tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: floa

tf.PaddingFIFOQueue.__init__(capacity, dtypes, shapes, names=None, shared_name=None, name='padding_fifo_queue') Creates a queue that dequeues elements in a first-in first-out order. A PaddingFIFOQueue has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A PaddingFIFOQueue 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 described by the s

tf.ifft2d(input, name=None) Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most 2 dimensions of input. Args: input: A Tensor of type complex64. A complex64 tensor. name: A name for the operation (optional). Returns: A Tensor of type complex64. A complex64 tensor of the same shape as input. The inner-most 2 dimensions of input are replaced with their inverse 2D Fourier Transform.

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.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

tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace(log_f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler_logspace') Importance sampling with a positive function, in log-space. With p(z) := exp{log_p(z)}, and f(z) = exp{log_f(z)}, this Op returns Log[ n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ] ], z_i ~ q, \approx Log[ E_q[ f(Z) p(Z) / q(Z) ] ] = Log[E_p[f(Z)]] This integral is done in log-space with max-subtraction to bet

tf.contrib.framework.model_variable(*args, **kwargs) Gets an existing model variable with these parameters or creates a new one. Args: name: the name of the new or existing variable. shape: shape of the new or existing variable. dtype: type of the new or existing variable (defaults to DT_FLOAT). initializer: initializer for the variable if one is created. regularizer: a (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the colle

tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: flo

tf.contrib.framework.get_model_variables(scope=None, suffix=None) Gets the list of model variables, filtered by scope and/or suffix. Args: scope: an optional scope for filtering the variables to return. suffix: an optional suffix for filtering the variables to return. Returns: a list of variables in collection with scope and suffix.

tf.contrib.framework.deprecated(date, instructions) Decorator for marking functions or methods deprecated. This decorator logs a deprecation warning whenever the decorated function is called. It has the following format: (from ) is deprecated and will be removed after . Instructions for updating: will include the class name if it is a method. It also edits the docstring of the function: ' (deprecated)' is appended to the first line of the docstring and a deprecation notice is prepended to t