tf.contrib.distributions.Gamma.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.NormalWithSoftplusSigma.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.learn.BaseEstimator.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.distributions.TransformedDistribution.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.layers.max_pool2d(*args, **kwargs) Adds a 2D Max Pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A Tensor of size [batch_size, height, width, channels]. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that pr

tf.invert_permutation(x, name=None) Computes the inverse permutation of a tensor. This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x, which represents the indices of a zero-based array, and swaps each value with its index position. In other words, for an output tensor y and an input tensor x, this operation computes the following: y[x[i]] = i for i in [0, 1, ..., len(x) - 1] The values must include 0. There can be no duplicate values or negative values

tf.OpError.__init__(node_def, op, message, error_code) Creates a new OpError indicating that a particular op failed. Args: node_def: The node_def_pb2.NodeDef proto representing the op that failed, if known; otherwise None. op: The ops.Operation that failed, if known; otherwise None. message: The message string describing the failure. error_code: The error_codes_pb2.Code describing the error.

class tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor LaplaceTensor is a StochasticTensor backed by the distribution Laplace.

tf.FixedLengthRecordReader.read_up_to(queue, num_records, name=None) Returns up to num_records (key, value pairs) produced by a reader. Will dequeue a work unit from queue if necessary (e.g., when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch. Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. num_records: Number of records t

tf.TFRecordReader.__init__(name=None, options=None) Create a TFRecordReader. Args: name: A name for the operation (optional). options: A TFRecordOptions object (optional).