keras tuner

1pip install -U keras-tuner

1from tensorflow import keras
2from tensorflow.keras import layers
3from kerastuner.tuners import RandomSearch
4
5def build_model_function(hp):
6    model = keras.Sequential()
7    model.add(layers.Dense(units=hp.Int('units', min_value=32,
8                           max_value=512, step=32), activation='relu'))
9    model.add(layers.Dense(10, activation='softmax'))
10    model.compile(optimizer=keras.optimizers.Adam(
11        hp.Choice('learning_rate',values=[1e-2, 1e-3, 1e-4])),
12        loss='sparse_categorical_crossentropy',
13        metrics=['accuracy'])
14    return model
15    
16tuner = RandomSearch(
17    build_model_function,
18    objective='val_accuracy',
19    max_trials=5,
20    executions_per_trial=3,
21    directory='my_dir',
22    project_name='helloworld')
23# You can print a summary of the search space:
24tuner.search_space_summary()
25# The call to search has the same signature as model.fit()
26tuner.search(x, y, epochs=5, validation_data=(val_x, val_y))
27# When search is over, you can retrieve the best model(s):
28models = tuner.get_best_models(num_models=2)
29# Or print a summary of the results:
30tuner.results_summary()