{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通过量子神经网络对鸢尾花进行分类\n",
    "\n",
    "[![查看源文件](https://gitee.com/mindspore/docs/raw/r1.6/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r1.6/docs/mindquantum/docs/source_zh_cn/classification_of_iris_by_qnn.ipynb)\n",
    "\n",
    "## 概述\n",
    "\n",
    "在之前的案例中，我们介绍了什么是变分量子线路，并通过一个简单的例子体验了如何搭建量子神经网络来解决一个小问题。在本文档中，我们将体验升级，将会介绍如何通过搭建量子神经网络来解决经典机器学习中的问题。我们选取的问题是：监督学习中的鸢尾花分类问题。\n",
    "\n",
    "问题描述：鸢尾花（iris）数据集是经典机器学习中常用的数据集，该数据集总共包含150个样本（分为3种不同的亚属：山鸢尾（setosa）、杂色鸢尾（versicolor）和维吉尼亚鸢尾（virginica），每个亚属各有50个样本），每个样本包含4个特征，分别为花萼长度（sepal length）、花萼宽度（sepal width）和花瓣长度（petal length）、花瓣宽度（petal width）。\n",
    "\n",
    "我们选取前100个样本（山鸢尾（setosa）和杂色鸢尾（versicolor）），并随机抽取80个样本作为训练集，通过搭建量子神经网络对量子分类器（Ansatz）进行训练，学习完成后，对剩余的20个样本进行分类测试，期望预测的准确率尽可能高。\n",
    "\n",
    "思路：我们需要将100个样本进行划分，分成80个训练样本和20个测试样本，根据训练样本的经典数据计算搭建Encoder所需的参数，然后，搭建Encoder，将训练样本的经典数据编码到量子态上，接着，搭建Ansatz，通过搭建的量子神经网络层和MindSpore的算子对Ansatz中的参数进行训练，进而得到最终的分类器，最后，对剩余的20个测试样本进行分类测试，得到预测的准确率。\n",
    "\n",
    "## 环境准备\n",
    "\n",
    "首先，我们需要导入鸢尾花的数据集，而在导入该数据集前，我们需要使用sklearn库中的datasets模块，因此读者需要检查是否安装了sklearn库，可执行如下代码检查。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: scikit-learn\n",
      "Version: 1.0.1\n",
      "Summary: A set of python modules for machine learning and data mining\n",
      "Home-page: http://scikit-learn.org\n",
      "Author: \n",
      "Author-email: \n",
      "License: new BSD\n",
      "Location: /usr/local/lib/python3.7/dist-packages\n",
      "Requires: joblib, numpy, scipy, threadpoolctl\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "!pip show scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "若无报错，则表明已安装。简单说明一下，sklearn是scikit-learn的简称，是一个基于Python的第三方模块。sklearn库集成了一些常用的机器学习方法，在进行机器学习任务时，并不需要实现算法，只需要简单的调用sklearn库中提供的模块就能完成大多数的机器学习任务。\n",
    "\n",
    "若未安装sklearn库，则可通过运行如下代码来安装。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "pip install scikit-learn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，我们设置本文档所需的线程数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # 导入os库\n",
    "os.environ['OMP_NUM_THREADS'] = '2'                       # 通过os.environ将量子线路模拟器的线程数设置为2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "说明：\n",
    "\n",
    "（1）os是一个标准库，里面包含许多操作文件和目录的函数；\n",
    "\n",
    "（2）os.environ()模块，可以获取并修改环境变量；一般来说，我们需要在一开始设置线程数；\n",
    "\n",
    "## 导入鸢尾花数据集\n",
    "\n",
    "有了上述准备，现在我们就可以导入鸢尾花的数据集了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor' 'virginica']\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np                                        # 导入numpy库并简写为np\n",
    "from sklearn import datasets                              # 导入datasets模块，用于加载鸢尾花的数据集\n",
    "\n",
    "iris_dataset = datasets.load_iris()                       # 加载鸢尾花的数据集，并存在iris_dataset\n",
    "\n",
    "print(iris_dataset.data.shape)                            # 打印iris_dataset的样本的数据维度\n",
    "print(iris_dataset.feature_names)                         # 打印iris_dataset的样本的特征名称\n",
    "print(iris_dataset.target_names)                          # 打印iris_dataset的样本包含的亚属名称\n",
    "print(iris_dataset.target)                                # 打印iris_dataset的样本的标签的数组\n",
    "print(iris_dataset.target.shape)                          # 打印iris_dataset的样本的标签的数据维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，该数据集共有150个样本，每个样本均有4个特征，分别为花萼长度（sepal length）、花萼宽度（sepal width）和花瓣长度（petal length）、花瓣宽度（petal width）。同时样本包含3种不同的亚属：山鸢尾（setosa）、杂色鸢尾（versicolor）和维吉尼亚鸢尾（virginica），每个样本有对应的分类编号，0表示样本属于setosa，1表示样本属于versicolor，2表示样本属于virginica，因此有一个由150个数字组成的数组来表示样本的亚属类型。\n",
    "\n",
    "由于我们只选取前100个样本，因此执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4)\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor']\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "X = iris_dataset.data[:100, :].astype(np.float32)         # 选取iris_dataset的data的前100个数据，将其数据类型转换为float32，并储存在X中\n",
    "X_feature_names = iris_dataset.feature_names              # 将iris_dataset的特征名称储存在X_feature_names中\n",
    "y = iris_dataset.target[:100].astype(int)                 # 选取iris_dataset的target的前100个数据，将其数据类型转换为int，并储存在y中\n",
    "y_target_names = iris_dataset.target_names[:2]            # 选取iris_dataset的target_names的前2个数据，并储存在y_target_names中\n",
