tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.loss(final_loss, name='Loss')

tf.nn.rnn_cell.LSTMStateTuple.c Alias for field number 0

tf.nn.rnn_cell.BasicLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shap

tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.WishartFull.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1

class tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor NormalWithSoftplusSigmaTensor is a StochasticTensor backed by the distribution NormalWithSoftplusSigma.

tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma') Construct Gamma distributions with parameters alpha and beta. The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation). Args: alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values. beta: Floating point tensor, the inverse scale params of the distribution(s). beta

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value(name='value')

tf.contrib.distributions.WishartCholesky.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.