keras-tuner

import keras import keras_tuner as kt import numpy as np # Define a HyperModel class MyHyperModel(kt.HyperModel): def build(self, hp): model = keras.Sequential() model.add(keras.layers.Flatten(input_shape=(28, 28))) model.add(keras.layers.Dense(units=hp.Int('units', min_value=32, max_value=512, step=32), activation='relu')) model.add(keras.layers.Dense(10, activation='softmax')) model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])), loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model # Load dummy data (Fashion MNIST) (x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data() x_train = x_train[:10000].astype('float32') / 255.0 y_train = y_train[:10000] x_val = x_test[:2000].astype('float32') / 255.0 y_val = y_test[:2000] # Instantiate a tuner tuner = kt.RandomSearch( MyHyperModel(), objective='val_accuracy', max_trials=3, executions_per_trial=2, directory='my_dir', project_name='my_intro_to_kt' ) # Search for the best hyperparameters tuner.search(x_train, y_train, epochs=2, validation_data=(x_val, y_val)) # Get the best hyperparameters best_hps = tuner.get_best_hyperparameters(num_trials=1)[0] print(f"Best units: {best_hps.get('units')}, best learning rate: {best_hps.get('learning_rate')}") # Get the best model best_model = tuner.get_best_models(num_models=1)[0] best_model.evaluate(x_val, y_val)