tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.name

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.dtype

class tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor WishartFullTensor is a StochasticTensor backed by the distribution WishartFull.

tf.contrib.graph_editor.SubGraphView.__copy__() Create a copy of this subgraph. Note that this class is a "view", copying it only create another view and does not copy the underlying part of the tf.Graph. Returns: A new identical instance of the original subgraph view.

tf.parse_tensor(serialized, out_type, name=None) Transforms a serialized tensorflow.TensorProto proto into a Tensor. Args: serialized: A Tensor of type string. A scalar string containing a serialized TensorProto proto. out_type: A tf.DType. The type of the serialized tensor. The provided type must match the type of the serialized tensor and no implicit conversion will take place. name: A name for the operation (optional). Returns: A Tensor of type out_type. A Tensor of type out_type.

class tf.contrib.learn.LinearRegressor Linear regressor model. Train a linear regression model to predict target variable value given observation of feature values. 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_x_occupation = crossed_column(columns=[edu

tf.contrib.distributions.Normal.sigma Distribution parameter for standard deviation.