tf.contrib.learn.TensorFlowEstimator.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.layers.l2_regularizer(scale, scope=None) Returns a function that can be used to apply L2 regularization to weights. Small values of L2 can help prevent overfitting the training data. Args: scale: A scalar multiplier Tensor. 0.0 disables the regularizer. scope: An optional scope name. Returns: A function with signature l2(weights) that applies L2 regularization. Raises: ValueError: If scale is negative or if scale is not a float.

tf.ReaderBase.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.

tf.image.grayscale_to_rgb(images, name=None) Converts one or more images from Grayscale to RGB. Outputs a tensor of the same DType and rank as images. The size of the last dimension of the output is 3, containing the RGB value of the pixels. Args: images: The Grayscale tensor to convert. Last dimension must be size 1. name: A name for the operation (optional). Returns: The converted grayscale image(s).

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.entropy(name='entropy')

tf.contrib.distributions.MultivariateNormalCholesky.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.MultivariateNormalCholesky.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.

tf.contrib.distributions.Multinomial.name Name prepended to all ops created by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.dtype

tf.truediv(x, y, name=None) Divides x / y elementwise, always producing floating point results. The same as tf.div for floating point arguments, but casts integer arguments to floating point before dividing so that the result is always floating point. This op is generated by normal x / y division in Python 3 and in Python 2.7 with from __future__ import division. If you want integer division that rounds down, use x // y or tf.floordiv. x and y must have the same numeric type. If the inputs are