TensorFlow构建模型（超参调优）五

本文主要讲模型训练过程中的超参数调优（如学习率、神经单元的个数等）。

模型的参数有两类：模型超参数，比如隐藏层的数量和宽度；算法超参数，比如学习率。

我们使用import keras_tuner as kt进行模型的参数调优。

准备我们需要的数据集

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

通过先构建一个模型，然后实例化Tuner并执行超参调优。

构建模型的同时定义超参数的搜索空间，我们构建可以调参的模型有两种方法：1、使用模型构建函数（如下代码）；2、定义Keras Tuner API的HyperModel类的子类。

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

  # Tune the number of units in the first Dense layer
  # Choose an optimal value between 32-512
  hp_units = hp.Int('units', min_value=32, max_value=512, step=32)
  model.add(keras.layers.Dense(units=hp_units, activation='relu'))
  model.add(keras.layers.Dense(10))

  # Tune the learning rate for the optimizer
  # Choose an optimal value from 0.01, 0.001, or 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

# 实例化Tuner，Keras有4个tuner可用：RandomSearch, Hyperband, BayesianOptimization,Sklearn。

为了能高效的进行调参，
tuner = kt.Hyperband(model_builder,
                     objective='val_accuracy',
                     max_epochs=10,
                     factor=3,
                     directory='my_dir',
                     project_name='intro_to_kt')
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')}.
""")

# Build the model with the optimal hyperparameters and train it on the data for 50 epochs
model = tuner.hypermodel.build(best_hps)
history = model.fit(img_train, label_train, epochs=50, validation_split=0.2)

val_acc_per_epoch = history.history['val_accuracy']
best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))  # Best epoch: 36

hypermodel = tuner.hypermodel.build(best_hps)

# Retrain the model
hypermodel.fit(img_train, label_train, epochs=best_epoch, validation_split=0.2)

eval_result = hypermodel.evaluate(img_test, label_test)
print("[test loss, test accuracy]:", eval_result) # [test loss, test accuracy]: [0.49477535486221313, 0.8920000195503235]

更多学习资源：

1. Keras Tuner on the TensorFlow blog
2. Keras Tuner website