TensorFlow2-模型子类化ValueError

我正在尝试使用TensorFlow 2的模型子分类尝试创建LeNet-300-100密集神经网络。我拥有的代码如下：

batch_size = 32
num_epochs = 20


# Load MNIST dataset-
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0

# Convert class vectors/target to binary class matrices or one-hot encoded values-
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)

X_train.shape, y_train.shape
# ((60000, 28, 28), (60000, 10))

X_test.shape, y_test.shape
# ((10000, 28, 28), (10000, 10)) 




class LeNet300(Model):
    def __init__(self, **kwargs):
        super(LeNet300, self).__init__(**kwargs)
        
        self.flatten = Flatten()
        self.dense1 = Dense(units = 300, activation = 'relu')
        self.dense2 = Dense(units = 100, activation = 'relu')
        self.op = Dense(units = 10, activation = 'softmax')

    def call(self, inputs):
        x = self.flatten(inputs)
        x = self.dense1(x)
        x = self.dense2(x)
        return self.op(x)




# Instantiate an object using LeNet-300-100 dense model-
model = LeNet300()

# Compile the defined model-
model.compile(
        optimizer=tf.keras.optimizers.Adam(),
        loss=tf.keras.losses.SparseCategoricalCrossentropy(),
        metrics=['accuracy']
        )


# Define early stopping callback-
early_stopping_callback = tf.keras.callbacks.EarlyStopping(
        monitor = 'val_loss', min_delta = 0.001,
        patience = 3)

# Train defined and compiled model-
history = model.fit(
    x = X_train, y = y_train,
    batch_size = batch_size, shuffle = True,
    epochs = num_epochs,
    callbacks = [early_stopping_callback],
    validation_data = (X_test, y_test)
    )

调用“ model.fit（）”时，出现以下错误：

ValueError：形状不匹配：标签的形状（接收到（320，））应等于对数的形状，但最后一个尺寸（接收到（32，10））除外。

怎么了

谢谢