tf.contrib.learn.TensorFlowRNNClassifier.fit()

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 features. The training input samples for fitting the model.

• y: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class labels in classification, real numbers in regression).

• steps: int, number of steps to train. If None or 0, train for self.steps.

• monitors: List of BaseMonitor objects to print training progress and invoke early stopping.

• logdir: the directory to save the log file that can be used for optional visualization.

Returns:

Returns self.