tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.name

tf.contrib.distributions.StudentT.dtype The DType of Tensors handled by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value(name='value')

class tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor BetaWithSoftplusABTensor is a StochasticTensor backed by the distribution BetaWithSoftplusAB.

tf.contrib.distributions.WishartFull.mean(name='mean') Mean.

tf.WholeFileReader.restore_state(state, name=None) Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. Args: state: A string Tensor. Result of a SerializeState of a Reader with matching type. name: A name for the operation (optional). Returns: The created Operation.

class tf.TensorArray Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays. This class is meant to be used with dynamic iteration primitives such as while_loop and map_fn. It supports gradient back-propagation via special "flow" control flow dependencies.

tf.contrib.learn.LinearRegressor.dnn_bias_ Returns bias of deep neural network part.

tf.image.convert_image_dtype(image, dtype, saturate=False, name=None) Convert image to dtype, scaling its values if needed. Images that are represented using floating point values are expected to have values in the range [0,1). Image data stored in integer data types are expected to have values in the range [0,MAX], where MAX is the largest positive representable number for the data type. This op converts between data types, scaling the values appropriately before casting. Note that converting

tf.contrib.distributions.DirichletMultinomial.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.