tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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

tf.nn.rnn_cell.DropoutWrapper.__init__(cell, input_keep_prob=1.0, output_keep_prob=1.0, seed=None) Create a cell with added input and/or output dropout. Dropout is never used on the state. Args: cell: an RNNCell, a projection to output_size is added to it. input_keep_prob: unit Tensor or float between 0 and 1, input keep probability; if it is float and 1, no input dropout will be added. output_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is float and 1, no

tf.ones(shape, dtype=tf.float32, name=None) Creates a tensor with all elements set to 1. This operation returns a tensor of type dtype with shape shape and all elements set to 1. For example: tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]] Args: shape: Either a list of integers, or a 1-D Tensor of type int32. dtype: The type of an element in the resulting Tensor. name: A name for the operation (optional). Returns: A Tensor with all elements set to 1.

tf.contrib.framework.assert_scalar_int(tensor) Assert tensor is 0-D, of type tf.int32 or tf.int64. Args: tensor: Tensor to test. Returns: tensor, for chaining. Raises: ValueError: if tensor is not 0-D, of type tf.int32 or tf.int64.

tf.contrib.distributions.Multinomial.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

class tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor BernoulliWithSigmoidPTensor is a StochasticTensor backed by the distribution BernoulliWithSigmoidP.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.entropy(name='entropy')

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

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

tf.floor(x, name=None) Returns element-wise largest integer not greater than x. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.