Tensorflow.js 多分类，机器学习区分企鹅种类

2023-05-15
广东
本文字数：10042 字
阅读完需：约 33 分钟

前言：

在规则编码中，我们常常会遇到需要通过多种区间判断某种物品分类。比如二手物品的定价，尽管不是新品没有 SKU 但是基本的参数是少不了。想通过成色来区分某种物品，其实主要是确定一些参数。然后根据参数数据以及参数对应成色的所有数据集归档用机器学习训练，这样机器就可以得出规则了。

机器制定规则后，我们后面再给相应参数，他就能对成色进行分类了。以上只是打了个比方加之没有二手商品相关的数据集，所以就找了一个企鹅品种数据集，大家可以在网上搜索 “帕尔默企鹅数据集” 就可以下载了。以下内容还是实战类，偏原理的可能后期补上。

数据集介绍：

\1. 背景描述

由 Kristen Gorman 博士和南极洲 LTER 的帕尔默科考站共同创建，包含 344 只企鹅的数据。

\2. 数据说明

species: 三个企鹅种类：阿德利 (Adelie) 巴布亚 (Gentoo) 帽带 (Chinstrap)

culmen_length_mm: 鸟的嘴峰长度

culmen_depth_mm: 鸟的嘴峰深度

flipper_length_mm: 脚掌长度

body_mass_g: 体重

island: 岛屿的名字

sex: 企鹅的性别

数据处理：

tensorflow.js 在进行训练前，都需要对原先的数据集进行 tensor 格式转换，为了训练质量，数据集的数值最好控制在 0 到 1 之间，所以必要时候还要对转换的 tensor 进行归一化处理。对于新手而言，这里的处理方式看个人，我就用 js 方式进行的处理。因为 "帕尔默企鹅数据集" 是 csv，我就用 js 原始的方法进行了数据转化。

数组索引 0 是企鹅种类 (0. 阿德利，1. 帽带 2. 巴布亚), 索引 1 岛屿 (0.Torgersen 1.Biscoe 2.Dream), 索引 2，索引 3，索引 4，索引 5 分别是企鹅嘴峰长度，企鹅嘴峰深度，脚掌长度，体重，性别。以上数据是长度的单位都是毫米，体重的单位都是克，所以数值比较大，转化数据如下。