    "\n",
    "print(X.shape)                                            # 打印样本的数据维度\n",
    "print(X_feature_names)                                    # 打印样本的特征名称\n",
    "print(y_target_names)                                     # 打印样本包含的亚属名称\n",
    "print(y)                                                  # 打印样本的标签的数组\n",
    "print(y.shape)                                            # 打印样本的标签的数据维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时的数据集`X`中只有100个样本，每个样本依然有4个特征，仍为花萼长度（sepal length）、花萼宽度（sepal width）和花瓣长度（petal length）、花瓣宽度（petal width）。此时只有2种不同的亚属：山鸢尾（setosa）和杂色鸢尾（versicolor），并且每一个样本有对应的分类编号，0表示它属于setosa，1表示它属于versicolor，因此有一个由100个数字组成的数组来表示样本的亚属类型。\n",
    "\n",
    "## 数据图像化\n",
    "\n",
    "为了更加直观地了解这100个样本组成的数据集，我们画出所有样本不同特征之间组成的散点图，执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1656x1656 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt                                                           # 导入matplotlib.pyplot模块并简写为plt\n",
    "\n",
    "feature_name = {0: 'sepal length', 1: 'sepal width', 2: 'petal length', 3: 'petal width'} # 将不同的特征名称分别标记为0,1,2,3\n",
    "axes = plt.figure(figsize=(23, 23)).subplots(4, 4)                                        # 画出一个大小为23*23的图，包含4*4=16个子图\n",
    "\n",
    "colormap = {0: 'r', 1: 'g'}                                                               # 将标签为0的样本设为红色，标签为1的样本设为绿色\n",
    "cvalue = [colormap[i] for i in y]                                                         # 将100个样本对应的标签设置相应的颜色\n",
    "\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        if i != j:\n",
    "            ax = axes[i][j]                                                               # 在[i][j]的子图上开始画图\n",
    "            ax.scatter(X[:, i], X[:, j], c=cvalue)                                        # 画出第[i]个特征和第[j]个特征组成的散点图\n",
    "            ax.set_xlabel(feature_name[i], fontsize=22)                                   # 设置X轴的名称为第[i]个特征名称，字体大小为22\n",
    "            ax.set_ylabel(feature_name[j], fontsize=22)                                   # 设置Y轴的名称为第[j]个特征名称，字体大小为22\n",
    "plt.show()                                                                                # 渲染图像，即呈现图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述呈现的图像可以看到，红色的点表示标签为“0”的样本，绿色的点表示标签为“1”的样本，另外，我们发现，这两类样本的不同特征还是比较容易区分的。\n",
    "\n",
    "## 数据预处理\n",
    "\n",
    "接下来，我们需要计算生成搭建Encoder时所要用到的参数，然后将数据集划分为训练集和测试集，执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 7)\n"
     ]
    }
   ],
   "source": [
    "alpha = X[:, :3] * X[:, 1:]           # 每一个样本中，利用相邻两个特征值计算出一个参数，即每一个样本会多出3个参数（因为有4个特征值），并储存在alpha中\n",
    "X = np.append(X, alpha, axis=1)       # 在axis=1的维度上，将alpha的数据值添加到X的特征值中\n",
    "\n",
    "print(X.shape)                        # 打印此时X的样本的数据维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时的数据集`X`中仍有100个样本，但此时每个样本却有7个特征，前4个特征值就是原来的特征值，后3个特征值就是通过上述预处理计算得到的特征值，其具体计算公式如下：\n",
    "\n",
    "$$\n",
    "X_{i+4}^{j} = X_{i}^{j} * X_{i+1}^{j}, i=0,1,2,j=1,2,...,100.\n",
    "$$\n",
    "\n",
    "最后，我们将此时的数据集分为训练集和测试集，执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 7)\n",
      "(20, 7)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split                                                   # 导入train_test_split函数，用于对数据集进行划分\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, shuffle=True) # 将数据集划分为训练集和测试集\n",
    "\n",
    "print(X_train.shape)                                                                                   # 打印训练集中样本的数据类型\n",
    "print(X_test.shape)                                                                                    # 打印测试集中样本的数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时的训练集有80个样本，测试集有20个样本，每个样本均有7个特征。\n",
    "\n",
    "说明：\n",
    "\n",
    "（1）append主要用于为原始数组添加一些值，一般格式如下：np.append(arr, values, axis=None)，arr是需要被添加值的数组，values就是添加到数组arr中的值，axis表示沿着哪个方向；\n",
    "\n",
    "（2）shuffle=True表示将数据集打乱，每次都会以不同的顺序返回， shuffle就是为了避免数据投入的顺序对网络训练造成影响。增加随机性，提高网络的泛化性能，避免因为有规律的数据出现，导致权重更新时的梯度过于极端，避免最终模型过拟合或欠拟合。\n",
    "\n",
    "（3）train_test_split是交叉验证中常用的函数，主要用于是从样本中随机地按比例选取训练数据集和测试数据集，一般格式如下：\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size, random_state, shuffle=True)，其中test_size表示测试样本的比例，random_state表示产生随机数的种子，shuffle=True表示将数据集打乱。\n",
