使用 Keras Tuner 帮助模型超参数调优

you will use the Keras Tuner to find the best hyperparameters for a machine learning model

import tensorflow as tf
from tensorflow import kerasimport 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

Define the model

When you build a model for hypertuning, you also define the hyperparameter search space in addition to the model architecture. The model you set up for hypertuning is called a hypermodel.

You can define a hypermodel through two approaches:

• By using a model builder function
• By subclassing the HyperModel class of the Keras Tuner API

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 32-512hp_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 0.0001hp_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

Instantiate the tuner and perform hypertuning

Instantiate the tuner to perform the hypertuning. The Keras Tuner has four tuners available - RandomSearch, Hyperband, BayesianOptimization, and Sklearn.
To instantiate the Hyperband tuner, you must specify the hypermodel, the objective to optimize and the maximum number of epochs to train (max_epochs).

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')}.
""")

Find the optimal number of epochs to train the model with the hyperparameters obtained from the search.

# 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,))