In the last couple of years, Artificial intelligence is finding its use in all sorts of applications. It can be in medical, health and fitness, education, video calling, sports… you just name it.

在过去的几年中，人工智能正在各种应用中找到其用途。 它可以用于医疗，健康和健身，教育，视频通话，体育……等等。

If you are wondering that can you use artificial intelligence techniques in your research areas? But you don’t have much idea how to use it.

如果您想知道是否可以在研究领域中使用人工智能技术？ 但是您对如何使用它并不了解。

Then I say- Yes, there is a good chance that you can use it and in this article, I am going to explain how to employ already developed artificial intelligence techniques to the application of your choice within 40 lines of code.

然后我说-是的，您很有可能会使用它，在本文中，我将解释如何在40行代码中将已经开发的人工智能技术应用于您选择的应用程序。

Another thing before diving into the code, if you are thinking that do I need to be an expert in coding to understand this?

在深入研究代码之前的另一件事，如果您认为我需要成为编码方面的专家才能理解这一点？

No, you don’t. If you have even basic knowledge of coding or done a little bit of it at the school or maybe at the college level, it should be sufficient to get started.

不，你没有。 如果您甚至具有编码方面的基础知识，或者在学校或大学一级都做了一些编码工作，那么入门就足够了。

I will show you how to apply artificial intelligence or specifically machine learning to an optical or photonics application problem. I have chosen an optical application because my background is in optical engineering. Your’s can be different- it can be in chemistry, physics, material science, biology, or any other. The steps which I am going to explain are transferable to all the research areas. Also, I will be coding in python language as this is commonly used coding language for the application of machine learning.

我将向您展示如何将人工智能或专门的机器学习应用于光学或光子学应用问题。 我选择光学应用是因为我的背景是光学工程。 您的可能会有所不同-化学，物理，材料科学，生物学或任何其他领域都可能不同。 我要解释的步骤可以转移到所有研究领域。 另外，我将使用python语言进行编码，因为这是机器学习应用程序中常用的编码语言。

There are various kinds of machine learning problem categories: Classification, Regression, Clustering, among others. For more details refer to this link. In this article, I am going to show you an example code for a regression problem.

机器学习问题种类繁多：分类，回归，聚类等。 有关更多详细信息，请参考此链接 。 在本文中，我将向您展示一个回归问题的示例代码。

The First step: for a machine learning application/problem is to have/generate a good, clean dataset. But then there is a good chance that the application that you have in mind doesn’t have the dataset freely available online. So, firstly here I briefly show the Photonics problem I am considering and generate the dataset for it.

第一步：对于机器学习应用程序/问题是拥有/生成一个良好的，干净的数据集。 但是，您所想到的应用程序很有可能没有在线免费提供的数据集。 因此，首先，我在这里简要展示我正在考虑的光子学问题，并为其生成数据集。

考虑的问题 (Problem considered)

The left circular structure is how the cross-section of a typical hexagonal Photonic Crystal Fiber (PCF) looks like in an optical/photonics problem. Then I need to decide what are the input parameters for this problem. I have shown five input parameters (in green color) but for this article, I am only using 3 of them (wavelength, diameter, pitch) to keep the problem small and simple. For various combinations of input parameters, I obtain desired output nodes quantities (in orange color) and store these in a pcf_data.xlsx file, as shown below.

左圆形结构是典型的六边形光子晶体光纤(PCF)的横截面在光学/光子学问题中的样子。 然后，我需要确定此问题的输入参数是什么。 我已经显示了五个输入参数(绿色)，但是对于本文，我仅使用其中三个参数(波长，直径，间距)来使问题小而简单。 对于输入参数的各种组合，我获得了所需的输出节点数量(橙色)，并将其存储在pcf_data.xlsx文件中，如下所示。

Again, I have only considered effective index as output to keep the problem simple. If you want you can have more output nodes as shown in the output layer nodes figure. Also, I have only taken 20 combinations of input parameters. Ideally, it is very less for a typical machine learning problem but for this article to demonstrate the method, it is sufficient.

再次，我只考虑有效索引作为输出，以使问题保持​​简单。 如果需要，可以有更多输出节点，如输出层节点图所示。 另外，我仅采用了20种输入参数组合。 理想情况下，对于典型的机器学习问题而言，它要少得多，但是对于本文中演示的方法而言，它就足够了。

For your case, you first need to figure out the problem, and it’s input and output parameters. Then generate the dataset in the CSV /XLSX format similar to shown above. Some sort of simulator/software or even an experimental/fabrication kit would be fine to generate the data.

对于您的情况，您首先需要弄清楚问题及其输入和输出参数。 然后以类似于上图所示的CSV / XLSX格式生成数据集。 某种模拟器/软件甚至实验/制造套件都可以生成数据。

The Second step: is to normalize the generated/collected data. The goal of normalization is to change the values of numeric columns in the dataset to a common scale. Here, we use the MinMaxScalar() function form Scikit-learn to translate each feature individually such that it is in the given range on the training set, e.g. between zero and one. Note: You may need to install, use pip install scikit-learn.

