def evaluate(self,
model,
x=None,
y=None,
batch_size=None,
verbose=1,
sample_weight=None,
steps=None,
callbacks=None,
**kwargs):
model._validate_or_infer_batch_size(batch_size, steps, x)
# Make sure that y, sample_weights, validation_split are not passed.
training_utils.validate_dataset_input(x, y, sample_weight)
return evaluate_generator(
model, x, steps=steps, verbose=verbose, workers=0, callbacks=callbacks)
def fit(self,
model,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_freq=1,
**kwargs):
model._validate_or_infer_batch_size(batch_size, steps_per_epoch, x)
# Make sure that y, sample_weights, validation_split are not passed.
training_utils.validate_dataset_input(x, y, sample_weight,
validation_split)
if (isinstance(x, (dataset_ops.DatasetV1, dataset_ops.DatasetV2))
and shuffle):
training_utils.verify_dataset_shuffled(x)
return fit_generator(model,
x,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
validation_data=validation_data,
validation_steps=validation_steps,
validation_freq=validation_freq,
class_weight=class_weight,
workers=0,
shuffle=shuffle,
initial_epoch=initial_epoch,
steps_name='steps_per_epoch')