107 lines
3.1 KiB
Plaintext
107 lines
3.1 KiB
Plaintext
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{
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"cells": [
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{
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"metadata": {},
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"cell_type": "raw",
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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)。",
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"id": "58ac648c45d06f50"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-05-19T04:24:31.687065Z",
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"start_time": "2024-05-19T04:24:22.281131Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import tensorflow as tf\n",
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"from d2l import tensorflow as d2l\n",
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"\n",
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"d2l.use_svg_display()"
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],
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"id": "4f19e5d16d0a7341",
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"execution_count": 1
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},
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{
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"metadata": {},
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"cell_type": "raw",
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"source": [
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"3.5.1. 读取数据集\n",
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"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。"
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],
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"id": "c67a3433075cb8ed"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "mnist_train, mnist_test = tf.keras.datasets.fashion_mnist.load_data()",
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"id": "e5defd40322a3af2"
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},
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{
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"metadata": {},
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"cell_type": "raw",
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"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。",
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"id": "9f2c0b217b6b5e30"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "len(mnist_train[0]), len(mnist_test[0])",
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"id": "6129edd7e9c7e9fc"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"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",
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"h像素、宽度w像素图像的形状记hxw或（h,w）。"
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],
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"id": "719bd9e11b3b06a9"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "mnist_train[0][0].shape",
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"id": "3ed7450fafac4f36"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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