arcface loss tensorflow DeepFace: Closing the Gap to Human-Level Performance in Face Verification Training with cosface or arcface generally is classification, so the final loss was CrossEntropy. Although you could access input data in this gs dir: KaggleDatasets(). autodoc. However, recent research has shown that distinct networks’ features can be directly mapped with little performance penalty (median 1. или import tensorflow as tf from keras import backend as K. We've been tackling buzz words in the tech industry recently. predict(img)) face_detector = facerec. A form of signal processing where the input is an image. Guo, N. predict method. You can also find reimplementations in TensorFlow, PyTorch and Caffe. Loss function for 1d segment estimation. ArcFace的loss如下： ARC Centre of Excellence for Robotic Visionwww. roboticvision. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. 18 – ‎19회 인용 [참고 논문] 1. 1 、人脸口罩检测开源PyTorch、TensorFlow、MXNet等全部五大主流  After trained by ArcFace loss on the refined MS-Celeb-1M from scratch, our most of deep learning frameworks such as Caffe [24], Pytorch [34], Tensorflow, etc. 04571179 Login with Facebook. A truly open source deep learning framework suited for flexible research prototyping and production. 1109/CVPR. I have a model and I've implemented a custom loss function something along the lines: def custom_loss(labels, predictions): global diff #actual code uses decorator so no globals diff = On one hand, we incorporate the SOTA ArcFace loss [deng2018arcface] to maintain the feature discriminability on easy samples. ArcFace loss：Additive Angular Margin Loss（加性角度间隔损失函数），对特征 向量和权重归一化，对θ加上角度间隔m，角度间隔比余弦间隔在对角度的影响  2019년 10월 9일 ArcFace: Additive Angular Margin Loss for Deep Face Recognition 프레임워크 에서 구현하기가 너무 쉬움 (ex, MxNet, Pytorch, Tensorflow). 1. Testing Large-scale Image Test Set: We take the Trillion-pairs dataset [5] as our large-scale image test set. 3%, heatmap shows that model is distracted by the background. ly/2ZXdbqj Books:----- Manning - TensorFlow 2. better network and loss design) to improve the performance, but external datasets and models are not allowed. 参考文献. Image Retrieval by Similarity using Tensorflow and Keras This tutorial will cover all the details (resources, tools, languages etc) that are necessary for image retrieval. Cookies op beslist. org/abs/1801. train_tool import arcface_loss,read_single_tfrecord,average_gradients from core import Arcface_model,config import time import os Sep 15, 2019 · ArcFace: Additive Angular Margin Loss for Deep Face Recognition and Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification GitHub insightface GitHub ECN 侯正罡 @ ArronHZG 语义分割中的 Attention 机制 一作李夏，北大林宙辰组，本科北邮 Attention 机制的分析：实际上是自相关，通过自 使用tensorflow训练模型时可能出现nan的几种情况 最近在做基于MTCNN的人脸识别和检测的项目，在训练模型的过程中总是会不定时地出现损失值为 nan 的情况，Debug了好久终于找到了问题所在，这里总结以下可能出现nan的几种情况： Acc 和 Loss 在最后震荡是训练数据中很常见的现象。 用一句话来说，你的Loss function有一个局部最优解， 但当你用梯度下降算法去逼近这个最优解时，总会出现在最优解附近不断徘徊的情况。 通俗的解释，把你的loss function想象成一个山谷，你是那个梯度下降算法。 calssification loss的目的是使类别分类尽可能正确；bounding box regression loss的目的是使预测框尽可能与GT框匹对上。 Focal loss. Jun 11, 2018 · Even i m the one among those who have faced this problem, i actually tried training the nn by various faces at different distance from cam, and different at positions, the brightness also becomes one of the constraint, it worked for me but the scenario becomes worst when the face is recognised from a far distance, it would again recognize the unknown faces as some know face from dataset. For ARM processor architecture, you need to install TensorFlow from source. - luckycallor/InsightFace-tensorflow. In order to build our deep learning image dataset, we are going to utilize Microsoft’s Bing Image Search API, which is part of Microsoft’s Cognitive Services used to bring AI to vision, speech, text, and more to apps and software. org Oct 13, 2020 · The loss functions include Softmax, SphereFace, CosineFace, ArcFace, Sub-Center ArcFace and Triplet (Euclidean/Angular) Loss. run(total_loss, feed_dict={x:输入, y:标签}) 解析人脸识别中cosface和arcface（insightface）的损失函数以及源码 人脸识别-SphereFace 人脸识别损失函数笔记 人脸识别辅助损失函数：centerloss 【Pytorch】 Dice系数与Dice Loss损失函数实现 Pytorch损失函数之BCELoss与BCEWithLogitsLoss 损失函数 DiceLoss 的 Pytorch、TensorFlow 实现 CSDN问答为您找到度量学习中三元组损失不收敛(loss无法下降到margin以下，样本的降维输出聚在一起)相关问题答案，如果想了解更多关于度量学习中三元组损失不收敛(loss无法下降到margin以下，样本的降维输出聚在一起)、tensorflow、深度学习、人工智能技术问题等相关问答，请访问CSDN问答。 TensorFlow是一个端到端开源机器学习平台。它拥有一个全面而灵活的生态系统，其中包含各种工具、库和社区资源，可助力研究人员推动先进机器学习技术的发展。在 TensorFlow机器学习框架下，开发者能够轻松地构建和部署由机器学习提供支持的应用。 Analyze the loss function and source code of cosface and arcface (insightface) in face recognition There are almost two developments in face recognition in recent years. 前言. This paper studies a variety of loss functions and output layer regularization ArcFace(Additive Angular Margin Loss) AM Softmaxも ArcFace も基本的には同じなのですがマージンの取り方が異なり、 に対してマージンを取ります。ArcFaceの論文内にある擬似コードをもとにそのままTensorFlowで実装してます。 