使用tensorflow创建数据集的问题

import tensorflow as tf

# 创建一个简单的数据集
data = [1, 2, 3, 4, 5]
dataset = tf.data.Dataset.from_tensor_slices(data)

# 遍历数据集
for element in dataset:
    print(element.numpy())

import tensorflow as tf

# 从 CSV 文件中加载数据集
dataset = tf.data.experimental.make_csv_dataset(
    'path/to/your/file.csv',
    batch_size=32,
    label_name='label_column',
    num_epochs=1,
    ignore_errors=True
)

# 遍历数据集
for element in dataset:
    print(element)

import tensorflow as tf

# 创建一个 TFRecord 文件
def serialize_example(example):
    feature = {
        'data': tf.train.Feature(float_list=tf.train.FloatList(value=example['data'])),
        'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[example['label']]))
    }
    example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
    return example_proto.SerializeToString()

with tf.io.TFRecordWriter('path/to/your/file.tfrecord') as writer:
    for example in your_large_dataset:
        serialized_example = serialize_example(example)
        writer.write(serialized_example)

# 从 TFRecord 文件中加载数据集
def parse_example(serialized_example):
    feature_description = {
        'data': tf.io.FixedLenFeature([10], tf.float32),
        'label': tf.io.FixedLenFeature([], tf.int64),
    }
    parsed_example = tf.io.parse_single_example(serialized_example, feature_description)
    return parsed_example['data'], parsed_example['label']

dataset = tf.data.TFRecordDataset(['path/to/your/file.tfrecord'])
dataset = dataset.map(parse_example)

# 遍历数据集
for data, label in dataset:
    print(data.numpy(), label.numpy())