const IRIS_DATA = [    [0,0,3.91,1.8699999999999999,1.81,3.75,0],    [0,0,3.95,1.7399999999999998,1.86,3.8,1],    [0,0,4.029999999999999,1.8,1.95,3.25,1],    [0,0,3.6700000000000004,1.9300000000000002,1.93,3.45,1],    [0,0,3.9299999999999997,2.06,1.9,3.65,0],    [0,0,3.8899999999999997,1.78,1.81,3.625,1],    [0,0,3.9200000000000004,1.9600000000000002,1.95,4.675,0],    [0,0,4.11,1.7600000000000002,1.82,3.2,1],    [0,0,3.8600000000000003,2.12,1.91,3.8,0],    [0,0,3.46,2.1100000000000003,1.98,4.4,0],    [0,0,3.66,1.78,1.85,3.7,1],    [0,0,3.87,1.9,1.95,3.45,1],    [0,0,4.25,2.07,1.97,4.5,0],    [0,0,3.44,1.8399999999999999,1.84,3.325,1],    [0,0,4.6,2.15,1.94,4.2,0],    [0,1,3.78,1.83,1.74,3.4,1],    [0,1,3.7700000000000005,1.8699999999999999,1.8,3.6,0],    [0,1,3.59,1.92,1.89,3.8,1],    [0,1,3.8200000000000003,1.81,1.85,3.95,0],    [0,1,3.88,1.72,1.8,3.8,0],    [0,1,3.53,1.89,1.87,3.8,1],    [0,1,4.0600000000000005,1.86,1.83,3.55,0],    [0,1,4.05,1.7899999999999998,1.87,3.2,1],    [0,1,3.79,1.86,1.72,3.15,1],    [0,1,4.05,1.89,1.8,3.95,0],    [0,2,3.95,1.67,1.78,3.25,1],    [0,2,3.72,1.81,1.78,3.9,0],    [0,2,3.95,1.78,1.88,3.3,1],    [0,2,4.09,1.89,1.84,3.9,0],    [0,2,3.6399999999999997,1.7,1.95,3.325,1],    [0,2,3.9200000000000004,2.1100000000000003,1.96,4.15,0],    [0,2,3.88,2,1.9,3.95,0],    [0,2,4.220000000000001,1.85,1.8,3.55,1],    [0,2,3.7600000000000002,1.9300000000000002,1.81,3.3,1],    [0,2,3.9799999999999995,1.9100000000000001,1.84,4.65,0],    [0,2,3.65,1.8,1.82,3.15,1],    [0,2,4.08,1.8399999999999999,1.95,3.9,0],    [0,2,3.6,1.85,1.86,3.1,1],    [0,2,4.41,1.97,1.96,4.4,0],    [0,2,3.7,1.69,1.85,3,1],    [0,2,3.96,1.8800000000000001,1.9,4.6,0],    [0,2,4.11,1.9,1.82,3.425,0],    [0,2,3.6,1.7899999999999998,1.9,3.45,1],    [0,2,4.2299999999999995,2.12,1.91,4.15,0],    [0,1,3.96,1.77,1.86,3.5,1],    [0,1,4.01,1.89,1.88,4.3,0],    [0,1,3.5,1.7899999999999998,1.9,3.45,1],    [0,1,4.2,1.95,2,4.05,0],    [0,1,3.45,1.81,1.87,2.9,1],    [0,1,4.14,1.86,1.91,3.7,0],    [0,1,3.9,1.75,1.86,3.55,1],    [0,1,4.0600000000000005,1.8800000000000001,1.93,3.8,0],    [0,1,3.65,1.6600000000000001,1.81,2.85,1],    [0,1,3.7600000000000002,1.9100000000000001,1.94,3.75,0],    [0,1,3.5700000000000003,1.69,1.85,3.15,1],    [0,1,4.13,2.1100000000000003,1.95,4.4,0],    [0,1,3.7600000000000002,1.7,1.85,3.6,1],    [0,1,4.11,1.8199999999999998,1.92,4.05,0],    [0,1,3.6399999999999997,1.7100000000000002,1.84,2.85,1],    [0,1,4.16,1.8,1.92,3.95,0],    [0,1,3.55,1.6199999999999999,1.95,3.35,1],    [0,1,4.11,1.9100000000000001,1.88,4.1,0],    [0,0,3.59,1.6600000000000001,1.9,3.05,1],    [0,0,4.18,1.94,1.98,4.45,0],    [0,0,3.35,1.9,1.9,3.6,1],    [0,0,3.97,1.8399999999999999,1.9,3.9,0],    [0,0,3.96,1.72,1.96,3.55,1],    [0,0,4.58,1.89,1.97,4.15,0],    [0,0,3.55,1.75,1.9,3.7,1],    [0,0,4.279999999999999,1.85,1.95,4.25,0],    [0,0,4.09,1.6800000000000002,1.91,3.7,1],    [0,0,3.72,1.94,1.84,3.9,0],    [0,0,3.62,1.61,1.87,3.55,1],    [0,0,4.21,1.9100000000000001,1.95,4,0],    [0,0,3.46,1.72,1.89,3.2,1],    [0,0,4.29,1.7600000000000002,1.96,4.7,0],    [0,0,3.6700000000000004,1.8800000000000001,1.87,3.8,1],    [0,0,3.5100000000000002,1.94,1.93,4.2,0],    [0,2,3.7299999999999995,1.78,1.91,3.35,1],    [0,2,4.13,2.0300000000000002,1.94,3.55,0],    [0,2,3.63,1.95,1.9,3.8,0],    [0,2,3.69,1.86,1.89,3.5,1],    [0,2,3.8299999999999996,1.92,1.89,3.95,0],    [0,2,3.8899999999999997,1.8800000000000001,1.9,3.6,1],    [0,2,3.5700000000000003,1.8,2.02,3.55,1],    [0,2,4.11,1.81,2.05,4.3,0],    [0,2,3.4,1.7100000000000002,1.85,3.4,1],    [0,2,3.96,1.81,1.86,4.45,0],    [0,2,3.62,1.73,1.87,3.3,1],    [0,2,4.08,1.89,2.08,4.3,0],    [0,2,3.81,1.86,1.9,3.7,1],    [0,2,4.029999999999999,1.85,1.96,4.35,0],    [0,2,3.31,1.61,1.78,2.9,1],    [0,2,4.32,1.85,1.92,4.1,0],    [0,1,3.5,1.7899999999999998,1.92,3.725,1],    [0,1,4.1,2,2.03,4.725,0],    [0,1,3.7700000000000005,1.6,1.83,3.075,1],    [0,1,3.78,2,1.9,4.25,0],    [0,1,3.79,1.86,1.93,2.925,1],    [0,1,3.97,1.89,1.84,3.55,0],    [0,1,3.8600000000000003,1.72,1.99,3.75,1],    [0,1,3.8200000000000003,2,1.9,3.9,0],    [0,1,3.81,1.7,1.81,3.175,1],    [0,1,4.32,1.9,1.97,4.775,0],    [0,1,3.81,1.65,1.98,3.825,1],    [0,1,4.5600000000000005,2.0300000000000002,1.91,4.6,0],    [0,1,3.97,1.77,1.93,3.2,1],    [0,1,4.220000000000001,1.95,1.97,4.275,0],    [0,1,3.96,2.07,1.91,3.9,1],    [0,1,4.2700000000000005,1.83,1.96,4.075,0],    [0,0,3.8600000000000003,1.7,1.88,2.9,1],    [0,0,3.7299999999999995,2.05,1.99,3.775,0],    [0,0,3.5700000000000003,1.7,1.89,3.35,1],    [0,0,4.11,1.86,1.89,3.325,0],    [0,0,3.62,1.72,1.87,3.15,1],    [0,0,3.7700000000000005,1.98,1.98,3.5,0],    [0,0,4.0200000000000005,1.7,1.76,3.45,1],    [0,0,4.14,1.85,2.02,3.875,0],    [0,0,3.5200000000000005,1.59,1.86,3.05,1],    [0,0,4.0600000000000005,1.9,1.99,4,0],    [0,0,3.88,1.7600000000000002,1.91,3.275,1],    [0,0,4.15,1.83,1.95,4.3,0],    [0,0,3.9,1.7100000000000002,1.91,3.05,1],    [0,0,4.41,1.8,2.1,4,0],    [0,0,3.85,1.7899999999999998,1.9,3.325,1],    [0,0,4.3100000000000005,1.92,1.97,3.5,0],    [0,2,3.6799999999999997,1.85,1.93,3.5,1],    [0,2,3.75,1.85,1.99,4.475,0],    [0,2,3.81,1.7600000000000002,1.87,3.425,1],    [0,2,4.11,1.75,1.9,3.9,0],    [0,2,3.56,1.75,1.91,3.175,1],    [0,2,4.0200000000000005,2.0100000000000002,2,3.975,0],    [0,2,3.7,1.65,1.85,3.4,1],    [0,2,3.97,1.7899999999999998,1.93,4.25,0],    [0,2,4.0200000000000005,1.7100000000000002,1.93,3.4,1],    [0,2,4.0600000000000005,1.72,1.87,3.475,0],    [0,2,3.21,1.55,1.88,3.05,1],    [0,2,4.07,1.7,1.9,3.725,0],    [0,2,3.7299999999999995,1.6800000000000002,1.92,3,1],    [0,2,3.9,1.8699999999999999,1.85,3.65,0],    [0,2,3.9200000000000004,1.86,1.9,4.25,0],    [0,2,3.66,1.8399999999999999,1.84,3.475,1],    [0,2,3.6,1.78,1.95,3.45,1],    [0,2,3.78,1.81,1.93,3.75,0],    [0,2,3.6,1.7100000000000002,1.87,3.7,1],    [0,2,4.15,1.85,2.01,4,0],    [1,2,4.65,1.7899999999999998,1.92,3.5,1],    [1,2,5,1.95,1.96,3.9,0],    [1,2,5.13,1.92,1.93,3.65,0],    [1,2,4.54,1.8699999999999999,1.88,3.525,1],    [1,2,5.2700000000000005,1.98,1.97,3.725,0],    [1,2,4.5200000000000005,1.78,1.98,3.95,1],    [1,2,4.61,1.8199999999999998,1.78,3.25,1],    [1,2,5.13,1.8199999999999998,1.97,3.75,0],    [1,2,4.6,1.89,1.95,4.15,1],    [1,2,5.13,1.9899999999999998,1.98,3.7,0],    [1,2,4.66,1.78,1.93,3.8,1],    [1,2,5.17,2.0300000000000002,1.94,3.775,0],    [1,2,4.7,1.73,1.85,3.7,1],    [1,2,5.2,1.81,2.01,4.05,0],    [1,2,4.59,1.7100000000000002,1.9,3.575,1],    [1,2,5.05,1.9600000000000002,2.01,4.05,0],    [1,2,5.029999999999999,2,1.97,3.3,0],    [1,2,5.8,1.78,1.81,3.7,1],    [1,2,4.64,1.86,1.9,3.45,1],    [1,2,4.92,1.8199999999999998,1.95,4.4,0],    [1,2,4.24,1.73,1.81,3.6,1],    [1,2,4.85,1.75,1.91,3.4,0],    [1,2,4.32,1.6600000000000001,1.87,2.9,1],    [1,2,5.0600000000000005,1.94,1.93,3.8,0],    [1,2,4.67,1.7899999999999998,1.95,3.3,1],    [1,2,5.2,1.9,1.97,4.15,0],    [1,2,5.05,1.8399999999999999,2,3.4,1],    [1,2,4.95,1.9,2,3.8,0],    [1,2,4.64,1.78,1.91,3.7,1],    [1,2,5.279999999999999,2,2.05,4.55,0],    [1,2,4.09,1.6600000000000001,1.87,3.2,1],    [1,2,5.42,2.08,2.01,4.3,0],    [1,2,4.25,1.67,1.87,3.35,1],    [1,2,5.1,1.8800000000000001,2.03,4.1,0],    [1,2,4.970000000000001,1.86,1.95,3.6,0],    [1,2,4.75,1.6800000000000002,1.99,3.9,1],    [1,2,4.76,1.83,1.95,3.85,1],    [1,2,5.2,2.07,2.1,4.8,0],    [1,2,4.6899999999999995,1.6600000000000001,1.92,2.7,1],    [1,2,5.35,1.9899999999999998,2.05,4.5,0],    [1,2,4.9,1.95,2.1,3.95,0],    [1,2,4.62,1.75,1.87,3.65,1],    [1,2,5.09,1.9100000000000001,1.96,3.55,0],    [1,2,4.55,1.7,1.96,3.5,1],    [1,2,5.09,1.7899999999999998,1.96,3.675,1],    [1,2,5.08,1.85,2.01,4.45,0],    [1,2,5.01,1.7899999999999998,1.9,3.4,1],    [1,2,4.9,1.9600000000000002,2.12,4.3,0],    [1,2,5.15,1.8699999999999999,1.87,3.25,0],    [1,2,4.9799999999999995,1.73,1.98,3.675,1],    [1,2,4.8100000000000005,1.64,1.99,3.325,1],    [1,2,5.14,1.9,2.01,3.95,0],    [1,2,4.57,1.73,1.93,3.6,1],    [1,2,5.07,1.97,2.03,4.05,0],    [1,2,4.25,1.73,1.87,3.35,1],    [1,2,5.220000000000001,1.8800000000000001,1.97,3.45,0],    [1,2,4.5200000000000005,1.6600000000000001,1.91,3.25,1],    [1,2,4.93,1.9899999999999998,2.03,4.05,0],    [1,2,5.0200000000000005,1.8800000000000001,2.02,3.8,0],    [1,2,4.5600000000000005,1.94,1.94,3.525,1],    [1,2,5.1899999999999995,1.95,2.06,3.95,0],    [1,2,4.68,1.65,1.89,3.65,1],    [1,2,4.57,1.7,1.95,3.65,1],    [1,2,5.58,1.98,2.07,4,0],    [1,2,4.35,1.81,2.02,3.4,1],    [1,2,4.96,1.8199999999999998,1.93,3.775,0],    [1,2,5.08,1.9,2.1,4.1,0],    [1,2,5.0200000000000005,1.8699999999999999,1.98,3.775,1],    [2,1,4.61,1.3199999999999998,2.11,4.5,1],    [2,1,5,1.6300000000000001,2.3,5.7,0],    [2,1,4.87,1.41,2.1,4.45,1],    [2,1,5,1.52,2.18,5.7,0],    [2,1,4.76,1.45,2.15,5.4,0],    [2,1,4.65,1.35,2.1,4.55,1],    [2,1,4.54,1.46,2.11,4.8,1],    [2,1,4.67,1.53,2.19,5.2,0],    [2,1,4.33,1.34,2.09,4.4,1],    [2,1,4.68,1.54,2.15,5.15,0],    [2,1,4.09,1.3699999999999999,2.14,4.65,1],    [2,1,4.9,1.61,2.16,5.55,0],    [2,1,4.55,1.3699999999999999,2.14,4.65,1],    [2,1,4.84,1.46,2.13,5.85,0],    [2,1,4.58,1.46,2.1,4.2,1],    [2,1,4.93,1.5699999999999998,2.17,5.85,0],    [2,1,4.2,1.35,2.1,4.15,1],    [2,1,4.92,1.52,2.21,6.3,0],    [2,1,4.62,1.45,2.09,4.8,1],    [2,1,4.87,1.51,2.22,5.35,0],    [2,1,5.0200000000000005,1.4300000000000002,2.18,5.7,0],    [2,1,4.51,1.45,2.15,5,1],    [2,1,4.65,1.45,2.13,4.4,1],    [2,1,4.63,1.58,2.15,5.05,0],    [2,1,4.29,1.31,2.15,5,1],    [2,1,4.61,1.51,2.15,5.1,0],    [2,1,4.779999999999999,1.5,2.15,5.65,0],    [2,1,4.82,1.4300000000000002,2.1,4.6,1],    [2,1,5,1.53,2.2,5.55,0],    [2,1,4.7299999999999995,1.53,2.22,5.25,0],    [2,1,4.279999999999999,1.42,2.09,4.7,1],    [2,1,4.51,1.45,2.07,5.05,1],    [2,1,5.96,1.7,2.3,6.05,0],    [2,1,4.91,1.48,2.2,5.15,1],    [2,1,4.84,1.6300000000000001,2.2,5.4,0],    [2,1,4.26,1.3699999999999999,2.13,4.95,1],    [2,1,4.4399999999999995,1.73,2.19,5.25,0],    [2,1,4.4,1.3599999999999999,2.08,4.35,1],    [2,1,4.87,1.5699999999999998,2.08,5.35,0],    [2,1,4.2700000000000005,1.3699999999999999,2.08,3.95,1],    [2,1,4.96,1.6,2.25,5.7,0],    [2,1,4.529999999999999,1.3699999999999999,2.1,4.3,1],    [2,1,4.96,1.5,2.16,4.75,0],    [2,1,5.05,1.59,2.22,5.55,0],    [2,1,4.36,1.3900000000000001,2.17,4.9,1],    [2,1,4.55,1.3900000000000001,2.1,4.2,1],    [2,1,5.05,1.59,2.25,5.4,0],    [2,1,4.49,1.33,2.13,5.1,1],    [2,1,4.5200000000000005,1.58,2.15,5.3,0],    [2,1,4.66,1.42,2.1,4.85,1],    [2,1,4.85,1.41,2.2,5.3,0],    [2,1,4.51,1.44,2.1,4.4,1],    [2,1,5.01,1.5,2.25,5,0],    [2,1,4.65,1.44,2.17,4.9,1],    [2,1,4.5,1.54,2.2,5.05,0],    [2,1,4.38,1.3900000000000001,2.08,4.3,1],    [2,1,4.55,1.5,2.2,5,0],    [2,1,4.32,1.45,2.08,4.45,1],    [2,1,5.04,1.53,2.24,5.55,0],    [2,1,4.529999999999999,1.3800000000000001,2.08,4.2,1],    [2,1,4.62,1.49,2.21,5.3,0],    [2,1,4.57,1.3900000000000001,2.14,4.4,1],    [2,1,5.43,1.5699999999999998,2.31,5.65,0],    [2,1,4.58,1.42,2.19,4.7,1],    [2,1,4.9799999999999995,1.6800000000000002,2.3,5.7,0],    [2,1,4.95,1.6199999999999999,2.29,5.8,0],    [2,1,4.35,1.42,2.2,4.7,1],    [2,1,5.07,1.5,2.23,5.55,0],    [2,1,4.7700000000000005,1.5,2.16,4.75,1],    [2,1,4.64,1.56,2.21,5,0],    [2,1,4.82,1.56,2.21,5.1,0],    [2,1,4.65,1.48,2.17,5.2,1],    [2,1,4.64,1.5,2.16,4.7,1],    [2,1,4.86,1.6,2.3,5.8,0],    [2,1,4.75,1.42,2.09,4.6,1],    [2,1,5.11,1.6300000000000001,2.2,6,0],    [2,1,4.5200000000000005,1.3800000000000001,2.15,4.75,1],    [2,1,4.5200000000000005,1.64,2.23,5.95,0],    [2,1,4.91,1.45,2.12,4.625,1],    [2,1,5.25,1.56,2.21,5.45,0],    [2,1,4.74,1.46,2.12,4.725,1],    [2,1,5,1.59,2.24,5.35,0],    [2,1,4.49,1.3800000000000001,2.12,4.75,1],    [2,1,5.08,1.73,2.28,5.6,0],    [2,1,4.34,1.44,2.18,4.6,1],    [2,1,5.13,1.42,2.18,5.3,0],    [2,1,4.75,1.4,2.12,4.875,1],    [2,1,5.21,1.7,2.3,5.55,0],    [2,1,4.75,1.5,2.18,4.95,1],    [2,1,5.220000000000001,1.7100000000000002,2.28,5.4,0],    [2,1,4.55,1.45,2.12,4.75,1],    [2,1,4.95,1.61,2.24,5.65,0],    [2,1,4.45,1.47,2.14,4.85,1],    [2,1,5.08,1.5699999999999998,2.26,5.2,0],    [2,1,4.9399999999999995,1.58,2.16,4.925,0],    [2,1,4.6899999999999995,1.46,2.22,4.875,1],    [2,1,4.84,1.44,2.03,4.625,1],    [2,1,5.11,1.65,2.25,5.25,0],    [2,1,4.85,1.5,2.19,4.85,1],    [2,1,5.59,1.7,2.28,5.6,0],    [2,1,4.720000000000001,1.55,2.15,4.975,1],    [2,1,4.91,1.5,2.28,5.5,0],    [2,1,4.68,1.61,2.15,5.5,0],    [2,1,4.17,1.47,2.1,4.7,1],    [2,1,5.34,1.58,2.19,5.5,0],    [2,1,4.33,1.4,2.08,4.575,1],    [2,1,4.8100000000000005,1.51,2.09,5.5,0],    [2,1,5.05,1.52,2.16,5,1],    [2,1,4.9799999999999995,1.59,2.29,5.95,0],    [2,1,4.35,1.52,2.13,4.65,1],    [2,1,5.15,1.6300000000000001,2.3,5.5,0],    [2,1,4.62,1.41,2.17,4.375,1],    [2,1,5.51,1.6,2.3,5.85,0],    [2,1,4.88,1.6199999999999999,2.22,6,0],    [2,1,4.720000000000001,1.3699999999999999,2.14,4.925,1],    [2,1,4.68,1.4300000000000002,2.15,4.85,1],    [2,1,5.04,1.5699999999999998,2.22,5.75,0],    [2,1,4.5200000000000005,1.48,2.12,5.2,1],    [2,1,4.99,1.61,2.13,5.4,0]];

