e4a5dbe7e29d64c9b095d134f0ca2a5543353dda,autokeras/auto_model.py,AutoModel,predict,#AutoModel#Any#Any#,208
Before Change
A list of numpy.ndarray objects or a single numpy.ndarray.
The predicted results.
best_model , x = self._prepare_best_model_and_data(
x=x,
y=None,
batch_size=batch_size,
After Change
A list of numpy.ndarray objects or a single numpy.ndarray.
The predicted results.
preprocess_graph , model = self.tuner.get_best_model()
x = preprocess_graph.preprocess(
self._process_xy(x, None, predict=True))[0].batch(batch_size)
y = model.predict(x, **kwargs)
y = self._postprocess(y)
if isinstance(y, list) and len(y) == 1:
y = y[0]
In pattern: SUPERPATTERN
Frequency: 4
Non-data size: 6
Instances Project Name: keras-team/autokeras
Commit Name: e4a5dbe7e29d64c9b095d134f0ca2a5543353dda
Time: 2019-10-20
Author: jhfjhfj1@gmail.com
File Name: autokeras/auto_model.py
Class Name: AutoModel
Method Name: predict
Project Name: jhfjhfj1/autokeras
Commit Name: e4a5dbe7e29d64c9b095d134f0ca2a5543353dda
Time: 2019-10-20
Author: jhfjhfj1@gmail.com
File Name: autokeras/auto_model.py
Class Name: AutoModel
Method Name: predict
Project Name: jhfjhfj1/autokeras
Commit Name: e4a5dbe7e29d64c9b095d134f0ca2a5543353dda
Time: 2019-10-20
Author: jhfjhfj1@gmail.com
File Name: autokeras/auto_model.py
Class Name: AutoModel
Method Name: evaluate