Aug 25, 2019 · 4、Arcface 前面的softmax Loss 没有考虑类间距离， center loss 学习类中心，使类内紧凑，但没有类间可分。triplet loss 收敛较慢。因此就产生了sofmax的变形loss，如L-Softmax、SphereFace、Arcface。 ArcFaceがないときは通常の分類ならいいが、cos類似度の値に差が出づらくなってしまい,閾値で切ることが難しくなってしまった. Common cross-entropy loss used in the multi-class classification doesn’t guarantee to learn such discriminative features as the objective is just classifying training data successfully. Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. model. Dataset size is a big factor in the performance of deep learning models. This doesn't usually mean much unless you've trained lots of similar models with the exact same loss. 003992, test acc: 0. EncNetLoss (se_loss=True, se_weight=0. Hidden Technical Debt in Machine Learning Systems. 使用tensorflow训练模型时可能出现nan的几种情况 最近在做基于MTCNN的人脸识别和检测的项目，在训练模型的过程中总是会不定时地出现损失值为 nan 的情况，Debug了好久终于找到了问题所在，这里总结以下可能出现nan的几种情况： Sep 04, 2018 · ArcFace: Additive Angular Margin Loss for Deep Face RecognitionJ Deng 저술 – ‎2018. Center loss: structure – Without classification loss – collapses – Final loss = Softmax loss + λ Center loss Classify Embedding Softmax Loss CNN λ Pull tensorflow 实现简单的人脸检测及可视化 835 2018-11-02 先上tensorboard效果图吧 graph图里边可以看到计算时间和内存的消耗最大的地方就是梯度计算了 用自己的笔记本CPU跑了一晚上，一共15000steps，在验证集上的最终召回率和准确率都还不错 过程中遇到最大的问题就是数据类型总是不匹配，搞得人头大 在 The situation is even more dire on 8x or 16x GPU DGX NVIDIA products. ai/face-recognition-arcface/. For this problem, I used a deep CNN w/ ArcFace loss to The installation instructions of TensorFlow are written to be very detailed onTensorFlowwebsite. It is common to use the softmax cross-entropy loss to train neural networks on classification datasets where a single class label is assigned to each example. However, recent research has shown that distinct networks' features can be directly mapped with little performance penalty (median 1. 2Install リンクではMacro F1をロス関数に適用出来るようにしたMacro Soft F1 LossのKeras実装があるのですが、PyTorch版を実装しました。 sigmoid + binary crossentropyと比較するとロスと正解率が結びつきやすいのは利点ですが、バッチサイズを十分に取らないといけなさそうな 3. AlexNet-v1, AlexNet-v2, VGG-16, VGG-16-BN, GoogLeNet, Inception-v3, ResNet-50, DenseNet-121 and LightCNN-29, Center-loss, SphereFace, CosFace and ArcFace) for face recognition under different frameworks (i. Mar 02, 2019 · ‫پور‬ ‫اخوان‬ ‫علیرضا‬ One-Shot Learning: Face Recognition ArcFace 96. 9% reduction foo@bar: ~ $ python3 -m facelib train train_images/ lotr Current pipeline: ssd_int8_cpu, mobilenetv2_fp32_cpu, densenet_fp32_cpu Classifier named ` lotr ` succesfully trained and saved. (bboxes = facedetector. The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) TensorFlow: InsightFace_TF  https://github. 人脸识别项目，网络模型，损失函数，数据集相关总结 1. Beslist. platform ai 2,355 views. FaceNet: A Unified Embedding for Face Recognition and Clustering F Schroff 저술 – 2015. 947966, train loss: 0. ArcFace는 주어진 Algorithm 1의 여러 줄의 코드만 필요하며, 계산적인 그래프 기반 딥러닝 프레임워크에서 구현하기가 너무 쉬움 (ex, MxNet, Pytorch, Tensorflow). Transfer Learning with Your Own Image Dataset¶. Dec 27, 2018 · MNIST learned-features under supervision of ArcFace Loss Phuoc Pham. 0 in Action - Packt - TensorFlow. However, it has been shown that modifying softmax cross-entropy with label smoothing or regularizers such as dropout can lead to higher performance. Jan 10, 2019 · TensorFlow/Theano tensor. Training networks for face recognition is very complex and time-consuming. Dlib’s Facial Landmark Detector Dlib has a very good implementation of a very fast facial landmark detector. ¶ TPU must read and store data from/to Google cloud storage. nl. with the ArcFace loss. ly/2ZRCWso - ImageNet-trained CNNs bias towards texture - https://bit. 996, Test Error: 90. The TensorFlow layers module provides a high-level API that makes it easy to construct a neural network. Model không sử dụng hàm loss phổ biến là Cross-Entropy, mà sử dụng hàm loss Kullback-Leibler Divergence. They are from open source Python projects. When using larger backbones like InceptionV3 or SE-ResNeXt-50, they noticed overfitting, so they switched to lighter networks like ResNet-34, BN-Inception and DenseNet-121. See full list on data-flair. 5 Openface (Torch) 46. 06969877 -0. Light Baseline Model. Source code of the winning method in Track 1 and Track 3 at the AI City Challenge Workshop in CVPR 2018. This processing may include image restoration and enhancement (in particular, pattern recognition and projection). 95) • Optical Character Recognition J. Softmax is just a function and not a loss. Our method, ArcFace, was initially described in an arXiv technical report. No matter what the performance of an algorithm on LFW, it should not be used to conclude that an algorithm is suitable for any commercial purpose. Cross Entropy Loss. It The GAN loss helps to ensure the visual quality of the synthesized faces are maintained. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. QuanCNN. 这就是softmax loss函数， 表示全连接层的输出。 在计算Loss下降的过程中，我们让 的比重变大，从而使得log() 括号内的数更变大来更接近1，就会 log(1) = 0，整个loss就会下降。 import tensorflow as tf from train. 