tf.contrib.learn.LinearRegressor.weights_

tf.contrib.learn.LinearRegressor.predict(*args, **kwargs) Returns predictions for given features. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below. Args: x: Matrix of shape [n_samples, n_features...]. Can be iterator that retur

tf.contrib.learn.LinearRegressor.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

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

tf.contrib.learn.LinearRegressor.model_dir

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

tf.contrib.learn.LinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None) Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training. This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you wa

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

tf.contrib.learn.LinearRegressor.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.LinearRegressor.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.