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

tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1) Run output_dict tensors n

tf.contrib.learn.LinearRegressor.config

tf.contrib.learn.DNNClassifier.get_variable_value(name) Returns value of the variable given by name. Args:

tf.contrib.learn.BaseEstimator.get_variable_value(name) Returns value of the variable given by name. Args:

tf.contrib.learn.TensorFlowRNNRegressor.__init__(rnn_size, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None

tf.contrib.learn.Estimator.get_params(deep=True) Get parameters for this estimator. Args:

tf.contrib.learn.DNNRegressor.__init__(hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gra

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

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