tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.distribution

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

tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided model_fn

tf.contrib.training.NextQueuedSequenceBatch.save_state(state_name, value, name=None) Returns an op to save the current batch of

tf.contrib.layers.summarize_tensor(tensor, tag=None) Summarize a tensor using a suitable summary type. This

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

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

tf.contrib.graph_editor.SubGraphView.__nonzero__() Allows for implicit boolean conversion.

tf.contrib.learn.DNNRegressor.config