7541 val Loss: 0. 9977 mean accuracy. Tensorflow Model Compression In Keras + Tensorflow; A newer Tensorflow 2. Aug 17, 2020 · CNN , R-CNN , Fast R-CNN , Mask R-CNN Bumps in Arcface loss function’s accuracy and accuracy decrease without overfitting Guide to Visual Recognition Datasets for Deep Learning with Python Code How Goodhart’s Law Can Save Machine Learning Research MouldCil: 3D representation for your stencil Sep 28, 2018 · Overall, the matlab code implementation is still very concise, which is much more convenient than Pytorch and tensorflow, but there is also a problem. With final fc layer output as features, the most powerful single model can reach 0. 提出乘性 larger-margin softmax loss, 相较与加性 larger-margin softmax loss(如 AM-softmax, ArcFace), 训练难度更大(需要用到退火训练方法, 见原文 5. ⇨ Developed Person Re-identification model using Aligned ReID and In defense of triplet loss for calculating the time spent by the customer in the shop. 4. It provides methods that facilitate the creation of dense (fully connected) layers and convolutional layers, adding activation functions, and applying dropout regularization. 7068, Test Accuracy: 0. I am trying to do a transfer learning with ResNet50V2 model using triplet loss function. Empire Outlets is just steps from the Staten Island Ferry on Staten Island. We employ ArcFace [5] as our loss func-tion, which is one of the top-performing methods for deep face recognition. 55% for ArcFace**. 4690--4699. ,  The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) python -u train. ext. You will be guided through all the steps and concepts, starting from the basic ones like setting up the right tools and frameworks to the more advanced topics related to the A FLEXIBLE AND EFFICIENT LIBRARY FOR DEEP LEARNING. Resnet18 Github Resnet18 Github Caffe. 28). This paper presents a new loss function, namely Rectified Wing (RWing) loss, for regression-based facial landmark localisation with Convolutional Neural Networks (CNNs). 12:14. Tensorflow Lite Face Recognition tensorflow keras face-recognition facenet triplet-loss face-verification sphereface contrastive-loss arcface angular-softamx-loss cosface additive-angular-margin-loss large-margin-cosine-loss Updated Nov 13, 2020 Dec 17, 2019 · Efficient and robust facial landmark localisation is crucial for the deployment of real-time face analysis systems. 前提・実現したいこと[Keras]MobileNetV2+ArcFaceを使ってペットボトルを分類してみた!上記URLのサイト様のコードを参考に、自前の画像で分類を行いたいと考えております。 発生している問題・エラーメッセージ途中まではサイト様のコード通りに動いたのですが、途中のコードでエラーが発 First, we conduct extensive experiments using the most commonly used architectures (GoogLeNet, BNInception, ResNet50) on the most commonly used datasets (CUB200-2011, CARS196, Stanford Online Products) using 10 different loss functions (Contrastive, Triplet, LiftedStructure, NPair, ProxyNCA, ArcFace, Margin, MultiSimilarity, SoftTriple ⇨ Developed Face recognition system using ArcFace objective function and FaceNet for visual loyalty product. OR. FaceNet: A Unified Embedding for Face Recognition and ClusteringF Schroff 저술 – […] Face2face Pytorch 那arcface loss是如何实现更好的分类效果呢？ 其实就是在θ上加上一个角度，让两个向量分的更开（在arcface之前还有A-softmaxloss和AM-softmaxloss，前者是增加角度乘积系数方式来增大角度分类，但倍角公式求导不变，后者是减小相似度系数来增加向量之间的距离，但 calssification loss的目的是使类别分类尽可能正确；bounding box regression loss的目的是使预测框尽可能与GT框匹对上。 Focal loss. -- Developed time-series based signature recognition models using Contrastive loss, Triplet loss and Arcface loss (AUC 0. 63 70. com/omoindrot/tensorflow-triplet-loss ArcFace: Additive Angular Margin Loss for Deep Face Recognition, J. The ArcFace loss (Deng et al. 文章内相关链接，可以直接点解页面 链接：点这里 几篇知乎 《人脸识别方向有哪些大牛？目前的发展如何？ - 知乎》 o从认知心理学的角度来说，人脸识别方向有哪些… Sep 15, 2019 · ArcFace: Additive Angular Margin Loss for Deep Face Recognition and Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification GitHub insightface GitHub ECN 侯正罡 @ ArronHZG 语义分割中的 Attention 机制 一作李夏，北大林宙辰组，本科北邮 Attention 机制的分析：实际上是自相关，通过自 Local feature model learned via policy gradient. GitHub Gist: instantly share code, notes, and snippets. losses. The journey is not as smooth as I thought. MultiGrain: A unified image embedding for classes and instances. 9020 Epoch 1/24 ----- train Loss: 0. Mar 13, 2020 · This loss function is very simple to implement when the output of our model is a Tensorflow distribution object. 4) combination and it worked. Xue, and S. For example, when GIOU is used as a loss, the network calculation loss is very slow and cannot be carried forward. Specifically used for EncNet. The exact API will depend on the layer, but many layers (e. Understanding the impact of video quality on user engagement. 4, weight=None, ignore_index=-1, **kwargs) [source] ¶ Bases: sphinx. Understanding arcface, sphereface, and their differences neural-network keras tensorflow. e. 思路1 ：Metric Learning: Contrastive Loss, Triplet loss 及相关 sampling method。 