tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.input_dict

tf.contrib.distributions.StudentT.allow_nan_stats Python boolean describing behavior when a stat is undefined.

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

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.allow_nan_stats Python boolean describing behavior when a stat is undefined

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value_type

tf.contrib.distributions.BernoulliWithSigmoidP.is_reparameterized

tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None) Most basic RNN: output = new_state = activation(W * input + U

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

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