第二步：标准化生成/收集的数据。 标准化的目的是将数据集中的数字列的值更改为通用比例。 在这里，我们使用Scikit-learn的MinMaxScalar()函数来分别转换每个特征，以使其在训练集上处于给定范围内，例如介于零和一之间。 注意：您可能需要安装，请使用pip install scikit-learn 。

import pandas as pdfrom sklearn.preprocessing import MinMaxScaler# Read the data stored in Excel file using pandas librarydf = pd.read_excel('pcf_data.xlsx', sheet_name='Sheet1')# Scale the input data in range (0,1)scaler = MinMaxScaler(feature_range=(0, 1))scaler.fit(df)df_scaler = scaler.transform(df)

All the input and output values are now scaled between zero and one with minimum and maximum value in every column equated to 0 and 1, respectively. These scaled values will become the inputs to the machine learning model. In the end, we will perform the inverse transform to obtain the original values.

现在，所有输入和输出值都在0和1之间缩放，每列中的最小值和最大值分别等于0和1。 这些缩放后的值将成为机器学习模型的输入。 最后，我们将执行逆变换以获得原始值。

The Third step: is to define your input and output parameter columns for the code to understand and split the whole dataset in training and test sets. In our case, the first 3 columns are inputs and the last column is output. The train_test_split()function is used to split the dataset. The test dataset extracted will be used to check the accuracy of the model. Here, 10% of data is stored separately as the test dataset.

第三步：定义代码的输入和输出参数列，以理解和拆分训练和测试集中的整个数据集。 在我们的例子中，前三列是输入，最后一列是输出。 train_test_split()函数用于拆分数据集。 提取的测试数据集将用于检查模型的准确性。 在这里，10％的数据作为测试数据集单独存储。

from sklearn.model_selection import train_test_splitnum_inputs = 3num_outputs = 1X = df_scaler[:,range(0, num_inputs)]y = df_scaler[:,range(num_inputs, num_inputs+num_outputs)]X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1)

The Fourth Step: is to define the machine learning model. Here, I use MLPRegressor() function developed by Scikit-learn to quickly define various layers and parameters of the machine learning model. Shuffling of data is done so that the model is not biased towards any particular inputs. For more details about the various parameters of MLPRegressor() , check the official website link. The .fit() function trains the model for the specified number of epochs/iterations.

第四步：定义机器学习模型。 在这里，我使用MLPRegressor() Scikit-learn开发的MLPRegressor()函数来快速定义机器学习模型的各个层和参数。 进行数据改组，以使模型不会偏向任何特定输入。 有关MLPRegressor()各种参数的更多详细信息，请访问官方网站链接 。 .fit()函数针对指定的纪元/迭代次数训练模型。

from sklearn.neural_network import MLPRegressorepochs = 1000mlp = MLPRegressor(shuffle=True, random_state=1, max_iter=epochs)mlp.fit(X_train, y_train)print("Training set score: ", mlp.score(X_train, y_train))

The Fifth step: is to check the prediction on the test set using the already trained model from the previous step. Here, it is required to do the inverse_transform() at the end to obtain the unscaled values. As the test set is randomly generated during the train_test_split() so you need to carefully check and compare the results obtained from the below function with the actual stored values in the test set.

第五步：使用上一步中已经训练好的模型检查测试集上的预测。 在这里，需要最后执行inverse_transform()以获得未缩放的值。 由于测试集是在train_test_split()期间随机生成的，因此您需要仔细检查并将以下函数获得的结果与测试集中的实际存储值进行比较。

import numpy as npdef prediction(data):    # data should already be scaled    pred_output = mlp.predict(data)    final = np.concatenate((data, pred_output.reshape(-1,1)), axis=1)    return scaler.inverse_transform(final)print(prediction(X_test))

Finally: I show how to obtain the output w.r.t to inputs given by the user and not for the test set. Let us say the user wants to predict the output for these inputs: [diaBYpitch, pitch, wavelength] → [0.7, 0.8, 1.8]. To use our machine learning model we take scaled inputs (defined above) with 4 columns in total for this problem. So here I append zero with the user inputs and then do the scaling using scaler.transfor() as we did above. You could append any integer/number in spite of zero and it won’t affect the output as we are not retraining the model. We only need the fourth column to do the scaling using the above defined scaler.

最后：我展示了如何获取用户给定输入而不是测试集输入的输出wrt。 假设用户想要预测这些输入的输出：[diaBYpitch，pitch，波长]→[0.7、0.8、1.8]。 要使用我们的机器学习模型，我们针对此问题采用总共4列的缩放输入(如上定义)。 因此，在这里我将零添加用户输入，然后像上面一样使用scaler.transfor()进行缩放 。 您可以附加任何整数/数字(尽管为零)，并且不会影响输出，因为我们没有重新训练模型。 我们只需要第四列使用上面定义的scaler 。

def predict_on_user_input(user_input):    user_input = np.append(user_input, 0).reshape(1,-1)    user_input = scaler.transform(user_input)    return prediction(user_input[:,0:num_inputs])output = predict_on_user_input([0.7, 0.8, 1.8])print('output: ', output)

所有代码在一起 (All the code together)

The steps described in this article are transferable to any other research area. But of course, you need to figure out the problem you are interested in and maybe collect the dataset by yourself if it is not available online. I hope this article will help you to get started with using machine learning with python even if you have very little coding experience. Cheers!!

本文中描述的步骤可以转移到任何其他研究领域。 但是，当然，您需要弄清楚自己感兴趣的问题，如果无法在线获取数据集，则可以自己收集。 我希望即使您只有很少的编码经验，本文也将帮助您开始使用python进行机器学习。 干杯！！