深度學習中交叉熵

計算機視覺 (Computer Vision)

Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image and the system is aware of some predetermined set of categories or labels. There are some fixed set of category labels and the job of the computer is to look at the picture and assign it a fixed category label. Convolutional Neural Network (CNN) has gained wide popularity in the field of pattern recognition and machine learning. In our present work, we have constructed a Convolutional Neural Network (CNN) for the identification of the presence of tantalum and niobium fragments in a High Entropy Alloy (HEA). The results showed 100 % accuracy while testing the given dataset.

深度計算機視覺能夠執行對象檢測和圖像分類任務。 在圖像分類任務中，特定系統接收一些輸入圖像，并且系統知道一些預定的類別或標簽集。 有一些固定的類別標簽集，計算機的工作是看圖片并為其分配一個固定的類別標簽。 卷積神經網絡(CNN)在模式識別和機器學習領域獲得了廣泛的普及。 在我們目前的工作中，我們構建了卷積神經網絡(CNN)，用于識別高熵合金(HEA)中鉭和鈮碎片的存在。 測試給定數據集時，結果顯示100％的準確性。

Introduction

介紹

Vision is the most important sense that humans possess. In day to day life, people depend on vision for example identifying objects, picking objects, navigation, recognizing complex human emotions and behaviors. Deep computer vision is able to solve extraordinary complex tasks that were not able to be solved in the past. Facial detection and recognition and detection are an example of deep computer vision. Figure 1 shows the vision coming into a deep neural network in the form of images or pixels or videos and the output at the bottom is the depiction of a human face [1–4].

視覺是人類擁有的最重要的感覺。 在日常生活中，人們依靠視覺來識別物體，拾取物體，導航，識別復雜的人類情感和行為。 深入的計算機視覺能夠解決過去無法解決的非凡復雜任務。 面部檢測，識別和檢測是深度計算機視覺的一個示例。 圖1顯示了以圖像，像素或視頻形式進入深層神經網絡的視覺，底部的輸出是對人臉的描繪[1-4]。

Fig.1. Illustration of the working of Deep Computer Vision圖。1。 深度計算機視覺工作插圖

The next thing should be worth answering to the question, how computer process an image or a video, and how do they process pixels coming from those? The images are just numbers and also the pixels have some numerical values. So our image can be represented by a two-dimensional matrix consisting of numbers. Let’s understand this with an example of image identification i.e. whether the image is of a boy or a girl or an animal. Figure 2 shows that the output variable takes a class label and can produce a probability of belonging to a particular class.

接下來的事情應該值得回答這個問題：計算機如何處理圖像或視頻，以及它們如何處理來自這些圖像或視頻的像素？ 圖像只是數字，像素也有一些數值。 因此，我們的圖像可以由包含數字的二維矩陣表示。 讓我們以圖像識別的示例(即圖像是男孩還是女孩還是動物)來理解這一點。 圖2顯示了輸出變量帶有類別標簽，并且可以產生屬于特定類別的概率。

Fig.2. Image Classification圖2。 影像分類

In order to properly classify the image, our pipeline must correctly tell about what is unique about the particular picture. Convolutional Neural Network (CNN) finds application in the manufacturing and material science domain. Lee et al. [5] proposed a CNN model for fault diagnosis and classification in the manufacturing process of semiconductors. Weimer et al. [6] designed deep convolutional neural network architectures for automated feature extraction in industrial applications. Scime et al. [7] used the CNN model for the detection of in situ processing defects in laser powder bed fusion additive manufacturing. The results showed that the CNN architecture improved the classification accuracy and overall flexibility of the designed system.

為了正確分類圖像，我們的管道必須正確告知特定圖片的獨特之處。 卷積神經網絡(CNN)在制造和材料科學領域得到了應用。 Lee等。 [5]提出了一種用于半導體制造過程中故障診斷和分類的CNN模型。 Weimer等。 [6]設計了用于工業應用中自動特征提取的深度卷積神經網絡體系結構。 Scime等。 [7]使用CNN模型來檢測激光粉末床熔融增材制造中的原位加工缺陷。 結果表明，CNN體系結構提高了設計系統的分類準確性和整體靈活性。

In the present work, we have designed the CNN architecture for detecting the trace of tantalum and niobium in the microstructure of high entropy alloy (HEA). In 1995, Yeh et al. [8] firstly discovered the high entropy alloys, and in 2004 Cantor et al. [9] coined high entropy alloy as a multi-component system. HEAs are generally advanced alloys and novel alloys which are consist of 5–35 at.% where all the elements behave as principal elements. In comparison to their conventional alloys, they possess superior properties like high wear, corrosion resistance, high thermal stability, and high strength. Zhang et al. [10–11] listed down the various parameters for the parameters for fabrication of HEAs which are shown in the below equations:

