tf.contrib.learn.run_feeds(*args, **kwargs) See run_feeds_iter(). Returns a list instead of an iterator.

tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=0, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None,

tf.contrib.learn.DNNClassifier.export(export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, default_batch_size=1, expo

tf.contrib.learn.TensorFlowEstimator.set_params(**params) Set the parameters of this estimator. The

tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See

tf.contrib.learn.TensorFlowRNNClassifier.predict_proba(x, batch_size=None) Predict class probability of the input samples x

class tf.contrib.learn.ModeKeys Standard names for model modes. The following standard keys are

tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None) Constructs

tf.contrib.learn.TensorFlowEstimator.__repr__()

tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)