def evaluate(self, x, y=None, batch_size=32, **kwargs):
"""Evaluate the best model for the given data.
# Arguments
x: Any allowed types according to the input node. Testing data.
y: Any allowed types according to the head. Testing targets.
Defaults to None.
**kwargs: Any arguments supported by keras.Model.evaluate.
# Returns
Scalar test loss (if the model has a single output and no metrics) or
list of scalars (if the model has multiple outputs and/or metrics).
The attribute model.metrics_names will give you the display labels for
the scalar outputs.
"""
self._check_data_format((x, y))
if isinstance(x, tf.data.Dataset):
dataset = x
x = dataset.map(lambda x, y: x)
y = dataset.map(lambda x, y: y)
x = self._adapt(x, self.inputs, batch_size)
y = self._adapt(y, self._heads, batch_size)
dataset = tf.data.Dataset.zip((x, y))
pipeline = self.tuner.get_best_pipeline()
dataset = pipeline.transform(dataset)
model = self.tuner.get_best_model()
return utils.evaluate_with_adaptive_batch_size(model=model,
batch_size=batch_size,
x=dataset,
**kwargs)
def evaluate(self, x, y=None, batch_size=32, verbose=1, **kwargs):
"""Evaluate the best model for the given data.
# Arguments
x: Any allowed types according to the input node. Testing data.
y: Any allowed types according to the head. Testing targets.
Defaults to None.
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.evaluate](http://tensorflow.org/api_docs/python/tf/keras/Model#evaluate)
**kwargs: Any arguments supported by keras.Model.evaluate.
# Returns
Scalar test loss (if the model has a single output and no metrics) or
list of scalars (if the model has multiple outputs and/or metrics).
The attribute model.metrics_names will give you the display labels for
the scalar outputs.
"""
self._check_data_format((x, y))
if isinstance(x, tf.data.Dataset):
dataset = x
x = dataset.map(lambda x, y: x)
y = dataset.map(lambda x, y: y)
x = self._adapt(x, self.inputs, batch_size)
y = self._adapt(y, self._heads, batch_size)
dataset = tf.data.Dataset.zip((x, y))
pipeline = self.tuner.get_best_pipeline()
dataset = pipeline.transform(dataset)
model = self.tuner.get_best_model()
return utils.evaluate_with_adaptive_batch_size(
model=model, batch_size=batch_size, x=dataset, verbose=verbose, **kwargs
)
