TF2.0自定义生成器出现问题

ValueError:未在未知的TensorShape上定义as\u list()。

这是我正在使用的代码。

    csv_path = '../Dataset/ShowData.csv'
    df = pd.read_csv(csv_path)
    base_path = "../Dataset/"
    # Make one hot encoding for lables
    le = LabelEncoder()
    df['Label'] = le.fit_transform(df['Label'])
    print(df.head(1)['Label'])
    print(df.tail(1)['Label'])
    '''
    # View the labels (if you want)
    list(encoder.classes_)
    # Convert some integers into their category names
    list(encoder.inverse_transform([2, 2, 1]))
    '''

    def process_dataframe(dataframe):
        for index, row in dataframe.iterrows():
            # print(row['Path'], row['Label'])
            # Load image and get lable
            img_path = os.path.join(base_path, row['Path'])
            img = load_img(img_path, target_size=(200, 200))
            img = img_to_array(img)
            img = img/255  # normalize the image
            label = row['Label']
            #label = to_categorical(label, num_classes, dtype=tf.float32)
            yield img, label


    def generate_dataset(dataframe):
        generator = lambda: process_dataframe(dataframe)
        return tf.data.Dataset.from_generator(generator=generator,
                                          output_types= (tf.float32, tf.int32))


    dataset = generate_dataset(df)
    data_batch = dataset.shuffle(10000).batch(32)
    print(data_batch)

    model = Sequential([
        Flatten(input_shape=(200, 200, 3)),
        Dense(128, activation='relu'),
        Dense(10, activation='softmax')
    ])

    model.compile(optimizer=tf.keras.optimizers.Adam(),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(),
              metrics=[tf.keras.metrics.Accuracy()])

    model.fit(data_batch, epochs=10, verbose=1)

哦,这就是批量打印出来的数据

<DatasetV1Adapter shapes: (<unknown>, <unknown>), types: (tf.float32, tf.int32)>

这是标签类型

Name: Label, dtype: int32