tf.contrib.learn.LinearRegressor.__init__(feature_columns, model_dir=None, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=None

tf.contrib.learn.DNNClassifier.config

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

tf.contrib.learn.DNNClassifier.get_variable_names() Returns list of all variable names in this model.

tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1

tf.contrib.learn.DNNClassifier.model_dir

tf.contrib.learn.LinearClassifier.config

tf.contrib.learn.TensorFlowEstimator.save(path) Saves checkpoints and graph to given path. Args:

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

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