def build_v1(hp: kt.HyperParameters, base_feature_size: int = 0):
spectral_size = hp.Choice("spectral_size",
values=[8, 16, 32, 64],
ordered=True)
dropout_rate = hp.Float("dropout_rate", 0.0, 0.8, step=0.1)
output_units = hp.Choice("embedding_size", [8, 16, 32, 64, 128],
ordered=True)
hidden_units = hp.Choice("hidden_units",
values=[32, 64, 128, 256, 512],
ordered=True)
hidden_layers = hp.Int("hidden_layers", min_value=1, max_value=3)
spec = tf.TensorSpec(
(
None,
spectral_size + base_feature_size,
),
dtype=tf.float32,
)
model = core.sgae(
spec,
functools.partial(
mlp,
output_units=output_units,
hidden_units=(hidden_units, ) * hidden_layers,
dropout_rate=dropout_rate,
),
)
_compile(hp, model)
return model
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 Choice(
hp: kt.HyperParameters,
values: tp.Sequence,
name: str,
ordered: tp.Optional[bool] = None,
default: tp.Optional[tp.Any] = None,
parent_name: tp.Optional[str] = None,
parent_values: tp.Optional[tp.Any] = None,
):
return hp.Choice(
values=values,
name=name,
ordered=ordered,
default=default,
parent_name=parent_name,
parent_values=parent_values,
)
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
(such as the learning rate), you can do so by passing a `hyperparameters` argument
to the tuner constructor, as well as `tune_new_entries=False` to specify that parameters
that you didn't list in `hyperparameters` should not be tuned. For these parameters, the default
value gets used.
"""
from keras_tuner import HyperParameters
from keras_tuner.applications import HyperXception
hypermodel = HyperXception(input_shape=(28, 28, 1), classes=10)
hp = HyperParameters()
# This will override the `learning_rate` parameter with your
# own selection of choices
hp.Choice("learning_rate", values=[1e-2, 1e-3, 1e-4])
tuner = RandomSearch(
hypermodel,
hyperparameters=hp,
# `tune_new_entries=False` prevents unlisted parameters from being tuned
tune_new_entries=False,
objective="val_accuracy",
max_trials=3,
overwrite=True,
directory="my_dir",
project_name="helloworld",
)
tuner.search(x_train[:100],
y_train[:100],
DEFAULT_ARCH = {
"layers": [[
"LSTM", {
"units":
DEFAULT_HP.Int(name='units',
min_value=32,
max_value=128,
step=32,
default=64),
"return_sequences":
False,
"kernel_initializer":
"glorot_uniform",
"activation":
DEFAULT_HP.Choice(name='LSTM_1_activation',
values=['relu', 'tanh', 'sigmoid', "linear"],
default='relu'),
}
],
[
"Dropout", {
"rate":
DEFAULT_HP.Float(name='dropout',
min_value=0.0,
max_value=0.5,
default=0.2,
step=0.05)
}
], ["Dense", {
"activation": "linear"
}]]