Keras调谐器:使用的层数与报告的层数不匹配 [英] Keras tuner: mismatch between number of layers used and number of layers reported

问题描述

使用Keras Tuner网站上的示例，我编写了简单的调试代码

Using example from Keras Tuner website, I wrote simple tuning code

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);

但是，当我以不同的层数运行它时，却显示报告的层数与num_layers的值不匹配.例如，它报告了三个Conv2D层，但将num_layers显示为1.为什么?

However, when I run it with varying number of layers, but it shows mismatch between number of layers reported and value of num_layers. For example it reports three Conv2D layers and yet it shows num_layers as 1. Why ?

[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

推荐答案

摘要中将显示到目前为止看到的任何超参数，这意味着一旦运行了包含三层的试验，所有后续摘要将包含三层大小.这并不意味着它使用了所有三层，这由该特定试验的num_layers: 1打印指示.

Any hyperparameter seen so far will be displayed in the summary, meaning that once a trial containing three layers has been run, all subsequent summaries will contain three layer sizes. It does not mean it uses all three layers, which is indicated by by the num_layers: 1 print for this particular trial.

有关更多详细信息，请参见omalleyt12的帖子: https://github.com/keras-team/keras-调谐器/问题/66#issuecomment-525923517

See omalleyt12's post here for more details: https://github.com/keras-team/keras-tuner/issues/66#issuecomment-525923517

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