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.distributions.MultivariateNormalDiagWithSoftplusStDev.mean(name='mean') Mean.

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

tf.contrib.distributions.Chi2WithAbsDf.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_continuous

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.clone(name=None, **dist_args)