在當前的工作中，我們設計了CNN體系結構，用于檢測高熵合金(HEA)微觀結構中的痕量鉭和鈮。 1995年，Yeh等人。 [8]首先發現了高熵合金，2004年Cantor等人。 [9]創造了高熵合金作為多組分系統。 HEA通常是高級合金和新型合金，由5–35 at。％的成分組成，其中所有元素均作為主要元素。 與常規合金相比，它們具有優異的性能，如高耐磨性，耐腐蝕性，高熱穩定性和高強度。 張等。 [10-11]列出了制造HEA的各種參數，這些參數如下式所示：

HEAs find application in various industries like aerospace, submarines, automobiles, and nuclear power plant industries [12–14]. HEAs are also used as a filler material for the micro-joining process [15]. Geanta et al. [16] carried out the testing and characterization of HEAs from AlCrFeCoNi System for Military Applications. It was observed that at the melt state, the microstructure of HEAs has frozen appearance as shown in Figure 3.

HEA在航空航天，潛艇，汽車和核電廠等各種行業中都有應用[12-14]。 HEA還用作微連接過程的填充材料[15]。 Geanta等。 [16]進行了軍事應用AlCrFeCoNi系統的HEA的測試和表征。 觀察到，在熔融狀態下，HEA的微觀結構具有凍結外觀，如圖3所示。

Fig.3. The appearance of frozen microstructure圖3。 冷凍組織的外觀

Material and Methods

材料與方法

Geanta et al. [17] fabricated biocompatible FeTaNbTiZrMo HEAs. In our study, we have used microstructure data from their research. The obtained microstructure is shown in Figures 4 and 5. Data collection is the process of gathering and measuring information from countless different sources. In order to use the data we collect to develop practical artificial intelligence (AI) and machine learning solutions, it must be collected and stored in a way that makes sense for the business problem at hand. Since we had a shortage of images, so we first did Image Augmentation.

Geanta等。 [17]制作了生物相容的FeTaNbTiZrMo HEA。 在我們的研究中，我們使用了他們研究的微觀結構數據。 獲得的微觀結構如圖4和5所示。數據收集是從無數不同來源收集和測量信息的過程。 為了使用我們收集的數據來開發實用的人工智能(AI)和機器學習解決方案，必須以對眼前的業務問題有意義的方式來收集和存儲數據。 由于圖像不足，因此我們首先進行圖像增強。

Fig.4.Undissolved Ta and Nb fragments in the FeTaNbTiZrMo alloy [17].圖4. FeTaNbTiZrMo合金中未溶解的Ta和Nb碎片[17]。 Fig.5.Undissolved tantalum fragment in the FeTaNbTiZrMo alloy.圖5 FeTaNbTiZrMo合金中未溶解的鉭碎片

Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize. Image data augmentation is supported in the Keras deep learning library via the Image Data Generator class. So, input data consists of two images. As we know that we can’t train our deep neural network with only two images because that would result in the over-fitting of the model. Over-fitting a model basically means that our model will give the best score on training data but not on testing or validation data or the data that it has not seen before. So such an over-fitted model will be of no use to train our model effectively, we will make more images with the help of these input images. We will achieve this by Image Augmentation.

圖像數據增強用于擴展訓練數據集，以提高模型的性能和泛化能力。 Keras深度學習庫通過圖像數據生成器類支持圖像數據增強。 因此，輸入數據包含兩個圖像。 眾所周知，我們無法僅使用兩個圖像來訓練我們的深度神經網絡，因為這將導致模型的過度擬合。 過度擬合模型基本上意味著我們的模型將在訓練數據上給出最佳分數，而在測試或驗證數據或之前從未見過的數據上則給出最佳分數。 因此，這種過度擬合的模型對于有效地訓練我們的模型將毫無用處，我們將在這些輸入圖像的幫助下制作更多圖像。 我們將通過圖像增強來實現。

Fig.5.Undissolved tantalum fragment in the FeTaNbTiZrMo alloy.

圖5 FeTaNbTiZrMo合金中未溶解的鉭碎片

We can use the Image Data Generator class to achieve this. First, we will make the object of this class. After that we will provide some parameters that are basically the fluctuations or feature that we want to provide the image like luminous intensity, width shift range, height shift range, etc. and we can iterate over the directory where the images are kept in, by providing the path in the function. In this way, we can generate numerous data. In this project, we have generated approximately 3000 images for each image.

