tf.contrib.distributions.LaplaceWithSoftplusScale.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.variance(name='variance') Variance.

bool tensorflow::Tensor::FromProto(const TensorProto &other) TF_MUST_USE_RESULT Parse other and construct the tensor. Returns true iff the parsing succeeds. If the parsing fails, the state of *this is unchanged.

tf.contrib.metrics.streaming_auc(predictions, labels, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, curve='ROC', name=None) Computes the approximate AUC via a Riemann sum. The streaming_auc function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the AUC. To discretize the AUC curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values. The a

tf.contrib.distributions.Gamma.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.__init__(num_units, use_peepholes=False, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=False, activation=tanh) Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell use_peepholes: bool, set True to enable diagonal/peephole connections. initializer: (optional) The initializer to use for the weight and projection matrices. num

static Status tensorflow::TensorShapeUtils::MakeShape(const int32 *dims, int64 n, TensorShape *out) Returns a TensorShape whose dimensions are dims[0], dims[1], ..., dims[n-1].

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.distribution