    "\n",
    "## 搭建Encoder\n",
    "\n",
    "根据图示的量子线路图，我们可以在MindQuantum中搭建Encoder，将经典数据编码到量子态上。\n",
    "\n",
    "![encoder classification of iris by qnn](https://gitee.com/mindspore/docs/raw/r1.6/docs/mindquantum/docs/source_zh_cn/images/encoder_classification_of_iris_by_qnn.png)\n",
    "\n",
    "在这里，我们采用的编码方式是IQP编码（Instantaneous Quantum Polynomial encoding），一般来说Encoder的编码方式不固定，可根据问题需要选择不同的编码方式，有时也会根据最后的性能对Encoder进行调整。\n",
    "\n",
    "Encoder中的参数$\\alpha_0,\\alpha_1,...,\\alpha_6$​​的值，就是用上述数据预处理中得到的7个特征值代入。​"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================Circuit Summary==================================\n",
      "|Total number of gates  : 17.                                                     |\n",
      "|Parameter gates        : 7.                                                      |\n",
      "|with 7 parameters are  : alpha0, alpha1, alpha2, alpha3, alpha4, alpha5, alpha6. |\n",
      "|Number qubit of circuit: 4                                                       |\n",
      "===================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/html": [
       "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha0)────●──────────────────●──────────────────────────────────────────────────</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                         │                  │                                                  </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha1)────X────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha4)────X────●──────────────────●──────────────────────────</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                                 │                  │                          </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha2)────────────────────────────X────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha5)────X────●──────────────────●──</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                                                         │                  │  </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha3)────────────────────────────────────────────────────X────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha6)────X──</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "q0: ──H────RZ(alpha0)────●──────────────────●──────────────────────────────────────────────────\n",
       "                         │                  │                                                  \n",
       "q1: ──H────RZ(alpha1)────X────RZ(alpha4)────X────●──────────────────●──────────────────────────\n",
       "                                                 │                  │                          \n",
       "q2: ──H────RZ(alpha2)────────────────────────────X────RZ(alpha5)────X────●──────────────────●──\n",
       "                                                                         │                  │  \n",
       "q3: ──H────RZ(alpha3)────────────────────────────────────────────────────X────RZ(alpha6)────X──"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "from mindquantum.core import Circuit                 # 导入Circuit模块，用于搭建量子线路\n",
    "from mindquantum.core import UN                      # 导入UN模块\n",
    "from mindquantum.core import H, X, RZ                # 导入量子门H, X, RZ\n",
    "\n",
    "encoder = Circuit()                                  # 初始化量子线路\n",
    "\n",
    "encoder += UN(H, 4)                                  # H门作用在每1位量子比特\n",
    "for i in range(4):                                   # i = 0, 1, 2, 3\n",
    "    encoder += RZ(f'alpha{i}').on(i)                 # RZ(alpha_i)门作用在第i位量子比特\n",
    "for j in range(3):                                   # j = 0, 1, 2\n",
    "    encoder += X.on(j+1, j)                          # X门作用在第j+1位量子比特，受第j位量子比特控制\n",
    "    encoder += RZ(f'alpha{j+4}').on(j+1)             # RZ(alpha_{j+4})门作用在第0位量子比特\n",
    "    encoder += X.on(j+1, j)                          # X门作用在第j+1位量子比特，受第j位量子比特控制\n",
    "\n",
    "encoder = encoder.no_grad()                          # Encoder作为整个量子神经网络的第一层，不用对编码线路中的梯度求导数，因此加入no_grad()\n",
    "encoder.summary()                                    # 总结Encoder\n",
