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.distributions.Exponential.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.FixedLenFeature.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.

tf.contrib.distributions.Categorical.is_continuous

tf.contrib.learn.TensorFlowRNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided model_fn and training data. Note: called first time constructs the graph and initializers variables. Consecutives times it will continue training the same model. This logic follows partial_fit() interface in scikit-learn. To restart learning, create new estimator. Args: x: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of featu

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

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.name

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.loss(final_loss, name='Loss')

tf.contrib.learn.monitors.CheckpointSaver.begin(max_steps=None)