tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.name

tf.contrib.layers.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner=None, default_id=None, name=None, partition_strategy='div') Lookup embedding results, accounting for invalid IDs and empty features. The partitioned embedding in embedding_weights must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of P. Invalid IDs (< 0) are pruned from input IDs and we

tf.contrib.training.NextQueuedSequenceBatch.total_length The lengths of the original (non-truncated) unrolled examples. Returns: An integer vector of length batch_size, the total lengths.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Produces the integers from 0 to limit-1 in a queue. Args: limit: An int32 scalar tensor. num_epochs: An integer (optional). If specified, range_input_producer produces each integer num_epochs times before generating an OutOfRange error. If not specified, range_input_producer can cycle through the integers an unlimited number of times. shuffle: Boolean. If true, the intege

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

TTypes< T >::ConstScalar tensorflow::Tensor::scalar() const

tf.contrib.metrics.streaming_sensitivity_at_specificity(predictions, labels, specificity, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None) Computes the the specificity at a given sensitivity. The streaming_sensitivity_at_specificity function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the sensitivity at the given specificity value. The threshold for the given specifici

tf.contrib.distributions.Mixture.components

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.