    "encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对Encoder的Summary中可以看到，该量子线路由17个量子门组成，其中有7个含参量子门且参数为$\\alpha_0,\\alpha_1,...,\\alpha_6$，该量子线路调控的量子比特数为4。\n",
    "\n",
    "说明：\n",
    "\n",
    "UN模块用于将量子门映射到不同的目标量子比特和控制量子比特，一般格式如下：mindquantum.circuit.UN(gate, maps_obj, maps_ctrl=None)，括号中的gate是我们需要执行的量子门，maps_obj是需要执行该量子门的目标量子比特，maps_ctrl是控制量子比特，若为None即无控制量子位。若每个量子比特位执行同一个非参数量子门，则可以直接写出UN(gate, N)，N表示量子比特个数。\n",
    "\n",
    "## 搭建Ansatz\n",
    "\n",
    "根据图示的量子线路图，我们可以在MindQuantum中搭建Ansatz。\n",
    "\n",
    "![ansatz classification of iris by qnn](https://gitee.com/mindspore/docs/raw/r1.6/docs/mindquantum/docs/source_zh_cn/images/ansatz_classification_of_iris_by_qnn.png)\n",
    "\n",
    "与Encoder一样，Ansatz的编码方式也不固定，我们可以尝试不同的编码方式来测试最后的结果。\n",
    "\n",
    "在这里，我们采用的是HardwareEfficientAnsatz，即上述量子线路图所示的编码方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================Circuit Summary====================================================\n",
      "|Total number of gates  : 25.                                                                                         |\n",
      "|Parameter gates        : 16.                                                                                         |\n",
      "|with 16 parameters are : d0_n0_0, d0_n1_0, d0_n2_0, d0_n3_0, d1_n0_0, d1_n1_0, d1_n2_0, d1_n3_0, d2_n0_0, d2_n1_0... |\n",
      "|Number qubit of circuit: 4                                                                                           |\n",
      "=======================================================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n0_0)────●────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n0_0)────────────────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n0_0)────────────────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n0_0)────────────────────────────────</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                     │                                       │                                            │                                                    </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n1_0)────X─────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n1_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n1_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n1_0)─────────────────</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                               │                                            │                                            │                                     </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n2_0)──────────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n2_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n2_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n2_0)──</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                              │                                            │                                            │                      </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n3_0)─────────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n3_0)────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n3_0)────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n3_0)──</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "q0: ──RY(d0_n0_0)────●────RY(d1_n0_0)────────────────────────●─────────RY(d2_n0_0)────────────────────────●─────────RY(d3_n0_0)────────────────────────────────\n",
       "                     │                                       │                                            │                                                    \n",
       "q1: ──RY(d0_n1_0)────X─────────●─────────RY(d1_n1_0)─────────X──────────────●─────────RY(d2_n1_0)─────────X──────────────●─────────RY(d3_n1_0)─────────────────\n",
       "                               │                                            │                                            │                                     \n",
       "q2: ──RY(d0_n2_0)──────────────X──────────────●─────────RY(d1_n2_0)─────────X──────────────●─────────RY(d2_n2_0)─────────X──────────────●─────────RY(d3_n2_0)──\n",
       "                                              │                                            │                                            │                      \n",
