tf.FixedLenFeature.__repr__() Return a nicely formatted representation string

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(name='entropy') Shanon entropy in nats.

tf.contrib.distributions.Multinomial.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.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.value_type

tf.contrib.distributions.BetaWithSoftplusAB.std(name='std') Standard deviation.

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.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type