def Fixed(
hp: kt.HyperParameters,
value,
parent_name: tp.Optional[str] = None,
parent_values: tp.Optional[tp.Any] = None,
):
return hp.Fixed(value=value,
parent_name=parent_name,
parent_values=parent_values)
def get_tuner(opDir, method, max_trials):
hp = HyperParameters()
hp.Int('n_sp_hidden_lyrs', min_value=1, max_value=3, step=1)
hp.Choice('sp_hidden_nodes', [16, 32, 64, 128])
hp.Int('n_mu_hidden_lyrs', min_value=1, max_value=3, step=1)
hp.Choice('mu_hidden_nodes', [16, 32, 64, 128])
hp.Int('n_smr_hidden_lyrs', min_value=1, max_value=3, step=1)
hp.Choice('smr_hidden_nodes', [16, 32, 64, 128])
hp.Choice('classification_loss_sp', ['binary_crossentropy', 'hinge'])
hp.Choice('classification_loss_mu', ['binary_crossentropy', 'hinge'])
# loss_weights = []
# alpha = np.arange(0.1,0.8,0.1)
# beta = 1-alpha
# for i in range(len(beta)):
# gamma = np.arange(0.1, beta[i]-0.1, 0.1)
# for j in range(len(gamma)):
# beta_i = beta[i] - gamma[j]
# loss_weights.append([alpha[i], beta_i, gamma[j]])
# loss_weights = np.round(loss_weights,1).tolist()
# hp.Choice('loss_weights', loss_weights)
hp.Fixed('TR_STEPS', PARAMS['TR_STEPS'])
misc.print_model_summary(opDir + '/model_summary.txt',
get_Lemaire_MTL_model(hp))
if method == 'RandomSearch':
tuner = RandomSearch(
get_Lemaire_MTL_model,
hyperparameters=hp,
objective='val_loss',
max_trials=max_trials,
executions_per_trial=2,
overwrite=False,
directory=opDir,
project_name='B3_MTL_architecture_tuning_non_causal',
tune_new_entries=True,
allow_new_entries=True,
)
elif method == 'BayesianOptimization':
tuner = BayesianOptimization(
get_Lemaire_MTL_model,
hyperparameters=hp,
objective='val_loss',
max_trials=max_trials,
executions_per_trial=2,
overwrite=False,
directory=opDir,
project_name='B3_MTL_architecture_tuning_non_causal',
tune_new_entries=True,
allow_new_entries=True,
)
return tuner
def get_tuner(opDir, method, max_trials):
hp = HyperParameters()
hp.Int('kernel_size', min_value=3, max_value=19, step=2) # 9
hp.Int('Nd', min_value=3, max_value=8, step=1) # 6
hp.Int('nb_stacks', min_value=3, max_value=10, step=1) # 8
hp.Int('n_layers', min_value=1, max_value=4, step=1) # 4
hp.Choice('n_filters', [8, 16, 32]) # 3
hp.Boolean('skip_some_connections') # 2
hp.Fixed('TR_STEPS', PARAMS['TR_STEPS'])
if method == 'RandomSearch':
tuner = RandomSearch(
get_Lemaire_model,
hyperparameters=hp,
objective='val_loss',
max_trials=max_trials,
executions_per_trial=2,
overwrite=False,
directory=opDir,
project_name='B3_architecture_tuning_non_causal',
tune_new_entries=True,
allow_new_entries=True,
)
elif method == 'BayesianOptimization':
tuner = BayesianOptimization(
get_Lemaire_model,
hyperparameters=hp,
objective='val_loss',
max_trials=max_trials,
executions_per_trial=2,
overwrite=False,
directory=opDir,
project_name='B3_architecture_tuning_non_causal',
tune_new_entries=True,
allow_new_entries=True,
)
return tuner
hp.Int("units", min_value=32, max_value=512, step=32, default=128)
```
If you don't, hyperparameters always have a default default (for `Int`, it is equal to `min_value`).
## Fixing values in a hypermodel
What if you want to do the reverse -- tune all available parameters in a hypermodel, **except** one (the learning rate)?
Pass a `hyperparameters` argument with a `Fixed` entry (or any number of `Fixed` entries), and specify `tune_new_entries=True`.
"""
hypermodel = HyperXception(input_shape=(28, 28, 1), classes=10)
hp = HyperParameters()
hp.Fixed("learning_rate", value=1e-4)
tuner = RandomSearch(
hypermodel,
hyperparameters=hp,
tune_new_entries=True,
objective="val_accuracy",
max_trials=3,
overwrite=True,
directory="my_dir",
project_name="helloworld",
)
tuner.search(x_train[:100],
y_train[:100],
epochs=1,