tf.contrib.distributions.Chi2WithAbsDf.parameters Dictionary of parameters used by this Distribution.

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

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name

tf.contrib.distributions.Normal.mu Distribution parameter for the mean.

tf.contrib.distributions.DirichletMultinomial.mode(name='mode') Mode.

tf.contrib.distributions.StudentT.name Name prepended to all ops created by this Distribution.

tf.errors.PermissionDeniedError.__init__(node_def, op, message) Creates a PermissionDeniedError.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf') Probability density function. 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. Raises: TypeError: if not is_continuous.

tf.contrib.crf.crf_sequence_score(inputs, tag_indices, sequence_lengths, transition_params) Computes the unnormalized score for a tag sequence. Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the unnormalized score. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] transition matrix.