|View source on GitHub|
Interface for objects that are trainable by, e.g.,
THIS CLASS IS DEPRECATED.
fit( x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None )
Trains a model given training data
x predictions and
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,
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,
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 -
Tensoror dictionary of string feature name to
Tensor. labels -
Tensoror dictionary of
Tensorwith labels. If input_fn is set,
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_stepsinstead. If set,
batch_size: minibatch size to use on the input, defaults to first dimension of
x. Must be
monitors: List of
BaseMonitorsubclass 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,
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.