我們可以使用Image Data Generator類來實現這一點。 首先，我們將成為此類的對象。 之后，我們將提供一些基本參數，這些參數基本上是我們想要提供圖像的波動或特征，例如發光強度，寬度偏移范圍，高度偏移范圍等。我們可以通過以下方式遍歷保存圖像的目錄：在函數中提供路徑。 這樣，我們可以生成大量數據。 在此項目中，我們為每個圖像生成了大約3000張圖像。

We created two datasets for the training and testing purpose. Python programming was used for the development of the code required for constructing the Convolutional Neural Network architecture. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.

我們為訓練和測試目的創建了兩個數據集。 Python編程用于開發構建卷積神經網絡體系結構所需的代碼。 卷積神經網絡(ConvNet / CNN)是一種深度學習算法，可以吸收輸入圖像，為圖像中的各個方面/對象分配重要性(可學習的權重和偏差)，并能夠區分彼此。 與其他分類算法相比，ConvNet中所需的預處理要低得多。 在原始方法中，過濾器是手工設計的，經過足夠的培訓，ConvNets可以學習這些過濾器/特征。

Results and Discussions

結果和討論

The augmented image of the microstructure is shown in Figure 6.

顯微組織的放大圖如圖6所示。

Fig.6.Augmented images of the microstructure圖6顯微組織的放大圖像

Model is compiled with loss-Binary cross-entropy and metrics-accuracy and optimizer is adam. To prevent the model from Over-fitting, early stopping and model checkpoints are used so as to prevent a model from overtraining. Early Stopping is basically a process in which the model is stopped training when it doesn’t undergo any improvement. This parameter is provided in early stopping while making its object. This parameter is known as Patience. Metrics and mode are also provided as a parameter to test the model on the basis of that. Suppose metrics are value accuracy and mode is maximum, so when the model will not show any improvement (increment in value accuracy), it will wait till the patience parameter and after that, it will stop. The results were quite satisfactory when we trained our model against unlabelled images. As we can see in Figure 7, during prediction, almost every actual value is matched with predicted value so our model has been trained effectively.

使用損失-二進制交叉熵和度量準確性來編譯模型，并且優化器是亞當。 為了防止模型過度擬合，使用了早期停止和模型檢查點，以防止模型過度訓練。 基本上，“早期停止”是一個過程，其中模型在未進行任何改進時就停止訓練。 在使其成為對象的早期停止中提供此參數。 此參數稱為耐心。 度量和模式也作為參數提供，以在此基礎上測試模型。 假設度量標準是值準確性，并且模式是最大，那么當模型沒有顯示出任何改善(值準確性增加)時，它將等待直到耐心參數，然后才停止。 當我們針對未標記圖像訓練模型時，結果非常令人滿意。 如圖7所示，在預測期間，幾乎每個實際值都與預測值匹配，因此我們的模型已得到有效訓練。

Fig.7. The predicted value matches the Actual Value圖7。 預測值與實際值匹配

The graphs in Figure 8 show the changes in metrics while training. As we can see, the model loss is getting lower as the epoch increases and accuracy is increasing as the epoch increases.

圖8中的圖形顯示了訓練期間指標的變化。 如我們所見，隨著歷時的增加，模型損失越來越小，隨著歷時的增加，模型的準確性也越來越高。

Fig.8.Graph showing model loss and model accuracy圖8顯示模型損失和模型準確性的圖

Conclusion

結論

It can be concluded that the current research is basically about image processing and classification, in which we first collected data due to a shortage of data, we did data augmentation to train our deep learning model, after that, we implemented our model architecture and compilation is done. After training, the results are shown. It is observed that the predicted value matches the actual value resulting in good accuracy for the image classification of the fragments present in HEAs.

可以得出結論，當前的研究基本上是關于圖像處理和分類的，其中我們首先由于數據不足而收集數據，我們進行了數據擴充以訓練我們的深度學習模型，之后，我們實現了模型架構和編譯已經完成了。 訓練后，將顯示結果。 可以看出，預測值與實際值匹配，從而導致HEA中存在的碎片的圖像分類具有良好的準確性。

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[17]。 Victor Geanta，Ionelia Voiculescu，Petrica Vizureanu和Andrei Victor Sandu(2019年9月21日)。 用于醫療應用的高熵合金[在線優先]，IntechOpen，DOI：10.5772 / intechopen.89318。

翻譯自: https://medium.com/towards-artificial-intelligence/deep-computer-vision-for-the-detection-of-tantalum-and-niobium-fragments-in-high-entropy-alloys-5d0c2d8c988a

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