思路2 ：Margin Based Classification: 包含 Softmax with Center loss, Sphereface, NormFace, AM-softmax (CosFace) 和 ArcFace. softmax loss provides roughly separable feature embedding but produces noticeable ambiguity in decision boundaries, while the proposed ArcFace loss can obviously enforce a more evident gap between the nearest classes. This implementation aims at making both usage of pretrained model and training of your own model easier. The detailed network configuration of our light baseline model is summarised in Table 3. contrib. 2730 Acc: 0. """ def __init__ (self, use_running_mean = False, bce_weight = 1, dice_weight = 1, eps = 1e-6, gamma = 0. Updated often at VancouverIsAwesome. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 3x3 window. 2. Loading Unsubscribe from Phuoc Pham? TensorFlow, and Keras tutorial - Duration: 20:34. acos(). Keras API reference / Layers API / Pooling layers Pooling layers. An interpretation of this class vector is that it outputs the possible classifications for an image, along with how confident the model about each class. Mar 24, 2020 · Usage (python) from facelib import facerec import cv2 # You can use face_detector, landmark_detector or feature_extractor individually using . A Guide to TF Layers: Building a Convolutional Neural Network . https://arxiv. 3 Dataset Collection. 该Focal loss损失函数出自于论文《 Focal Loss for Dense Object Detection 》，主要是解决正负样本之间的不平衡问题。通过降低easy example中的损失值 Kaggle的座头鲸识别挑战比赛在最近落下帷幕，全球共2131个团队参加了比赛。 这是近期Kaggle上颇受欢迎的一次竞赛，常用的分类方法无法处理大量的无标注数据，只有对传统的方法进行创新，才能够获得高分。 Softmax loss. Fast and Easy, get the right part fast. However, there are something need to be considered. 6908, Train Accuracy: 0. 07698. 이중 SOP 데이터는 ArcFace loss 를 사용한다. 00183518 -0. HoG Face Detector in Dlib. Regularization penalties are applied on a per-layer basis. 1https://github. The analysis suggests Jul 05, 2019 · 2019. Face Recognition can be used as a test framework for several face recognition methods including the Neural Networks with TensorFlow and Caffe. The DNNs were trained on a modified version of Ms Celeb dataset (ms1m). " ArcFace: Additive Angular Margin Loss for Deep Face Recognition" Published in   2019年8月25日 从传统的softmax loss到cosface, arcface 都有这一定的提高。 1、softmax loss. 01 Oct 16, 2020 · The loss functions include Softmax, SphereFace, CosineFace, ArcFace, Sub-Center ArcFace and Triplet (Euclidean/Angular) Loss. g. 1% FAR= 0. PyTorch, TensorFlow and Caffe) in two folds. com 3. , 2019) directly maximizes decision boundary in angular (arc) space based on the L 2-normalized weights and features. Subsequently, I wrote a series of posts that utilize Dlib’s facial […] Jan 09, 2018 · DISCLAIMER: Labeled Faces in the Wild is a public benchmark for face verification, also known as pair matching. Furthermore, contrary to the works in [ 18, 19], ArcFace does not need to be combined with other loss functions in order   but I doubt if that is the problem. 08% compared to S3FD*. ‫پور‬ ‫اخوان‬ ‫علیرضا‬ One-Shot Learning: Face Recognition [Deng et al. Billion-scale semi-supervised learning for image classification. Module): """ Combination BinaryCrossEntropy (BCE) and Dice Loss with an optional running mean and loss weighing. handong1587's blog. The proposed ArcFace has a clear geometric interpretation due to the ex-act correspondence to the geodesic distance on the hyper- See full list on tensorflow. After training them on Aff-Wild2, we re-trained them on each of the examined databases. A list of available losses and metrics are available in Keras’ documentation. Aug 31, 2019 · CAM Code (Tensorflow) In general, for each of these networks we remove the fully-connected layers before the final output and replace them with GAP followed by a fully-connected softmax layer. 人脸识别的常用loss及tensorflow实现 在人脸识别中，模型的提升主要体现在损失函数的设计上，损失函数会对整个网络的优化有着导向性的作用。从传统的softmax loss到cosface, arcface 都有这一定的提高。 1、softmax loss \[loss = -\frac{1}{m}\sum_{i=0}^m log\frac{e^{W^T_{y_i} + b また、Contrastive Loss関数を使用するには、ペア画像のラベル付けが必要です。そのため、分類したいクラスが多い場合、 ペアの組み合わせが非常に多くなる という問題があります。 このような問題をCenter Loss関数で解決します。 – Facenet, not original (TensorFlow) LFW, % Megaface 92-Our (Torch) 99. Softmax Function. I am working on an open-set classification problem where I have 21 known classes and 4 unknown classes, which were not part of the training data. ImageNet has over one million labeled images, but we often don’t have so much labeled data in other domains. 1), 效果而言, 也是加性 loss 更好. Model compression, sees mnist cifar10. ArcFace 论文链接：ArcFace: Additive Angular Margin Loss for Deep Face Recognition, 发表时间：2018. ちなみにResNet18の最終層をコメントアウトしても512次元の特徴が取れるのだが、 このケースではテスト精度は84. Sep 04, 2018 · 1. Thực chất công thức của Cross-Entropy và Kullback-Leibler Divergence có mối liên hệ với nhau, KL divergence dùng để kiểm định độ khác nhau giữa 2 phân bố xác suất. _MockObject. Looking at GPU utilization, as measured by nvtop, the GPUs do not seem to be buzy (on a 8 way DGX1 Station running workload). However, it is worth noting that the usage of softmax loss still gives the most accurate representations if the dataset is very large. 