AttributeError：'SparseCategoricalCrossentropy'对象没有属性'__name__'

我在基本的CNN中遇到此错误：

AttributeError：'SparseCategoricalCrossentropy'对象没有属性' name '

我以为你可以loss=tf.metrics.SparseCategoricalCrossentropy()进入model.compile()？

import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
import matplotlib.pyplot as plt
tf.random.set_seed(42)

train, test = tfds.load('fashion_mnist', split=['train', 'test'], as_supervised=True)

train = train.map(lambda x, y: (tf.divide(x, 255), y)).batch(8)
test = test.map(lambda x, y: (tf.divide(x, 255), y)).batch(8)

custom_model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
    tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')])

custom_model.compile(loss=tf.metrics.SparseCategoricalCrossentropy(),
                     optimizer=tf.optimizers.Adam(),
                     metrics=[tf.metrics.SparseCategoricalAccuracy()])

early_stopping = [tf.keras.callbacks.EarlyStopping(patience=5)]

conv_dropout_history = custom_model.fit(train, validation_data=test,
                                        epochs=100, callbacks=early_stopping)

有什么方法可以使用此对象，model.compile()还是我应该只使用tf.metrics.sparse_categorical_crossentropy（或字符串形式）？