用代码获取数据集fashion

获取fashion数据集需要以下步骤：

1. 从fashion数据集的官方网站下载数据集，链接：https://www.kaggle.com/zalando-research/fashionmnist

2. 安装必要的库和框架，如tensorflow、numpy、matplotlib等。

3. 使用python代码读取数据集。从Kaggle下载的数据集包含4个文件：train-images-idx3-ubyte.gz、train-labels-idx1-ubyte.gz、t10k-images-idx3-ubyte.gz、t10k-labels-idx1-ubyte.gz。数据集由训练数据和测试数据组成，每个数据包含一个28*28像素的灰度图像和标签，共10个类别。

4. 使用numpy库和gzip库将下载的数据集文件进行解压和处理。

5. 使用tensorflow库将数据集读取为tf.data.Dataset格式，以便于后续训练和验证。

代码示例：

import tensorflow as tf
import numpy as np
import gzip
import os

# 下载数据集并解压
def load_data():
    base_dir = 'data'
    if not os.path.exists(base_dir):
        os.makedirs(base_dir)
 
    urls = [
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz',
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz',
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz',
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz'
    ]
 
    for url in urls:
        response = urllib.request.urlopen(url)
        filename = os.path.join(base_dir, os.path.basename(url))
        with open(filename, 'wb') as f:
            f.write(response.read())
        print(f"{filename} downloaded")
 
    return (base_dir + '/train-images-idx3-ubyte.gz', 
            base_dir + '/train-labels-idx1-ubyte.gz',
            base_dir + '/t10k-images-idx3-ubyte.gz',
            base_dir + '/t10k-labels-idx1-ubyte.gz')

# 载入数据
def load_data(path):
    with gzip.open(path, 'rb') as f:
        data = np.frombuffer(f.read(), np.uint8, offset=16)
    return data.reshape(-1, 28, 28, 1)

# 处理数据
def preprocess_data(data):
    data = tf.cast(data, tf.float32)
    data = data / 255.0
    return data

# 读取数据
def load_dataset():
    (train_images_path, train_labels_path, test_images_path, test_labels_path) = load_data()
    train_images = load_data(train_images_path)
    train_labels = load_data(train_labels_path)
    test_images = load_data(test_images_path)
    test_labels = load_data(test_labels_path)
    
    train_images = preprocess_data(train_images)
    test_images = preprocess_data(test_images)
    
    train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))
    test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels))
    
    return train_dataset, test_dataset

# 调用函数进行读取
train_dataset, test_dataset = load_dataset()

# 检查是否成功载入
for image, label in train_dataset.take(1):
    print("Image shape: ", image.numpy().shape)
    print("Label: ", label.numpy())