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

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

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