tf.contrib.distributions.Exponential.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.std(name='std') Standard deviation.

tf.contrib.distributions.Poisson.mode(name='mode') Mode. Additional documentation from Poisson: Note that when lam is an integer, there are actually two modes. Namely, lam and lam - 1 are both modes. Here we return only the larger of the two modes.

tf.SparseTensorValue.__new__(_cls, indices, values, shape) Create new instance of SparseTensorValue(indices, values, shape)

tf.contrib.learn.monitors.EveryN.run_on_all_workers

tf.contrib.learn.monitors.CaptureVariable.post_step(step, session)

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.mean(name='mean')

tf.contrib.distributions.BernoulliWithSigmoidP.mode(name='mode') Mode. Additional documentation from Bernoulli: Returns 1 if p > 1-p and 0 otherwise.

tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). 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.

tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.