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

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

tf.contrib.learn.TensorFlowRNNRegressor.get_variable_value(name) Returns value of the variable given by name.

tf.contrib.learn.LinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None) Incremental

tf.contrib.learn.DNNRegressor.predict(*args, **kwargs) Returns predictions for given features. (deprecated arguments)

tf.contrib.learn.TensorFlowEstimator.get_tensor(name) Returns tensor by name. Args:

tf.contrib.learn.TensorFlowEstimator.restore(cls, path, config=None) Restores model from give path. Args:

tf.contrib.learn.LinearRegressor.predict(*args, **kwargs) Returns predictions for given features. (deprecated arguments)

tf.contrib.learn.Estimator.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None) Incremental fit

tf.contrib.learn.infer(restore_checkpoint_path, output_dict, feed_dict=None) Restore graph from restore_checkpoint_path