The base HyperModel class
HyperModel class
keras_tuner.HyperModel(name=None, tunable=True)
Defines a search space of models.
A search space is a collection of models. The build function will build
one of the models from the space using the given HyperParameters object.
Users should subclass the HyperModel class to define their search spaces
by overriding build(), which creates and returns the Keras model.
Optionally, you may also override fit() to customize the training process
of the model.
Examples
In build(), you can create the model using the hyperparameters.
class MyHyperModel(kt.HyperModel):
def build(self, hp):
model = keras.Sequential()
model.add(keras.layers.Dense(
hp.Choice('units', [8, 16, 32]),
activation='relu'))
model.add(keras.layers.Dense(1, activation='relu'))
model.compile(loss='mse')
return model
When overriding HyperModel.fit(), if you use model.fit() to train your
model, which returns the training history, you can return it directly. You
may use hp to specify any hyperparameters to tune.
class MyHyperModel(kt.HyperModel):
def build(self, hp):
...
def fit(self, hp, model, *args, **kwargs):
return model.fit(
*args,
epochs=hp.Int("epochs", 5, 20),
**kwargs)
If you have a customized training process, you can return the objective value as a float.
If you want to keep track of more metrics, you can return a dictionary of the metrics to track.
class MyHyperModel(kt.HyperModel):
def build(self, hp):
...
def fit(self, hp, model, *args, **kwargs):
...
return {
"loss": loss,
"val_loss": val_loss,
"val_accuracy": val_accuracy
}
Arguments
- name: Optional string, the name of this HyperModel.
- tunable: Boolean, whether the hyperparameters defined in this
hypermodel should be added to search space. If
False, either the search space for these parameters must be defined in advance, or the default values will be used. Defaults to True.
build method
HyperModel.build(hp)
Builds a model.
Arguments
- hp: A
HyperParametersinstance.
Returns
A model instance.