tf.contrib.learn.Estimator.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.Estimator.config

class tf.contrib.learn.Estimator Estimator class is the basic TensorFlow model trainer/evaluator.

tf.contrib.learn.DNNRegressor.__repr__()

tf.contrib.learn.DNNRegressor.__init__(hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=None, config=None) Initializes a DNNRegressor instance. Args: hidden_units: List of hidden units per layer. All layers are fully connected. Ex. [64, 32] means first layer has 64 nodes and second one has 32. feature_columns: An iterable containing all the feature columns used by the model.

tf.contrib.learn.DNNRegressor.weights_

tf.contrib.learn.DNNRegressor.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.DNNRegressor.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 returns

tf.contrib.learn.DNNRegressor.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 want

tf.contrib.learn.DNNRegressor.model_dir