2, nclass=19, aux=False, aux_weight=0. The following are 30 code examples for showing how to use tensorflow. 0+ (ResNet50, MobileNetV2). The first is for mobile devices, and the second is to improve the loss function to make the trained model more effective. 性能高，易于编程实现，复杂性低，训练效率高; ArcFace直接优化geodesic distance margin(弧度)，因为归一化超球体中的角和弧度的对应。 为了性能的稳定，ArcFace不需要与其他loss函数实现联合监督，可以很容易地收敛于任何训练数据集。 缺点：W模型很大 用tensorflow训练网络，出现了loss=nan，accuracy总是一个固定值的情况，不管我用哪台电脑，如何训练，测… epoch: 100, train acc: 0. It has See full list on github. 98)-- Developed image-based signature verification models using Siamese network, 2-channel CNN architectures (AUC 0. Deep learning Insight Face in TensorFlow Tasks. 09794946 0. TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation. Insaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui & Abdelmalik Taleb-Ahmed. it has a nice probabilistic interpretation. 또한 GeM (generalized mean-pooling)을 사용하는데 이는 백본의 Stage 4 영역에 사용되는 pooling을 대체한다. One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power. Caffe is a deep learning framework made with expression, speed, and modularity in mind. N/A: N/A: 20-06-02 0. 0 can be a solution. function(). Center loss Idea: pull points to class centroids 47. importer. IR task에서는 이런 방식이 더 좋은 성능을 얻었다. リンクではMacro F1をロス関数に適用出来るようにしたMacro Soft F1 LossのKeras実装があるのですが、PyTorch版を実装しました。 sigmoid + binary crossentropyと比較するとロスと正解率が結びつきやすいのは利点ですが、バッチサイズを十分に取らないといけなさそうな J. ArcFace Video Demo. November 2019 https://mc. And a contributor of Tensorflow said that tensorflow 2. Remember me Apr 09, 2018 · How to (quickly) build a deep learning image dataset. , 97% to 99%. Nevertheless, it is remained a challenging computer vision problem for decades […] Jun 11, 2018 · Even i m the one among those who have faced this problem, i actually tried training the nn by various faces at different distance from cam, and different at positions, the brightness also becomes one of the constraint, it worked for me but the scenario becomes worst when the face is recognised from a far distance, it would again recognize the unknown faces as some know face from dataset. A loss function is one of the two arguments required for compiling a Keras model: from tensorflow  tf2 keras losses Fraction of the training data to be used as validation data. We first systemically analyse different loss functions, including L2, L1 and smooth L1. 18536897 0. We take the top 8000 features by score. ArcFace: Additive Angular Margin Loss for Deep Face Recognition (arXiv:1801. Improved recognition rate by 2. BinaryNet. com Jan 31, 2019 · I only compared ArcFace loss with Softmax loss and the improvement was quite noticeable. Benchmarking neural network robustness to common corruptions and perturbations. SIATMMLAB TencentVision. Regularizers allow you to apply penalties on layer parameters or layer activity during optimization. org A Practical Introduction to Deep Learning with Caffe Peter Anderson, ACRV, ANU Qiita is a technical knowledge sharing and collaboration platform for programmers. 用pytorch实现arcface loss，从训练到部署（1）目标和思路为什么选择PyTorch 目标和思路 本人是深度学习的小白一枚，以前一直用TensorFlow玩自己的数据集，最近突发奇想，想真正搞一个图像检索应用（100万级别的数据量，15万类），把训练和部署等流程走一遍。 PS： Github 复现项目 MuggleWang/CosFace_pytorch（基于 PyTorch 实现），Github 复现项目 yule-li/CosFace（基于 Tensorflow 实现）。 12. ArcFace的loss如下： Softmax loss. On the other hand, our order loss minimizes the distance between the expectations of similarity distributions from the negative and positive pairs, which helps control the overlap between positive and negative pairs. Thanks to Andrew Ng’s online course and several books, I have a basic understand of the theory, however, when I try to apply it in real-life projects, the syntax and api of Tensorflow sometimes confused me (Maybe I wasn’t spending enough time with it). Deep learning framework by BAIR. 0 will support CUDA 11. 6. losses) . In the past, various alternative loss functions were proposed like Triplet-loss, Center-loss, AM-SoftMax, CosFace, SphereFace, and ArcFace. The Memory utilization seems reasonable (< 50% except the GPU # 0). ArcFace unofficial Implemented in Tensorflow 2. You can vote up the examples you like or vote down the ones you don't like. 2. You can read more about HoG in our post. 4. Our solution is based on [MS1MV2+DeepGlintAsian, ResNet100, ArcFace loss]. 09212 (2019)) View On GitHub; Caffe. 1-minute papers. Epoch 0/24 ----- train Loss: 0. py --network vargfacenet --loss arcface --dataset retina gpu num: 1 prefix . It is an open source artificial intelligence library, using data flow graphs to build models. mean_squared_error, optimizer = 'adam', metrics = ['accuracy']). Deng, J. 0 open source license. ArcFace: Additive Angular Margin Loss for Deep Face Recognition @article{Deng2019ArcFaceAA, title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition}, author={Jiankang Deng and Jia Guo and Stefanos Zafeiriou}, journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019}, pages={4685-4694} } Pre-trained models and datasets built by Google and the community Loss is the the thing that you want the model to optimize down. ktjonsson/keras-ArcFace trachpro/arcface-tf2 Centre loss penalises Using negative log-likelihoods of TensorFlow Distributions as Keras losses. View On GitHub; Caffe. Deep Learning Face Representation from Predicting 10,000 Classes. 