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2. ??????

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import syssys.path.append(r'E:\Pycharm\project\yeah&ok\load_data')  from load_data import load_data_func, test_image, augment

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data_dir = 'E:\Pycharm\project\yeah&ok\dataset'Batch_size = 32  # ?????train_dataset, test_dataset = load_data_func(data_dir, batchsize=Batch_size)test_image(train_dataset)  # ??9???

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4. ?????

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import tensorflow as tfimport matplotlib.pyplot as pltimport numpy as npimport pathlibimport randomimport tensorflow_datasets as tfds

5. ?????

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def load_data_func(data_dir, batch_size):    data_root = pathlib.Path(data_dir)    all_image_path = list(data_root.glob('*/*'))    all_image_path = [str(path) for path in all_image_path]    random.shuffle(all_image_path)        label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())    label_to_index = {name: idx for idx, name in enumerate(label_names)}    print(label_to_index)        all_image_label = [label_to_index[pathlib.Path(p).parent.name] for p in all_image_path]    print(len(all_image_label))        image_path = all_image_path[5]    image_show = (1 + load_preprocess_image(image_path)) / 2.    plt.imshow(image_show)    plt.show()        path_ds = tf.data.Dataset.from_tensor_slices(all_image_path)    image_dataset = path_ds.map(load_preprocess_image)    label_dataset = tf.data.Dataset.from_tensor_slices(all_image_label)    dataset = tf.data.Dataset.zip((image_dataset, label_dataset))        image_count = len(all_image_path)    test_count = int(image_count * 0.2)    train_count = image_count - test_count    print(test_count, train_count)        train_dataset = dataset.skip(test_count).shuffle(buffer_size=150).repeat(3).batch(batch_size)    test_dataset = dataset.take(test_count).batch(batch_size)        train_dataset = train_dataset.map(augment)    return train_dataset, test_dataset

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def load_preprocess_image(img_path):    img_raw = tf.io.read_file(img_path)    img_tensor = tf.image.decode_jpeg(img_raw, channels=3)    img_tensor = tf.image.resize(img_tensor, [160, 160])    img_tensor = tf.cast(img_tensor, tf.float32)    img = img_tensor / 127.5 - 1    return img

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def augment(image, label):    image = tf.image.random_flip_left_right(image)    image = tf.image.random_contrast(image, lower=0.0, upper=1.0)    image = tf.image.random_flip_up_down(image)    image = tf.image.random_brightness(image, max_delta=0.5)    image = tf.image.random_hue(image, max_delta=0.3)    image = tf.image.random_saturation(image, lower=0.3, upper=0.5)    return image, label

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def test_image(train_dataset):    plt.figure(figsize=(12, 12))    for batch in tfds.as_numpy(train_dataset):        for i in range(9):            image, label = (1 + batch[0][i]) / 2., batch[1][i]            plt.subplot(3, 3, i + 1)            plt.imshow(image)            plt.grid(False)        break    plt.show()