       "q3: ──RY(d0_n3_0)─────────────────────────────X─────────RY(d1_n3_0)────────────────────────X─────────RY(d2_n3_0)────────────────────────X─────────RY(d3_n3_0)──"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "from mindquantum.algorithm import HardwareEfficientAnsatz                                           # 导入HardwareEfficientAnsatz\n",
    "from mindquantum.core import RY                                                                     # 导入量子门RY\n",
    "\n",
    "ansatz = HardwareEfficientAnsatz(4, single_rot_gate_seq=[RY], entangle_gate=X, depth=3).circuit     # 通过HardwareEfficientAnsatz搭建Ansatz\n",
    "ansatz.summary()                                                                                    # 总结Ansatz\n",
    "ansatz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对Ansatz的Summary中可以看到，该量子线路由25个量子门组成，其中有16个含参量子门且参数为d2_n3_0, d1_n1_0, d0_n2_0, d1_n0_0, d3_n2_0, d2_n2_0, d0_n1_0, d3_n1_0, d2_n0_0, d3_n0_0...，该量子线路调控的量子比特数为4。\n",
    "\n",
    "说明：\n",
    "\n",
    "HardwareEfficientAnsatz是一种容易在量子芯片上实现的Ansatz，其量子线路图由红色虚线框内的量子门组成，一般格式如下：mindquantum.ansatz.HardwareEfficientAnsatz(n_qubits, single_rot_gate_seq, entangle_gate=X, entangle_mapping=\"linear\", depth=1)，括号中的n_qubits表示ansatz需要作用的量子比特总数，single_rot_gate_seq表示一开始每一位量子比特执行的参数门，同时后面需要执行的参数门也固定了，只是参数不同，entangle_gate=X表示执行的纠缠门为X，entangle_mapping=\"linear\"表示纠缠门将作用于每对相邻量子比特，depth表示黑色虚线框内的量子门需要重复的次数。\n",
    "\n",
    "那么完整的量子线路就是Encoder加上Ansatz。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================Circuit Summary================================================\n",
      "|Total number of gates  : 42.                                                                                 |\n",
      "|Parameter gates        : 23.                                                                                 |\n",
      "|with 23 parameters are : alpha0, alpha1, alpha2, alpha3, alpha4, alpha5, alpha6, d0_n0_0, d0_n1_0, d0_n2_0...|\n",
      "|Number qubit of circuit: 4                                                                                   |\n",
      "===============================================================================================================\n"
     ]
    },
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       "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha0)────●──────────────────●────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n0_0)──────────────────────────────────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n0_0)────────────────────────────────────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n0_0)────────────────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n0_0)────────────────────────────────</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                         │                  │                                                         │                                                                │                                            │                                                    </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha1)────X────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha4)────X─────────●───────────────────────●────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n1_0)────────X───────────────────────────────────────●────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n1_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n1_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n1_0)─────────────────</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                                      │                       │                                                               │                                       │                                            │                                     </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha2)─────────────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha5)────X─────────●────────────────────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n2_0)────X─────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n2_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n2_0)─────────X──────────────●─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n2_0)──</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                                                                        │                            │                                  │                                            │                                            │                      </span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──H────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha3)───────────────────────────────────────────────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RZ</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(alpha6)─────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d0_n3_0)──────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d1_n3_0)────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d2_n3_0)────────────────────────X─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RY</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(d3_n3_0)──</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "q0: ──H────RZ(alpha0)────●──────────────────●────RY(d0_n0_0)──────────────────────────────────────────●─────────RY(d1_n0_0)────────────────────────────────────────────●─────────RY(d2_n0_0)────────────────────────●─────────RY(d3_n0_0)────────────────────────────────\n",
       "                         │                  │                                                         │                                                                │                                            │                                                    \n",
       "q1: ──H────RZ(alpha1)────X────RZ(alpha4)────X─────────●───────────────────────●────RY(d0_n1_0)────────X───────────────────────────────────────●────RY(d1_n1_0)─────────X──────────────●─────────RY(d2_n1_0)─────────X──────────────●─────────RY(d3_n1_0)─────────────────\n",
       "                                                      │                       │                                                               │                                       │                                            │                                     \n",
       "q2: ──H────RZ(alpha2)─────────────────────────────────X─────────RZ(alpha5)────X─────────●────────────────────────────●─────────RY(d0_n2_0)────X─────────●─────────RY(d1_n2_0)─────────X──────────────●─────────RY(d2_n2_0)─────────X──────────────●─────────RY(d3_n2_0)──\n",
       "                                                                                        │                            │                                  │                                            │                                            │                      \n",
       "q3: ──H────RZ(alpha3)───────────────────────────────────────────────────────────────────X─────────RZ(alpha6)─────────X─────────RY(d0_n3_0)──────────────X─────────RY(d1_n3_0)────────────────────────X─────────RY(d2_n3_0)────────────────────────X─────────RY(d3_n3_0)──"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "circuit = encoder + ansatz                   # 完整的量子线路由Encoder和Ansatz组成\n",
    "circuit.summary()\n",
    "circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对完整的量子线路的Summary中可以看到，该量子线路由42个量子门组成，其中有23个含参量子门且参数为$\\alpha_0,\\alpha_1,...,\\alpha_6$和d2_n3_0, d1_n1_0, d0_n2_0, d1_n0_0, d3_n2_0, d2_n2_0, d0_n1_0, d3_n1_0, d2_n0_0, d3_n0_0...，该量子线路调控的量子比特数为4。\n",
    "\n",
    "## 构建哈密顿量\n",
    "\n",
    "我们分别对第2位和第3位量子比特执行泡利`Z`算符测量，构建对应的哈密顿量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0 [Z2] , 1.0 [Z3] ]\n"
     ]
    }
   ],
   "source": [
    "from mindquantum.core import QubitOperator                     # 导入QubitOperator模块，用于构造泡利算符\n",
    "from mindquantum.core import Hamiltonian                       # 导入Hamiltonian模块，用于构建哈密顿量\n",
    "\n",
    "hams = [Hamiltonian(QubitOperator(f'Z{i}')) for i in [2, 3]]   # 分别对第2位和第3位量子比特执行泡利Z算符测量，且将系数都设为1，构建对应的哈密顿量\n",
    "print(hams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时构建的哈密顿量有2个，分别为对第2位和第3位量子比特执行泡利`Z`算符，且将系数都设为1。通过泡利`Z`算符测量，我们可以得到2个哈密顿量测量值，若第1个测量值更大，则会将此样本归类到标签为“0”的类，同理，若第2个测量值更大，则会将此样本归类到标签为“1”的类。通过神经网络的训练，期望训练样本中标签为“0”的样本的第1个测量值更大，而标签为“1”的样本的第2个测量值更大，最后应用此模型来预测新样本的分类。\n",
    "\n",
    "## 搭建量子神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MQLayer<\n",
