clearml/examples/frameworks/kerastuner/keras_tuner_cifar.py

"""Keras Tuner CIFAR10 example for the TensorFlow blog post."""

import keras_tuner as kt
import tensorflow as tf
import tensorflow_datasets as tfds

from clearml.external.kerastuner import ClearmlTunerCallback

from clearml import Task

physical_devices = tf.config.list_physical_devices("GPU")
if physical_devices:
    tf.config.experimental.set_visible_devices(physical_devices[0], "GPU")
    tf.config.experimental.set_memory_growth(physical_devices[0], True)


def build_model(hp):
    inputs = tf.keras.Input(shape=(32, 32, 3))
    x = inputs
    for i in range(hp.Int("conv_blocks", 3, 5, default=3)):
        filters = hp.Int("filters_" + str(i), 32, 256, step=32)
        for _ in range(2):
            x = tf.keras.layers.Convolution2D(filters, kernel_size=(3, 3), padding="same")(x)
            x = tf.keras.layers.BatchNormalization()(x)
            x = tf.keras.layers.ReLU()(x)
        if hp.Choice("pooling_" + str(i), ["avg", "max"]) == "max":
            x = tf.keras.layers.MaxPool2D()(x)
        else:
            x = tf.keras.layers.AvgPool2D(pool_size=1)(x)
    x = tf.keras.layers.GlobalAvgPool2D()(x)
    x = tf.keras.layers.Dense(hp.Int("hidden_size", 30, 100, step=10, default=50), activation="relu")(x)
    x = tf.keras.layers.Dropout(hp.Float("dropout", 0, 0.5, step=0.1, default=0.5))(x)
    outputs = tf.keras.layers.Dense(10, activation="softmax")(x)

    model = tf.keras.Model(inputs, outputs)
    model.compile(
        optimizer=tf.keras.optimizers.Adam(hp.Float("learning_rate", 1e-4, 1e-2, sampling="log")),
        loss="sparse_categorical_crossentropy",
        metrics=["accuracy"],
    )
    return model


# Connecting ClearML with the current process,
# from here on everything is logged automatically
task = Task.init("examples", "kerastuner cifar10 tuning")

tuner = kt.Hyperband(
    build_model,
    project_name="kt examples",
    # logger=ClearmlTunerLogger(),
    objective="val_accuracy",
    max_epochs=10,
    hyperband_iterations=6,
)

data = tfds.load("cifar10")
train_ds, test_ds = data["train"], data["test"]


def standardize_record(record):
    return tf.cast(record["image"], tf.float32) / 255.0, record["label"]


train_ds = train_ds.map(standardize_record).cache().batch(64).shuffle(10000)
test_ds = test_ds.map(standardize_record).cache().batch(64)

tuner.search(
    train_ds,
    validation_data=test_ds,
    callbacks=[
        tf.keras.callbacks.EarlyStopping(patience=1),
        tf.keras.callbacks.TensorBoard(),
        ClearmlTunerCallback(tuner)
    ],
)

best_model = tuner.get_best_models(1)[0]
best_hyperparameters = tuner.get_best_hyperparameters(1)[0]