KerasTuner HyperModels
The HyperModel base class makes the search space better encapsulated for
sharing and reuse. A HyperModel subclass only needs to implement a
build(self, hp) method, which creates a keras.Model using the hp argument
to define the hyperparameters and returns the model instance.
A simple code example is shown as follows.
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
You can pass a HyperModel instance to the Tuner as the search space.
tuner = kt.RandomSearch(
MyHyperModel(),
objective='val_loss',
max_trials=5)
There are also some built-in HyperModel subclasses (e.g. HyperResNet,
HyperXception) for the users to directly use so that the users don't need to
write their own search spaces.
tuner = kt.RandomSearch(
HyperResNet(input_shape=(28, 28, 1), classes=10),
objective='val_loss',
max_trials=5)