       "  (evolution): MQOps<4 qubits projectq VQA Operator>\n",
       "  >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "import mindspore as ms                                                                         # 导入mindspore库并简写为ms\n",
    "from mindquantum.framework import MQLayer                                                      # 导入MQLayer\n",
    "from mindquantum.simulator import Simulator\n",
    "\n",
    "ms.context.set_context(mode=ms.context.PYNATIVE_MODE, device_target=\"CPU\")\n",
    "ms.set_seed(1)                                                                                 # 设置生成随机数的种子\n",
    "sim = Simulator('projectq', circuit.n_qubits)\n",
    "grad_ops = sim.get_expectation_with_grad(hams,\n",
    "                                         circuit,\n",
    "                                         None,\n",
    "                                         None,\n",
    "                                         encoder.params_name,\n",
    "                                         ansatz.params_name,\n",
    "                                         parallel_worker=5)\n",
    "QuantumNet = MQLayer(grad_ops)          # 搭建量子神经网络\n",
    "QuantumNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，我们已经成功搭建了量子机器学习层，其可以无缝地跟MindSpore中其它的算子构成一张更大的机器学习网络。\n",
    "\n",
    "说明：\n",
    "\n",
    "MindSpore是一个全场景深度学习框架，旨在实现易开发、高效执行、全场景覆盖三大目标，提供支持异构加速的张量可微编程能力，支持云、服务器、边和端多种硬件平台.\n",
    "\n",
    "## 训练\n",
    "\n",
    "接下来，我们需要定义损失函数，设定需要优化的参数，然后将搭建好的量子机器学习层和MindSpore的算子组合，构成一张更大的机器学习网络，最后对该模型进行训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 step: 16, loss is 0.6600145\n",
      "epoch: 2 step: 16, loss is 0.4009103\n",
      "epoch: 3 step: 16, loss is 0.39099234\n",
      "epoch: 4 step: 16, loss is 0.3733629\n",
      "epoch: 5 step: 16, loss is 0.3705962\n",
      "epoch: 6 step: 16, loss is 0.37426245\n",
      "epoch: 7 step: 16, loss is 0.37181872\n",
      "epoch: 8 step: 16, loss is 0.37131247\n",
      "epoch: 9 step: 16, loss is 0.37142643\n",
      "epoch: 10 step: 16, loss is 0.37067422\n",
      "epoch: 11 step: 16, loss is 0.3701976\n",
      "epoch: 12 step: 16, loss is 0.36975253\n",
      "epoch: 13 step: 16, loss is 0.36923727\n",
      "epoch: 14 step: 16, loss is 0.3688001\n",
      "epoch: 15 step: 16, loss is 0.3684062\n",
      "epoch: 16 step: 16, loss is 0.36804128\n",
      "epoch: 17 step: 16, loss is 0.36773998\n",
      "epoch: 18 step: 16, loss is 0.36747772\n",
      "epoch: 19 step: 16, loss is 0.36726192\n",
      "epoch: 20 step: 16, loss is 0.36708587\n"
     ]
    }
   ],
   "source": [
    "from mindspore.nn import SoftmaxCrossEntropyWithLogits                         # 导入SoftmaxCrossEntropyWithLogits模块，用于定义损失函数\n",
    "from mindspore.nn import Adam, Accuracy                                        # 导入Adam模块和Accuracy模块，分别用于定义优化参数，评估预测准确率\n",
    "from mindspore import Model                                                    # 导入Model模块，用于建立模型\n",
    "from mindspore.dataset import NumpySlicesDataset                               # 导入NumpySlicesDataset模块，用于创建模型可以识别的数据集\n",
    "from mindspore.train.callback import Callback, LossMonitor                     # 导入Callback模块和LossMonitor模块，分别用于定义回调函数和监控损失\n",
    "\n",
    "loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')            # 通过SoftmaxCrossEntropyWithLogits定义损失函数，sparse=True表示指定标签使用稀疏格式，reduction='mean'表示损失函数的降维方法为求平均值\n",
    "opti = Adam(QuantumNet.trainable_params(), learning_rate=0.1)                  # 通过Adam优化器优化Ansatz中的参数，需要优化的是Quantumnet中可训练的参数，学习率设为0.1\n",
    "\n",
    "model = Model(QuantumNet, loss, opti, metrics={'Acc': Accuracy()})             # 建立模型：将MindQuantum构建的量子机器学习层和MindSpore的算子组合，构成一张更大的机器学习网络\n",
    "\n",
    "train_loader = NumpySlicesDataset({'features': X_train, 'labels': y_train}, shuffle=False).batch(5) # 通过NumpySlicesDataset创建训练样本的数据集，shuffle=False表示不打乱数据，batch(5)表示训练集每批次样本点有5个\n",
    "test_loader = NumpySlicesDataset({'features': X_test, 'labels': y_test}).batch(5)                   # 通过NumpySlicesDataset创建测试样本的数据集，batch(5)表示测试集每批次样本点有5个\n",
    "\n",
    "class StepAcc(Callback):                                                        # 定义一个关于每一步准确率的回调函数\n",
