tf.fill(dims, value, name=None) Creates a tensor filled with a scalar value. This operation creates a tensor of shape dims and fills it with value. For example: # Output tensor has shape [2, 3]. fill([2, 3], 9) ==> [[9, 9, 9] [9, 9, 9]] Args: dims: A Tensor of type int32. 1-D. Represents the shape of the output tensor. value: A Tensor. 0-D (scalar). Value to fill the returned tensor. name: A name for the operation (optional). Returns: A Tensor. Has the same type a

tf.contrib.crf.crf_unary_score(tag_indices, sequence_lengths, inputs) Computes the unary scores of tag sequences. Args: tag_indices: A [batch_size, max_seq_len] matrix of tag indices. sequence_lengths: A [batch_size] vector of true sequence lengths. inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials. Returns: unary_scores: A [batch_size] vector of unary scores.

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

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.is_reparameterized

tf.asin(x, name=None) Computes asin of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Exponential.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.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.distribution

tf.contrib.distributions.InverseGamma.entropy(name='entropy') Shanon entropy in nats. Additional documentation from InverseGamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

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

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.name