我正在尝试将U-net应用于CT扫描的图像分割。我的数据集包括大约8，000张训练图像和506张验证图像。我一步一步地遵循this tutorial，但我的训练时间对于一个时段来说太长了，准确地说是29个小时。我可能做错了什么？

import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import numpy as np
import cv2
from glob import glob
from sklearn.utils import shuffle
import tensorflow as tf
from tensorflow.python.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard
from tensorflow.python.keras.optimizers import Adam
from tensorflow.python.keras.metrics import Recall, Precision
from model import build_unet
from metrics import dice_loss, dice_coef, iou

H = 512
W = 512

def create_dir(path):
    """ Create a directory. """
    if not os.path.exists(path):
        os.makedirs(path)

def shuffling(x, y):
    x, y = shuffle(x, y, random_state=42)
    return x, y

def load_data(path):
    x = sorted(glob(os.path.join(path, "image", "*.jpg")))
    y = sorted(glob(os.path.join(path, "mask", "*.jpg")))
    return x, y

def read_image(path):
    path = path.decode()
    x = cv2.imread(path, cv2.IMREAD_COLOR)
    x = x/255.0
    x = x.astype(np.float32)
    return x

def read_mask(path):
    path = path.decode()
    x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    x = x/255.0
    x = x > 0.5
    x = x.astype(np.float32)
    x = np.expand_dims(x, axis=-1)
    return x

def tf_parse(x, y):
    def _parse(x, y):
        x = read_image(x)
        y = read_mask(y)
        return x, y

    x, y = tf.numpy_function(_parse, [x, y], [tf.float32, tf.float32])
    x.set_shape([H, W, 3])
    y.set_shape([H, W, 1])
    return x, y

def tf_dataset(x, y, batch=8):
    dataset = tf.data.Dataset.from_tensor_slices((x, y))
    dataset = dataset.map(tf_parse)
    dataset = dataset.batch(batch)
    dataset = dataset.prefetch(10)
    return dataset

if __name__ == "__main__":
    """ Seeding """
    np.random.seed(42)
    tf.random.set_seed(42)

    """ Directory for storing files """
    create_dir("files")

    """ Hyperparameters """
    batch_size = 16
    lr = 1e-3
    num_epochs = 5
    model_path = os.path.join("files", "model.h5")
    csv_path = os.path.join("files", "data.csv")

    """ Dataset """
    dataset_path = os.path.join("new_data")
    train_path = os.path.join(dataset_path, "train")
    valid_path = os.path.join(dataset_path, "valid")

    train_x, train_y = load_data(train_path)
    train_x, train_y = shuffling(train_x, train_y)
    valid_x, valid_y = load_data(valid_path)

    print(f"Train: {len(train_x)} - {len(train_y)}")
    print(f"Valid: {len(valid_x)} - {len(valid_y)}")

    train_dataset = tf_dataset(train_x, train_y, batch=batch_size)
    valid_dataset = tf_dataset(valid_x, valid_y, batch=batch_size)

    """ Model """
    model = build_unet((H, W, 3))
    metrics = [dice_coef, iou, Recall(), Precision()]
    model.compile(loss=dice_loss, optimizer=Adam(lr), metrics=metrics)

    callbacks = [
        ModelCheckpoint(model_path, verbose=1, save_best_only=True),
        ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=1e-7, verbose=1),
        CSVLogger(csv_path),
        TensorBoard(),
        EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=False),
    ]

    model.fit(
        train_dataset,
        epochs=num_epochs,
        validation_data=valid_dataset,
        callbacks=callbacks,
        shuffle=False
    )