DeepLearning
DeepLearning - HyperParameter 튜닝
Hoon[]
2022. 11. 1. 15:39
from keras.layers import BatchNormalization
import keras_tuner
from tensorflow import keras
def build_model(hp):
ip = Input(shape=(X.shape[1],))
n = BatchNormalization()(ip)
n = Dense(hp.Choice('units', [8, 16, 32, 64, 128, 256]),
activation='elu')(n)
n = Dropout(0.5)(n)
n = BatchNormalization()(n)
n = Dense(hp.Choice('units', [8, 16, 32, 64, 128, 256]),
activation='elu')(n)
n = BatchNormalization()(n)
n = Dense(hp.Choice('units', [8, 16, 32, 64, 128, 256]),
activation='elu')(n)
n = Dropout(0.5)(n)
n = BatchNormalization()(n)
n = Dense(hp.Choice('units', [8, 16, 32, 64, 128, 256]),
activation='elu')(n)
n = Dense(1, activation='sigmoid')(n)
model = Model(inputs=ip, outputs=n)
model.compile(loss='mse', optimizer='adam', metrics='accuracy')
return model
tuner3 = keras_tuner.RandomSearch(
build_model, objective='val_accuracy', max_trials=5, directory='./tuner4')
tuner3.search(X, y, epochs=1500, validation_split=0.1)
best_model3 = tuner3.get_best_models()[0]
best_model3.evaluate(X_test, y_test)