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Interface for objects that are trainable by, e.g., Experiment.




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Trains a model given training data x predictions and y labels.

x Matrix of shape [n_samples, n_features...] or the dictionary of Matrices. Can be iterator that returns arrays of features or dictionary of arrays of features. The training input samples for fitting the model. If set, input_fn must be None.
y Vector or matrix [n_samples] or [n_samples, n_outputs] or the dictionary of same. Can be iterator that returns array of labels or dictionary of array of labels. The training label values (class labels in classification, real numbers in regression). If set, input_fn must be None. Note: For classification, label values must be integers representing the class index (i.e. values from 0 to n_classes-1).
input_fn Input function returning a tuple of: features - Tensor or dictionary of string feature name to Tensor. labels - Tensor or dictionary of Tensor with labels. If input_fn is set, x, y, and batch_size must be None.
steps Number of steps for which to train model. If None, train forever. 'steps' works incrementally. If you call two times fit(steps=10) then training occurs in total 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.
batch_size minibatch size to use on the input, defaults to first dimension of x. Must be None if input_fn is provided.
monitors List of BaseMonitor subclass instances. Used for callbacks inside the training loop.
max_steps Number of total steps for which to train model. If None, train forever. If set, steps must be None.

Two calls to fit(steps=100) means 200 training iterations. On the other hand, two calls to fit(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.

self, for chaining.