I am experimenting with kerastuner.
Here is my code with a reproducible example:
import kerastuner as kt
from kerastuner.tuners.bayesian import BayesianOptimization
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data(
path="boston_housing.npz", test_split=0.2, seed=113
)
params = {}
params['shape'] = x_train[0].shape
def build_model(hp):
number_of_layers = hp.Choice('number_of_layers', values = [2, 3], default = 2)
if number_of_layers == 2:
nodes2 = hp.Int('nodes2', 64, 524 , 64, default = 64)
nodes3 = hp.Int('nodes3', 32, np.min([nodes2//2, 128]) , 32, default = 32)
nodes_list = [nodes2, nodes3]
dropout2 = hp.Float('dropout2', 0., 0.2, 0.05, default = 0.)
dropout3 = hp.Float('dropout3', 0.2, 0.5, 0.05, default = 0.5)
dropouts_list = [dropout2, dropout3]
else:
nodes1 = hp.Int('nodes1', 128, 1024, 128, default = 128)
nodes2 = hp.Int('nodes2', 64, np.min([nodes1//2, 524]) , 64, default = 64)
nodes3 = hp.Int('nodes3', 32, np.min([nodes2//2, 128]) , 32, default = 32)
nodes_list = [nodes1, nodes2, nodes3]
dropout1 = hp.Float('dropout1', 0., 0.2, 0.05, default = 0.)
dropout2 = hp.Float('dropout2', 0., 0.2, 0.05, default = 0.)
dropout3 = hp.Float('dropout3', 0.2, 0.5, 0.05, default = 0.5)
dropouts_list = [dropout1, dropout2, dropout3]
inputs = Input(shape = params['shape'])
x = inputs
for i in range(len(nodes_list)):
nodes = nodes_list[i]
dropout = dropouts_list[i]
x = Dense(nodes, activation = 'relu')(x)
x = Dropout(dropout)(x)
prediction = Dense(1)(x)
model = Model(inputs, prediction)
model.compile(
optimizer = tf.keras.optimizers.Adam(hp.Float('learning_rate', 1e-4, 1e-2, sampling = 'log')),
loss = 'mse'
)
return(model)
tuner = BayesianOptimization(
build_model,
objective='val_loss',
max_trials = 100)
tuner.search(x_train, y_train, validation_split = 0.2, callbacks = [tf.keras.callbacks.EarlyStopping(patience = 10)] )
INFO:tensorflow:Reloading Oracle from existing project .\untitled_project\oracle.json
INFO:tensorflow:Reloading Tuner from .\untitled_project\tuner0.json
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
<ipython-input-120-3bfac2133c4d> in <module>
68 max_trials = 100)
69
---> 70 tuner.search(x_train, y_train, validation_split = 0.2, callbacks = [tf.keras.callbacks.EarlyStopping(patience = 10)] )
~\Anaconda3\envs\tf2\lib\site-packages\kerastuner\engine\base_tuner.py in search(self, *fit_args, **fit_kwargs)
118 self.on_search_begin()
119 while True:
--> 120 trial = self.oracle.create_trial(self.tuner_id)
121 if trial.status == trial_module.TrialStatus.STOPPED:
122 # Oracle triggered exit.
~\Anaconda3\envs\tf2\lib\site-packages\kerastuner\engine\oracle.py in create_trial(self, tuner_id)
147 values = None
148 else:
--> 149 response = self._populate_space(trial_id)
150 status = response['status']
151 values = response['values'] if 'values' in response else None
~\Anaconda3\envs\tf2\lib\site-packages\kerastuner\tuners\bayesian.py in _populate_space(self, trial_id)
99
100 # Fit a GPR to the completed trials and return the predicted optimum values.
--> 101 x, y = self._vectorize_trials()
102 try:
103 self.gpr.fit(x, y)
~\Anaconda3\envs\tf2\lib\site-packages\kerastuner\tuners\bayesian.py in _vectorize_trials(self)
204
205 # Embed an HP value into the continuous space [0, 1].
--> 206 prob = hp_module.value_to_cumulative_prob(trial_value, hp)
207 vector.append(prob)
208
~\Anaconda3\envs\tf2\lib\site-packages\kerastuner\engine\hyperparameters.py in value_to_cumulative_prob(value, hp)
1044 sampling = hp.sampling or 'linear'
1045 if sampling == 'linear':
-> 1046 return (value - hp.min_value) / (hp.max_value - hp.min_value)
1047 elif sampling == 'log':
1048 return (math.log(value / hp.min_value) /
ZeroDivisionError: division by zero