    "    def __init__(self, model, test_loader):\n",
    "        self.model = model\n",
    "        self.test_loader = test_loader\n",
    "        self.acc = []\n",
    "\n",
    "    def step_end(self, run_context):\n",
    "        self.acc.append(self.model.eval(self.test_loader, dataset_sink_mode=False)['Acc'])\n",
    "\n",
    "monitor = LossMonitor(16)                                                       # 监控训练中的损失，每16步打印一次损失值\n",
    "\n",
    "acc = StepAcc(model, test_loader)                                               # 使用建立的模型和测试样本计算预测的准确率\n",
    "\n",
    "model.train(20, train_loader, callbacks=[monitor, acc], dataset_sink_mode=False)# 将上述建立好的模型训练20次"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，20次迭代后，损失值不断下降并趋于稳定，最后收敛于约0.367。\n",
    "\n",
    "说明：\n",
    "\n",
    "（1）nn.SoftmaxCrossEntropyWithLogits可以计算数据和标签之间的softmax交叉熵。使用交叉熵损失测量输入（使用softmax函数计算）的概率和目标之间的分布误差，其中类是互斥的（只有一个类是正的），一般格式如下：mindspore.nn.SoftmaxCrossEntropyWithLogits(sparse=False, reduction=\"none\")，sparse=False表示指定标签是否使用稀疏格式，默认值:False；reduction=\"none\"表示适用于损失的减少类型。可选值为mean、sum和none。如果为none，则不执行减少，默认值:“没有”。\n",
    "\n",
    "（2）Adam模块通过自适应矩估计算法更新梯度，可以优化Ansazt中的参数，输入的是神经网络中可训练的参数；一般格式如下：nn.Adam(net.trainable_params(), learning_rate=0.1)，学习率可以自己调节；\n",
    "\n",
    "（3）mindspore.Model是用于训练或测试的高级API，模型将层分组到具有训练和推理特征的对象中，一般格式如下：mindspore.Model(network, loss_fn=None, optimizer=None, metrics=None, eval_network=None, eval_indexes=None, amp_level=\"O0\", acc_level=\"O0\")，其中network就是我们要训练的网络即Quantumnet；loss_fn即目标函数，在这里就是定义的loss函数；optimizer即优化器，用于更新权重，在这里就是定义的opti；metrics就是模型在训练和测试期间需要评估的字典或一组度量，在这里就是评估准确率；\n",
    "\n",
    "（4）Accuracy用于计算分类和多标签数据的准确率，一般格式如下：mindspore.nn.Accuracy(eval_type=\"classification\")，用于分类（单标签）和多标签（多标签分类)）的数据集上计算准确率的度量，默认值：“分类”；\n",
    "\n",
    "（5）NumpySlicesDataset使用给定的数据切片创建数据集，主要用于将Python数据加载到数据集中，一般格式如下：mindspore.dataset.NumpySlicesDataset(data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None)；\n",
    "\n",
    "（6）Callback用于构建回调类的抽象基类，回调是上下文管理器，在传递到模型时将输入和输出。你可以使用此机制自动初始化和释放资源。回调函数将执行当前步骤或数据轮回中的一些操作；\n",
    "\n",
    "（7）LossMonitor主要用于监控训练中的损失，如果损失是NAN或INF，它将终止训练，一般格式如下：mindspore.train.callback.LossMonitor(per_print_times=1)，per_print_times=1表示每秒钟打印一次损失，默认值：1；\n",
    "\n",
    "（8）train模块用于训练模型，其中迭代由Python前端控制；当设置PyNative模式或CPU时，训练过程将在数据集不接收的情况下执行，一般格式如下：train(epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1)，其中epoch表示在数据上的总迭代次数；train_dataset就是我们定义的train_loader；callbacks就是我们需要回调的损失值和准确率；dataset_sink_mode表示确定是否通过数据集通道传递数据，文档中为否。\n",
    "\n",
    "## 训练过程中的准确率\n",
    "\n",
    "我们已经看到损失值趋于稳定，那么我们还可以将模型在训练过程中的预测的准确率呈现出来，执行如下代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(acc.acc)\n",
    "plt.title('Statistics of accuracy', fontsize=20)\n",
    "plt.xlabel('Steps', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印的图像可以看到，在大约50步后，预测的准确率收敛于1，也就是说预测的准确率已经可以达到100%。\n",
    "\n",
    "## 预测\n",
    "\n",
    "最后，我们测试一下训练好的模型，将其应用在测试集上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测分类结果： [0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0]\n",
      "实际分类结果： [0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0]\n",
      "{'Acc': 1.0}\n"
     ]
    }
   ],
   "source": [
    "from mindspore import ops, Tensor                                            # 导入ops模块和Tensor模块\n",
    "\n",
    "predict = np.argmax(ops.Softmax()(model.predict(Tensor(X_test))), axis=1)    # 使用建立的模型和测试样本，得到测试样本预测的分类\n",
    "correct = model.eval(test_loader, dataset_sink_mode=False)                   # 计算测试样本应用训练好的模型的预测准确率\n",
    "\n",
    "print(\"预测分类结果：\", predict)                                              # 对于测试样本，打印预测分类结果\n",
    "print(\"实际分类结果：\", y_test)                                               # 对于测试样本，打印实际分类结果\n",
    "\n",
    "print(correct)                                                               # 打印模型预测的准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印的可以看到，预测分类结果和实际分类结果完全一致，模型预测的准确率达到了100%。\n",
    "\n",
    "至此，我们体验了如何通过搭建量子神经网络来解决经典机器学习中的经典问题——鸢尾花分类问题。相信大家也对使用MindQuantum有了更进一步的了解！期待大家挖掘更多的问题，充分发挥MindQuantum强大的功能！\n",
    "\n",
    "若想查询更多关于MindQuantum的API，请点击：[https://mindspore.cn/mindquantum/](https://mindspore.cn/mindquantum/)。"
   ]
  }
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