Hyperparameter Tuning with Keras Tuner

Hyperparameter Tuning with Keras Tuner

• Use Keras Tuner to find the optimal hyperparameters.pythonCopy codefrom kerastuner import HyperModel, RandomSearch class MyHyperModel(HyperModel): def build(self, hp): model = Sequential() model.add(Dense(units=hp.Int('units', min_value=32, max_value=512, step=32), activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return model tuner = RandomSearch(MyHyperModel(), objective='val_accuracy', max_trials=10) tuner.search(X_train, y_train, epochs=10, validation_data=(X_val, y_val))
• Keras Tuner automates the search for optimal architecture and hyperparameters.