tf.contrib.learn.DNNRegressor.linear_weights_ Returns weights per feature of the linear part.

tf.contrib.learn.DNNRegressor.linear_bias_ Returns bias of the linear part.

tf.contrib.learn.DNNRegressor.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

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

tf.contrib.learn.DNNRegressor.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See Trainable. Raises: ValueError: If x or y are not None while input_fn is not None. ValueError: If both steps and max_steps are not None.

tf.contrib.learn.DNNRegressor.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogethe

tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See Evaluable. Raises: ValueError: If at least one of x or y is provided, and at least one of input_fn or feed_fn is provided. Or if metrics is not None or dict.

tf.contrib.learn.DNNRegressor.dnn_weights_ Returns weights of deep neural network part.

tf.contrib.learn.DNNRegressor.dnn_bias_ Returns bias of deep neural network part.