Keras调谐器:使用的层数和报告的层数不匹配

Question

使用Keras Tuner网站上的示例，我编写了简单的调优代码

base_model = tf.keras.applications.vgg16.VGG16(input_shape=IMG_SHAPE,
include_top=False, 
weights='imagenet')
base_model.trainable = False
def build_model(hp):
model = tf.keras.Sequential();
model.add(base_model);
for i in range(hp.Int('num_layers', 1, 2)):
model.add(tf.keras.layers.Conv2D(filters=hp.Int('Conv2D_' + str(i),
min_value=32,
max_value=512,
step=32),
kernel_size=3, activation='relu'));
model.add(tf.keras.layers.Dropout(hp.Choice('rate', [0.3, 0.5])));
model.add(tf.keras.layers.GlobalAveragePooling2D());
model.add(tf.keras.layers.Flatten());
model.add(tf.keras.layers.Dropout(0.2));
model.add(tf.keras.layers.Dense(5, activation='softmax'));
model.compile(optimizer=tf.keras.optimizers.RMSprop(hp.Choice('learning_rate', [1e-4, 1e-5])),
loss='categorical_crossentropy',
metrics=['accuracy']);
return model

epochs = 2
callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
tuner = RandomSearch(
build_model,
objective='val_accuracy',
max_trials=24,
executions_per_trial=1,
directory=LOG_DIR);
tuner.search_space_summary();
tuner.search(train_generator,
callbacks=[callback],
epochs = epochs,
steps_per_epoch = train_generator.samples // BATCH_SIZE,
validation_data = valid_generator,
validation_steps = valid_generator.samples // BATCH_SIZE,
verbose = 1);
tuner.results_summary();
models = tuner.get_best_models(num_models=2);

然而，当我在不同层数的情况下运行它时，它显示报告的层数和num_layers的值不匹配。例如，它报告了三个Conv2D层，但它将num_layers显示为1。为什么？

[Trial summary]
|-Trial ID: 79cd7bb6146b4c243eb2bc51f19985de
|-Score: 0.8444444537162781
|-Best step: 0
> Hyperparameters:
|-Conv2D_0: 448
|-Conv2D_1: 448
|-Conv2D_2: 512
|-learning_rate: 0.0001
|-num_layers: 1
|-rate: 0.5

Answer 1

到目前为止看到的任何超参数都将显示在摘要中，这意味着一旦运行了包含三层的试验，所有后续摘要都将包含三层大小。这并不意味着它使用了所有三层，这一点在本次特定试验中由num_layers: 1打印表示。

有关更多详细信息，请参阅omalleyt12的帖子：https://github.com/keras-team/keras-tuner/issues/66#issuecomment-525923517