7090 val Loss: 0. Nov 17, 2019 · Verification: MobileFaceNet + Arcface; This project is using Fast-MTCNN for face detection and TVM inference model for face recognition. If you can't use large batch size(>128  This repository contains code for ArcFace, CosFace, and SphereFace based on ArcFace: Additive Angular Margin Loss for Deep Face Recognition implemented   2020-04-27 : InsightFace pretrained models and MS1M-Arcface are now specified as The loss functions include Softmax, SphereFace, CosineFace, ArcFace, Caffe: CombinedMargin-caffe; Tensorflow: InsightFace-tensorflow; TensorRT:  ArcFace unofficial Implemented in Tensorflow 2. 9% CNNs are the dominant method for creating face embeddings for recognition. Jul 05, 2019 · Face recognition is the problem of identifying and verifying people in a photograph by their face. Our method, ArcFace, was initially described in an arXiv technical report . Google Scholar; Florin Dobrian, Vyas Sekar, Asad Awan, Ion Stoica, Dilip Joseph, Aditya Ganjam, Jibin Zhan, and Hui Zhang. The proposed ArcFace has a  In this repository, we provide training data, network settings and loss designs for deep face recognition. By using this The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) Loss. Ternary Weight Network. py --network r100 --loss arcface -- dataset emore Caffe: CombinedMargin-caffe; Tensorflow: InsightFace- tensorflow  2019年4月19日 ArcFaceは普通の分類にレイヤーを一層追加するだけで距離学習ができる numpy as np import tensorflow as tf class Arcfacelayer(Layer): # s:softmaxの ArcFace: Additive Angular Margin Loss for Deep Face Recognition MxNet [8], Pytorch [25] and Tensorflow [4]. It might be assumed that, since these networks are distinct, complex, nonlinear functions, that their embeddings are network specific, and thus have some degree of anonymity. 985238 Epoch… The following are code examples for showing how to use tensorflow. Model trained with a cycle-consistency loss and supervised with depth. 0 implementation. By using this ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see InsignFace. If I try to train with an ArcFace layer, the loss does not change at all. 该篇论文为 larger-margin softmax loss 的开篇之作. It makes sure that the sum of all these values is equal to 1 i. I had reviewed it in my post titled Facial Landmark Detection. This is a widely used face detection model, based on HoG features and SVM. se_loss is the Semantic Encoding Loss from the paper Context Encoding for Semantic Segmentation. MobileNetV2: Inverted Residuals and Linear Bottlenecks MobileNetV2 : paper ArcFace: Additive Angular Margin Loss for Deep Face Recognition ArcFace : paper 用pytorch实现arcface loss，从训练到部署（1）目标和思路为什么选择PyTorch 目标和思路 本人是深度学习的小白一枚，以前一直用TensorFlow玩自己的数据集，最近突发奇想，想真正搞一个图像检索应用（100万级别的数据量，15万类），把训练和部署等流程走一遍。 Arcface github Arcface github Vancouver's source for news, photos, videos and more. asked Oct 24 at 23:17. 该Focal loss损失函数出自于论文《 Focal Loss for Dense Object Detection 》，主要是解决正负样本之间的不平衡问题。通过降低easy example中的损失值 Inception V2中，Rethinking改进了目标函数：原来的目标函数，在单类情况下，如果某一类概率接近1，其他的概率接近0，那么会导致交叉熵… 显示全部 关注者 label smoothing就是一种正则化的方法而已，让分类之间的cluster更加紧凑 这一整个是一个计算图，TensorFlow在运行的时候，会先搭建这样的一个计算图. I might have came across the same problem recently as well. This paper studies a variety of loss functions and output layer regularization Aug 14, 2019 · Adversarial face synthesis results on LFW dataset in (a) obfuscation and (b) impersonation attack settings (cosine similarity scores obtained from ArcFace [6] with threshold @ 0. 35 65 Google’s Facenet 99. 🤗Transformers: State-of- the-art Natural Language Processing for Pytorch and TensorFlow 2. This is actually a loss function. The proposed ArcFace has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere. 9 The participants can use any method (e. ArcFace, which is a quite recent publication, implements a series of DNNs (ResNet-100, ResNet-50 and ResNet-34) along with the ArcFace loss. 6729 Acc: 0. evaluate code. 16 ноя 2018 В реализации мы будем использовать Keras и Tensorflow. Arcface model Arcface model Sep 04, 2018 · 얼굴 인식 과정. ), various losses (e. lefnire. with TF2. TensorFlow/Theano tensor of the same shape as y_true. Custom Loss Functions If you has some issues with RTX 3080, using Tensorflow nightly build and CUDA 11. intro: CVPR 2014. " ArcFace: Additive Angular Margin Loss for Deep Face Recognition" Published in   Additionally, we adapt the ArcFace loss function in the emotion recognition con- implemented in TensorFlow, on a Tesla V100 32GB GPU, using Adam  5 days ago HRNet, etc. mxnet dataset to tfrecords; backbone network architectures [vgg16, vgg19, resnet] backbone network architectures [resnet-se, resnext] DOI: 10. 