使用Keras tuner调整超参数

什么是Keras Tuner？

• Keras Tuner 是为解决寻找最优超参数问题而设计的简单易用的，可扩展的超参数优化框架

如何安装Keras Tuner？
Python≥3.6, TensorFlow ≥2.0, 安装命令：

pip install keras-tuner --upgrade

如何使用Keras Tuner？范例如下：

import tensorflow as tf 
import keras_tuner as kt 
from tensorflow import keras


(img_train, label_train), (img_test, label_test) = keras.datasets.fashion_mnist.load_data()
# Normalize pixel values between 0 and 1
img_train = img_train.astype('float32') / 255.0
img_test = img_test.astype('float32') / 255.0

def model_builder(hp):
    model = keras.Sequential()
    model.add(keras.layers.Flatten(input_shape=(28,28)))

    # 调整第一个Dense层的神经元个数
    # 个数选择32--512
    hp_units = hp.Int('units', min_value=32, max_value=512, step=8)
    model.add(keras.layers.Dense(units=hp_units, activation='relu'))
    model.add(keras.layers.Dense(10))

    # Tune优化器的learning rate
    # 优化参数为：0.01, 0.001，或0.0001
    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])

    model.compile(
        optimizer = keras.optimizers.Adam(learning_rate=hp_learning_rate),
        loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=['accuracy']
    )

    return model 

tuner = kt.Hyperband(
    model_builder,
    objective='val_accuracy',
    max_epochs=10,
    factor=3,
    directory='my_dir',
    project_name='kt_demo'
)

stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

tuner.search(img_train, label_train, epochs=50, validation_split=0.2, callbacks=[stop_early])

# Get the optimal hyperparameters
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]

print(f"""
The hyperparameter search is complete. The optimal number of units in the first densely-connected
layer is {best_hps.get('units')} and the optimal learning rate for the optimizer
is {best_hps.get('learning_rate')}.
""")

运行结果： Keras-tuner找到了最佳参数