tf.contrib.learn.TensorFlowRNNRegressor.get_tensor(name) Returns tensor by name. Args: name: string, name of the tensor. Returns: Tensor.

tf.contrib.learn.TensorFlowRNNRegressor.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.TensorFlowRNNRegressor.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 featur

tf.contrib.learn.TensorFlowRNNRegressor.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

tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) Evaluates given model with provided evaluation data. See superclass Estimator for more details. Args: x: features. y: targets. input_fn: Input function. feed_fn: Function creating a feed dict every time it is called. batch_size: minibatch size to use on the input. steps: Number of steps for which to evaluate model. metrics: Dict of metric op

tf.contrib.learn.TensorFlowRNNRegressor.config

tf.contrib.learn.TensorFlowRNNRegressor.bias_ Returns bias of the rnn layer.

class tf.contrib.learn.TensorFlowRNNRegressor TensorFlow RNN Regressor model.

tf.contrib.learn.TensorFlowRNNClassifier.__repr__()

tf.contrib.learn.TensorFlowRNNClassifier.__init__(rnn_size, n_classes, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, attn_length=None, attn_size=None, attn_vec_size=None, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1) Initializes a TensorFlowRNNClassifier instance. Args: rnn_size: The size for rnn cell, e.g. siz