00482 Corpus ID: 8923541. 3299 Acc: 0. Papers. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. ,2018, ArcFace: Additive Angular Margin Loss for Deep Face Recognition] ArcFace 97. In SphereFace [15, 16], ArcFace, margins in well-established loss functions in order to max-imise face class separability. js Quick Start Guide. training TensorFlow Tutorials and Things. 04: Arcface achieved state-of-the-art performance (7/109) on the NIST Face Recognition Vendor Test (FRVT) (1:1 verification)report (name: Imperial-000 and Imperial-001). log_prob(y) In our dataset every example is very similar to the other, the difference is only the noise, so we will examine only the first example from the test set. – ‎1796회 인용 2. The network uses convolutional layers to extract features from input sequences of speaker images and is designed to take the same input as the lipreading system. 2020;Vol. 8954 Epoch 2/24 The ArcFace loss (Deng et al. Running the code in Tensorflow or PyTorch with mpi is way faster. Model compression, see mnist cifar10. 前言paper: CVPR2019_ArcFace: Additive Angular Margin Loss for Deep Face Recognition code: MXNet, pytorch, pytorch, tensorflow author: 邓健康开源代码中   Usage of losses with compile() & fit(). compile(loss = keras. The proposed ArcFace has a  In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. In CVPR, 2019. nl gebruikt Functionele en Analytische cookies voor website optimalisatie en statistieken. Meer uitleg. The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) Loss. # loss function for distributions negloglik = lambda y, rv_y: -rv_y. This method currently achieves the best performance on LFW and YTF. Guo et al. The last 3 layers of my model loss就是拉大anchor negative距离，减少anchor和positive距离。 这样的方法关键在于如何选择三元组。 8. You can check the detail page of our work ArcFace(which accepted in CVPR-2019) and SubCenter-ArcFace(which accepted in ECCV-2020). Without the proposed perturbation loss, perturbations in the adversarial mask are unbounded and therefore, leads to a lack in perceptual quality. 3이다. Google Scholar - ArcFace: Additive Angular Margin Loss for Deep Face Recognition - https://bit. The model is built out of 5 HOG filters – front looking, left looking, right looking, front looking but rotated left, and a front looking but rotated right. 6702 Acc: 0. Aug 30, 2018 · Deep Face Recognition using Tensorflow workshop material (Thursday, August 30, 2018 ) ArcFace: Additive Angular Margin Loss for Deep Face Recognition (2019) TensorFlowでDeep Learningを実行している途中で、損失関数がNaNになる問題が発生した。 Epoch: 10, Train Loss: 85. The Trillion-Pairs consists of the following two parts: In this tutorial I will explore a few ways to speed up Dlib’s Facial Landmark Detector. In this article, you'll find a collection of articles all about TensorFlow, which is "an end-to-end open source platform for machine learning. Deng and J. InsightFace : Additive Angular Margin Loss for Deep Face Recognition and Stefanos Zafeiriou (Current method name ArcFace may be replaced to avoid conflicts If you want to align dataset by yourself, install tensorflow as we're using the  2017年9月1日 2020-04-27 : InsightFace pretrained models and MS1M-Arcface are Our solution is based on [MS1MV2+DeepGlintAsian, ResNet100, ArcFace loss]. 8, 255, 224, 189, 5. get_gcs_path() , I would recommand you to shard and serialise the data beforehand Dec 01, 2019 · We have performed a benchmark of various models (i. " ArcFace: Additive Angular Margin Loss for Deep Face Recognition" Published in   is to train a state of art face recognizer using TensorFlow 2. 7 * Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z. landmark-detection lfw sphereface center-loss focal-loss arcface am-softmax 4 Tensorflow,Keras-VGGFace2-ResNet50 Feb 19, 2019 · Such popular datasets  21 Sep 2019 Tensorflow GPU installation made easy: Ubuntu Version. metric_learning. y_pred: Predictions. Arcface的优点. los s  Realization of arcface loss function on mnist dataset, Programmer Sought, the Mnist is an introduction to tensorflow, but many people are stuck on the Mnist  In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. Date Competition Rank Upvote Title Github User Reply; 2020-10-06: stanford-covid-vaccine Tensorrt Detectron2 IR 에서는 cosine-softmax loss 를 사용한다. Adopt the ensemble of very  ArcFace and ours are trained by VGGFace2 dataset with backbone ResNet50. In this section we propose a multi-stage strategy to effectively collect a large face dataset containing hundreds of example images for thousands of unique identities (Table1). 이 때 사용되는 margin은 0. ArcFace CosFace: Large Margin Cosine Loss AdaCos: Adaptively Scaling Cosine Logits Input (1) Execution Info Log Comments (4) This Notebook has been released under the Apache 2. для минимизации потерь, подсчитанных с помощью функции Triplet Loss. DoReFa-Net. 8954 Epoch 2/24 ArcFace: Additive Angular Margin Loss for Deep Face Recognition Abstract 使用深度卷积神经网络（DCNN）进行大规模人脸识别的特征学习的主要挑战之一是设计适当的损失函数，以增强判别力。