tf.contrib.learn.monitors.EveryN.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.contrib.learn.monitors.CheckpointSaver.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.distributions.InverseGamma.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.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.variance(name='variance') Variance. Additional documentation from InverseGamma: Variance for inverse gamma is defined only for alpha > 2. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value(name='value')

tf.inv(x, name=None) Computes the reciprocal of x element-wise. I.e., \(y = 1 / x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.clone(name=None, **dist_args)

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

tf.contrib.distributions.MultivariateNormalDiag.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.

class tf.WholeFileReader A Reader that outputs the entire contents of a file as a value. To use, enqueue filenames in a Queue. The output of Read will be a filename (key) and the contents of that file (value). See ReaderBase for supported methods.