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编码：

\1. 数据标注

虽然上面已经对数据做了初步处理，但是还没达到可以用来训练的效果。为了能转换为 Tensor 需要将数据进行拆分标注 value 和 label，为了提升训练性能需要将数据集分为训练集和验证集，以下提供了数据拆分和转换的两个函数。

import * as tf from '@tensorflow/tfjs';

// Adelie：阿德利， Chinstrap：帽带， Gentoo： 巴布亚
export const IRIS_CLASSES =    ['阿德利', '帽带', '巴布亚'];export const IRIS_NUM_CLASSES = IRIS_CLASSES.length;
// 性别const SEX = ['MALE','FEMALE'];
// 所处岛屿const LAND = ['Torgersen','Biscoe','Dream'];
function convertToTensors(data, targets, testSplit) {  const numExamples = data.length;  if (numExamples !== targets.length) {    throw new Error('data and split have different numbers of examples');  }
  const indices = [];  for (let i = 0; i < numExamples; ++i) {    indices.push(i);  }  tf.util.shuffle(indices);
  const shuffledData = [];  const shuffledTargets = [];  for (let i = 0; i < numExamples; ++i) {    shuffledData.push(data[indices[i]]);    shuffledTargets.push(targets[indices[i]]);  }
  const numTestExamples = Math.round(numExamples * testSplit);  const numTrainExamples = numExamples - numTestExamples;
  const xDims = shuffledData[0].length;
  const xs = tf.tensor2d(shuffledData, [numExamples, xDims]);
  const ys = tf.oneHot(tf.tensor1d(shuffledTargets).toInt(), IRIS_NUM_CLASSES);
  const xTrain = xs.slice([0, 0], [numTrainExamples, xDims]);  const xTest = xs.slice([numTrainExamples, 0], [numTestExamples, xDims]);  const yTrain = ys.slice([0, 0], [numTrainExamples, IRIS_NUM_CLASSES]);  const yTest = ys.slice([0, 0], [numTestExamples, IRIS_NUM_CLASSES]);  return [xTrain, yTrain, xTest, yTest];}
export function getIrisData(testSplit) {
  return tf.tidy(() => {
    const dataByClass = [];    const targetsByClass = [];
    for (let i = 0; i < IRIS_CLASSES.length; ++i) {      dataByClass.push([]);      targetsByClass.push([]);    }
    for (const example of IRIS_DATA) {      const target = example[0];      const data = example.slice(1, example.length);      dataByClass[target].push(data);      targetsByClass[target].push(target);    }
    const xTrains = [];    const yTrains = [];    const xTests = [];    const yTests = [];
    for (let i = 0; i < IRIS_CLASSES.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(dataByClass[i], targetsByClass[i], testSplit);
      xTrains.push(xTrain);      yTrains.push(yTrain);      xTests.push(xTest);      yTests.push(yTest);    }
    const concatAxis = 0;
    return [      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)    ];
  });
}

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\2. 页面布局

页面通过 html 方式展示，通过选择和输入必要的参数，模型预测出企鹅的种类，同样的也有整个模型训练过程 UI 展示，代码如下。

<script src="script.js"></script><form action="#" onsubmit="predict(this); return false;">    <!-- 岛屿名：<input type="text" name="a"><br> -->    岛屿名：<select name="a">                <option value="0">南极帕尔默</option>                <option value="1">南极比斯科</option>                <option value="2">南极梦幻</option>            </select>    <br>    嘴峰长度(mm)：<input type="text" name="b"><br>    嘴峰深度(mm)：<input type="text" name="c"><br>    脚掌长度(mm)：<input type="text" name="d"><br>    体重(g)：<input type="text" name="e"><br>    <!-- 性别：<input type="text" name="f"><br> -->
    性别：<select name="f">                <option value="0">雄性</option>                <option value="1">雌性</option>            </select>    <br>
    <button type="submit">预测</button></form>

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\3. 模型训练

模型训练还是和之前线性回归类似，创建模型，添加隐藏层输出层和设置神经元格式，激活函数等，最后模型编译和模型训练。

import * as tf from '@tensorflow/tfjs';import * as tfvis from '@tensorflow/tfjs-vis';import { getIrisData, IRIS_CLASSES } from './data';
window.onload = async () => {    const [xTrain, yTrain, xTest, yTest] = getIrisData(0.15);
    const model = tf.sequential();    model.add(tf.layers.dense({        units: 10,        inputShape: [xTrain.shape[1]],        activation: 'sigmoid'    }));    model.add(tf.layers.dense({        units: 3,        activation: 'softmax'    }));
    model.compile({        loss: 'categoricalCrossentropy',        optimizer: tf.train.adam(0.1),        metrics: ['accuracy']    });
    await model.fit(xTrain, yTrain, {        epochs: 100,        validationData: [xTest, yTest],        callbacks: tfvis.show.fitCallbacks(            { name: '训练效果' },            ['loss', 'val_loss', 'acc', 'val_acc'],            { callbacks: ['onEpochEnd'] }        )    });
    // 为了将企鹅分类数据值降低到个位数    window.predict = (form) => {        const input = tf.tensor([[            form.a.value * 1,            form.b.value * 1/10,            form.c.value * 1/10,            form.d.value * 1/100,            form.e.value * 1/1000,            form.f.value * 1        ]]);        const pred = model.predict(input);        alert(`预测结果：${IRIS_CLASSES[pred.argMax(1).dataSync(0)]}`);        return false;    };};