{
 "cells": [
  {
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   "source": "MNIST数据集 (LeCun et al., 1998) 是图像分类中广泛使用的数据集之一，但作为基准数据集过于简单。 我们将使用类似但更复杂的Fashion-MNIST数据集 (Xiao et al., 2017)。",
   "id": "58ac648c45d06f50"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-19T04:24:31.687065Z",
     "start_time": "2024-05-19T04:24:22.281131Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import tensorflow as tf\n",
    "from d2l import tensorflow as d2l\n",
    "\n",
    "d2l.use_svg_display()"
   ],
   "id": "4f19e5d16d0a7341",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-19 12:24:22.318857: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "raw",
   "source": [
    "3.5.1. 读取数据集\n",
    "我们可以通过框架中的内置函数将Fashion-MNIST数据集下载并读取到内存中。"
   ],
   "id": "c67a3433075cb8ed"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "mnist_train, mnist_test = tf.keras.datasets.fashion_mnist.load_data()",
   "id": "e5defd40322a3af2"
  },
  {
   "metadata": {},
   "cell_type": "raw",
   "source": "Fashion-MNIST由10个类别的图像组成， 每个类别由训练数据集（train dataset）中的6000张图像 和测试数据集（test dataset）中的1000张图像组成。 因此，训练集和测试集分别包含60000和10000张图像。 测试数据集不会用于训练，只用于评估模型性能。",
   "id": "9f2c0b217b6b5e30"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "len(mnist_train[0]), len(mnist_test[0])",
   "id": "6129edd7e9c7e9fc"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "每个输入图像的高度和宽度均为28像素。 数据集由灰度图像组成，其通道数为1。 为了简洁起见，本书将高度\n",
    "h像素、宽度w像素图像的形状记hxw或（h,w）。"
   ],
   "id": "719bd9e11b3b06a9"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "mnist_train[0][0].shape",
   "id": "3ed7450fafac4f36"
  }
 ],
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