tf.contrib.distributions.BetaWithSoftplusAB.mode()

tf.contrib.distributions.BetaWithSoftplusAB.mode(name='mode') Mode. Additional documentation from Beta: Note that the mode for the Beta distribution is only defined when a > 1, b > 1. This returns the mode when a > 1 and b > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.WishartCholesky.is_continuous

tf.contrib.distributions.WishartCholesky.is_continuous

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor MultivariateNormalDiagWithSoftplusStDevTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagWithSoftplusStDev.

tf.contrib.framework.reduce_sum_n()

tf.contrib.framework.reduce_sum_n(tensors, name=None) Reduce tensors to a scalar sum. This reduces each tensor in tensors to a scalar via tf.reduce_sum, then adds them via tf.add_n. Args: tensors: List of tensors, all of the same numeric type. name: Tensor name, and scope for all other ops. Returns: Total loss tensor, or None if no losses have been configured. Raises: ValueError: if losses is missing or empty.

tf.contrib.distributions.Chi2WithAbsDf.variance()

tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance') Variance.

tf.contrib.distributions.MultivariateNormalCholesky.mode()

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

tf.contrib.learn.RunConfig

class tf.contrib.learn.RunConfig This class specifies the specific configurations for the run. If you're a Google-internal user using command line flags with learn_runner.py (for instance, to do distributed training or to use parameter servers), you probably want to use learn_runner.EstimatorConfig instead.

tf.contrib.distributions.Mixture.entropy()

tf.contrib.distributions.Mixture.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.learn.DNNClassifier

class tf.contrib.learn.DNNClassifier A classifier for TensorFlow DNN models. Example: education = sparse_column_with_hash_bucket(column_name="education", hash_bucket_size=1000) occupation = sparse_column_with_hash_bucket(column_name="occupation", hash_bucket_size=1000) education_emb = embedding_column(sparse_id_column=education, dimension=16, combiner="sum") occupation_emb =

tf.nn.rnn_cell.LSTMStateTuple.h

tf.nn.rnn_cell.LSTMStateTuple.h Alias for field number 1