tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.dtype

tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf') Log probability density function.

tf.contrib.distributions.MultivariateNormalFull.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape)

tf.contrib.metrics.streaming_recall_at_k(*args, **kwargs) Computes the recall@k of the predictions with respect to dense labels

tf.contrib.learn.monitors.GraphDump.epoch_end(epoch) End epoch. Args:

tf.contrib.learn.monitors.SummarySaver.set_estimator(estimator)

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.learn.monitors.StepCounter.step_begin(step) Overrides BaseMonitor.step_begin. When

tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf') Probability density function. Args: