tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.input_dict

class tf.contrib.distributions.Chi2WithAbsDf Chi2 with parameter transform df = floor(abs(df)).

tf.FIFOQueue.__init__(capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue') Creates a queue that dequeues elements in a first-in first-out order. A FIFOQueue has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A FIFOQueue holds a list of up to capacity elements. Each element is a fixed-length tuple of tensors whose dtypes are described by dtypes, and whose shapes are optionally described by the shapes argumen

tf.contrib.distributions.Laplace.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.losses.sum_of_squares(*args, **kwargs) Adds a Sum-of-Squares loss to the training procedure. (deprecated) THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_squared_error. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weig

tf.contrib.losses.get_losses(scope=None, loss_collection='losses') Gets the list of losses from the loss_collection. Args: scope: an optional scope for filtering the losses to return. loss_collection: Optional losses collection. Returns: a list of loss tensors.

tf.test.assert_equal_graph_def(actual, expected) Asserts that two GraphDefs are (mostly) the same. Compares two GraphDef protos for equality, ignoring versions and ordering of nodes, attrs, and control inputs. Node names are used to match up nodes between the graphs, so the naming of nodes must be consistent. Args: actual: The GraphDef we have. expected: The GraphDef we expected. Raises: AssertionError: If the GraphDefs do not match. TypeError: If either argument is not a GraphDef.

class tf.contrib.distributions.GammaWithSoftplusAlphaBeta Gamma with softplus transform on alpha and beta.

tf.reduce_max(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the maximum of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is return

tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.