tf.contrib.distributions.Chi2WithAbsDf.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.WishartFull.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.graph_editor.SubGraphView.remap_outputs(new_output_indices) Remap the output of the subgraph. If the output of the original subgraph are [t0, t1, t2], remapping to [1,1,0] will create a new instance whose outputs is [t1, t1, t0]. Note that this is only modifying the view: the underlying tf.Graph is not affected. Args: new_output_indices: an iterable of integers representing a mapping between the old outputs and the new ones. This mapping can be under-complete and can have repetitio

tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.layers.summarize_collection(collection, name_filter=None, summarizer=summarize_tensor) Summarize a graph collection of tensors, possibly filtered by name. The layers module defines convenience functions summarize_variables, summarize_weights and summarize_biases, which set the collection argument of summarize_collection to VARIABLES, WEIGHTS and BIASES, respectively.

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

tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n) Posterior Normal distribution with conjugate prior on the mean. This model assumes that n observations (with sum s) come from a Normal with unknown mean mu (described by the Normal prior) and known variance sigma^2. The "known sigma posterior" is the distribution of the unknown mu. Accepts a prior Normal distribution object, having parameters mu0 and sigma0, as well as known sigma values of the predictive dist

tf.contrib.distributions.Dirichlet.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.sign(x, name=None) Returns an element-wise indication of the sign of a number. y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0. For complex numbers, y = sign(x) = x / |x| if x != 0, otherwise y = 0. Args: x: A Tensor or SparseTensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor or SparseTensor, respectively. Has the same type as x.