tf.contrib.learn.LinearClassifier.get_variable_names()

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

tf.contrib.learn.DNNRegressor.config

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

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

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

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

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

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