tf.contrib.layers.sum_regularizer(regularizer_list, scope=None) Returns a function that applies the sum of multiple regularizers. Args: regularizer_list: A list of regularizers to apply. scope: An optional scope name Returns: A function with signature sum_reg(weights) that applies the sum of all the input regularizers.

TTypes<T>::Flat tensorflow::Tensor::flat() Return the tensor data as an Eigen::Tensor of the data type and a specified shape. These methods allow you to access the data with the dimensions and sizes of your choice. You do not need to know the number of dimensions of the Tensor to call them. However, they CHECK that the type matches and the dimensions requested creates an Eigen::Tensor with the same number of elements as the tensor. Example: Tensor my_ten(...built with Shape{planes: 4, ro

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of sha

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate along a new outer dimension. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.graph_editor.assign_renamed_collections_handler(info, elem, elem_) Add the transformed elem to the (renamed) collections of elem. Args: info: Transform._Info instance. elem: the original element (tf.Tensor or tf.Operation) elem_: the transformed element

tf.contrib.distributions.LaplaceWithSoftplusScale.variance(name='variance') Variance.

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.DirichletMultinomial.__init__(n, alpha, validate_args=False, allow_nan_stats=True, name='DirichletMultinomial') Initialize a batch of DirichletMultinomial distributions. Args: n: Non-negative floating point tensor, whose dtype is the same as alpha. The shape is broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values. alpha: Positive floating

tf.contrib.graph_editor.placeholder_name(t=None, scope=None) Create placeholder name for tjhe graph editor. Args: t: optional tensor on which the placeholder operation's name will be based on scope: absolute scope with which to predix the placeholder's name. None means that the scope of t is preserved. "" means the root scope. Returns: A new placeholder name prefixed by "geph". Note that "geph" stands for Graph Editor PlaceHolder. This convention allows to quickly identify the placeholder

tf.contrib.bayesflow.monte_carlo.expectation(f, p, z=None, n=None, seed=None, name='expectation') Monte Carlo estimate of an expectation: E_p[f(Z)] with sample mean. This Op returns n^{-1} sum_{i=1}^n f(z_i), where z_i ~ p \approx E_p[f(Z)] User supplies either Tensor of samples z, or number of samples to draw n Args: f: Callable mapping samples from p to Tensors. p: tf.contrib.distributions.BaseDistribution. z: Tensor of samples from p, produced by p.sample_n. n: Integer Tensor. Number