def predict(self, x, batch_size=32, verbose=1, **kwargs):
"""Predict the output for a given testing data.
# Arguments
x: Any allowed types according to the input node. Testing data.
batch_size: Number of samples per batch.
If unspecified, batch_size will default to 32.
verbose: Verbosity mode. 0 = silent, 1 = progress bar.
Controls the verbosity of
[keras.Model.predict](https://tensorflow.org/api_docs/python/tf/keras/Model#predict)
**kwargs: Any arguments supported by keras.Model.predict.
# Returns
A list of numpy.ndarray objects or a single numpy.ndarray.
The predicted results.
"""
if isinstance(x, tf.data.Dataset) and self._has_y(x):
x = x.map(lambda x, y: x)
self._check_data_format((x, None), predict=True)
dataset = self._adapt(x, self.inputs, batch_size)
pipeline = self.tuner.get_best_pipeline()
model = self.tuner.get_best_model()
dataset = pipeline.transform_x(dataset)
dataset = tf.data.Dataset.zip((dataset, dataset))
y = model.predict(dataset, **kwargs)
y = utils.predict_with_adaptive_batch_size(model=model,
batch_size=batch_size,
x=dataset,
verbose=verbose,
**kwargs)
return pipeline.postprocess(y)
def predict(self, x, batch_size=32, **kwargs):
"""Predict the output for a given testing data.
# Arguments
x: Any allowed types according to the input node. Testing data.
**kwargs: Any arguments supported by keras.Model.predict.
# Returns
A list of numpy.ndarray objects or a single numpy.ndarray.
The predicted results.
"""
if isinstance(x, tf.data.Dataset):
if self._has_y(x):
x = x.map(lambda x, y: x)
self._check_data_format((x, None), predict=True)
dataset = self._adapt(x, self.inputs, batch_size)
pipeline = self.tuner.get_best_pipeline()
model = self.tuner.get_best_model()
dataset = pipeline.transform_x(dataset)
y = model.predict(dataset, **kwargs)
y = utils.predict_with_adaptive_batch_size(model=model,
batch_size=batch_size,
x=dataset,
**kwargs)
return pipeline.postprocess(y)