tf.contrib.rnn.CoupledInputForgetGateLSTMCell.output_size

tf.contrib.distributions.DirichletMultinomial.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T fo

tf.sparse_reshape(sp_input, shape, name=None) Reshapes a SparseTensor to represent values in a new dense shape. This operation has the same semantics as reshape on the represented dense tensor. The indices of non-empty values in sp_input are recomputed based on the new dense shape, and a new SparseTensor is returned containing the new indices and new shape. The order of non-empty values in sp_input is unchanged. If one component of shape is the special value -1, the size of that dimension is c

tf.contrib.learn.TensorFlowRNNClassifier.__repr__()

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.loss(final_loss, name='Loss')

tf.contrib.learn.RunConfig.job_name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.graph

tf.contrib.learn.TensorFlowRNNClassifier.weights_ Returns weights of the rnn layer.

class tf.contrib.learn.TensorFlowRNNRegressor TensorFlow RNN Regressor model.

Rep64* tensorflow::TensorShape::unused_aligner