtype , validate_args = validate_args ) , reinterpreted_batch_ndims = tf . size ( input = event_shape ) , validate_args = validate_args ) dist . _logits = dist . distribution . _logits # pylint: disable=protected-access dist . _probs = dist . distribution . _probs # pylint: disable=protected-access dist . logits = tfd . Bernoulli . logits dist . probs = tfd . Bernoulli . probs return dist
Create the distribution instance from a `params` vector.
Create the distribution instance from a params vector .