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

tf.contrib.layers.one_hot_encoding(*args, **kwargs) Transform numeric labels into onehot_labels using tf.one_hot. Args: labels: [batch_size] target labels. num_classes: total number of classes. on_value: A scalar defining the on-value. off_value: A scalar defining the off-value. outputs_collections: collection to add the outputs. scope: Optional scope for name_scope. Returns: one hot encoding of the labels.

tf.TFRecordReader.supports_serialize Whether the Reader implementation can serialize its state.

tf.is_non_decreasing(x, name=None) Returns True if x is non-decreasing. Elements of x are compared in row-major order. The tensor [x[0],...] is non-decreasing if for every adjacent pair we have x[i] <= x[i+1]. If x has less than two elements, it is trivially non-decreasing. See also: is_strictly_increasing Args: x: Numeric Tensor. name: A name for this operation (optional). Defaults to "is_non_decreasing" Returns: Boolean Tensor, equal to True iff x is non-decreasing. Raises: TypeError

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.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.mean(name='mean')

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.sparse_reduce_sum(sp_input, reduction_axes=None, keep_dims=False) Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_sum(). In particular, this Op also returns a dense Tensor instead of a sparse one. Reduces sp_input along the dimensions given in reduction_axes. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_axes. If keep_dims is true, the reduced dimensions a

tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from DirichletMultinomial: For each batch of counts [n_1,...,n_k], P[counts] is the probability that after sampling n draws from this Dirichlet Multinomial distribution, the number of draws falling in class j is n_j. Note that different sequences of draws can result in the same counts, thus the probability includes a combina

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