中心损失惩罚欧氏空间中深部特征与其相应的类中心之间的距离，以实现类内紧凑性。 A form of signal processing where the input is an image. Electronics. 12-28 Visual Bag 06-22 Github License List Dec 12, 2018 · ArcFace: Additive Angular Margin Loss for Deep Face Recognition. 9, combined_loss_only = True, ** kwargs): """:param use_running_mean: - bool (default: False) Whether to accumulate a running Tensorrt example python dexception/2018AICity_TeamUW 0 . CNNs are the dominant method for creating face embeddings for recognition. 前提・実現したいことkerasでCNNを使った多クラス分類モデルを作っています。その際、以下のようなエラーが発生してしまいました。おそらくArcFace層の次元の数が統一されていないのかと思うのですが、どう修正したら良いかわからなかったため、質問させていただきました。こういった質問は 17 How OpenStack enables face recognition with GPUs and FPGAs Step 4: Extract embeddings [-0. 前言 paper: CVPR2019_ArcFace: Additive Angular Margin Loss for Deep Face Recognition code: MXNet, pytorch, pytorch, tensorflow author: 邓健康 开源代码中称之为 insightface，思路简单，效果却很好，并且与其他变种做了详尽的对比。 那arcface loss是如何实现更好的分类效果呢？ 其实就是在θ上加上一个角度，让两个向量分的更开（在arcface之前还有A-softmaxloss和AM-softmaxloss，前者是增加角度乘积系数方式来增大角度分类，但倍角公式求导不变，后者是减小相似度系数来增加向量之间的距离，但 loss就是拉大anchor negative距离，减少anchor和positive距离。 这样的方法关键在于如何选择三元组。 8. Log: CUDA_VISIBLE_DEVICES='0' python -u train. This system outputs a 512-dimensional feature vector for face images. Buy Wright Commercial Mower Parts and Accessories Online by Part Diagram or by Part Number. “Past, Present, and Future of Face Recognition: A Review”. " We have articles Oct 07, 2020 · The training procedure for LipsID implements ArcFace loss to separate different speakers in the dataset and to provide distinctive features for every one of them. Li, “S3fd: Single shot scale-invariant face detector,” in ICCV, 2017. 30 Jun 2020 ArcFace: Additive Angular Margin Loss for Deep Face Recognition check out the Tensorflow 2 implementations of RetinaFace and ArcFace. Usually you look at the loss graph to see when the model stopped making progress so that you can stop training. 07698) (CC) Normally, classification networks use Cross-Entropy Loss to output a vector of class probabilities. e. Comparison with SphereFace and CosFace Numerical Similarity. Our results outperformed all state-of-the-art networks, illustrating: i) the richness of Aff-Wild2 (providing it with the ability to be used as robust prior for network pre-training) and ii) that the ArcFace loss can be used in 2 days ago · FaceNet (Google) They use a triplet loss with the goal of keeping the L2 intra-class distances low and inter-class distances high. By using this repository, you can simply achieve LFW 99. 7%になった。 CIFAR10ではResNet18でもDeepなのかも。 Jun 05, 2018 · Loss: The authors propose Performance: The proposed ArcFace achieves state-of-the-art results on the MegaFace Challenge, Optimizing TensorFlow Models for Serving. It squishes the values between 0 and 1. 04. The differential framework is not efficient enough. I have kept Include_top = False, input shape = (160,160,3) with Imagenet weights. 3. 1 + cudnn 8. ArcFace는 대규모 이미지와 비디오 Datasets을 포함하는 10개의 얼굴 인식 Benchmarks에서 최첨단 성능을 달성함. 2D Cross Entropy Loss with SE Loss. The Input Operator t c n s 1122 ×3 Conv3×3 - 32 1 2 562 ×32 Depthwise Conv3×3 1 64 3 2 With a multi-machine and multi-GPU tensorflow cluster, three extremely deep inception-resnet-like deep networks (depth= 360, 540, 720) are trained to accomplish face verification tasks. MaxPooling1D layer; MaxPooling2D layer this loss is applied at multiple layers, not just the final one. 近年来随着硬件计算能力的大爆发，在高性能计算的支持下深度学习有了革命性的进步，在互联网大数据的保证下深度学习有了持续不断的动力，优秀的网络结构被不断提出，深度学习技术已被推向 时代浪潮。 Triplet Loss and Online Triplet Mining in TensorFlow Tensorflow实现Triplet Loss（[1] 的译文） tf. 12 Apr 2019 On upper row,. 然后开启一个会话sess，如果你想得到计算图中的某个结果，比如total_loss，你就需要sess. Improved detection rate by 3. Edit) 10/21/2020 - I tested (Tensorflow nightly-build + CUDA 11. baseline model. Arcface github There are many factors that can contribute to aging such as sun exposure and how much water you drink. Keras documentation. Arcface: Additive angular margin loss for deep face recognition. 80%+ and Megaface 98%+ by a single model. These examples are extracted from open source projects. It allows developers to create large-scale neural networks with many layers. With the generator alone, undesirable artifacts are introduced. The team also used carefully selected augmentations and numerous network This is how authors of ArcFace paper address this problem: In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. triplet_semihard_loss 点赞 评论 分享 x 海报分享 扫一扫，分享海报 收藏 手机看 分享到 微信朋友 x. Easy. 这就是softmax loss函数， 表示全连接层的输出。 在计算Loss下降的过程中，我们让 的比重变大，从而使得log() 括号内的数更变大来更接近1，就会 log(1) = 0，整个loss就会下降。 損失型= 4：ArcFace; 損失型= 5：複合マージン; 損失型= 12：トリプル損失; 我々の方法であるArcFaceは、当初、 arXivのテクニカルレポートで説明されていました。 このリポジトリを使用することで、LFW 99. The general form of Cross Entropy loss is as follows - Cross Entropy Loss. 12. Build a TensorFlow Image Classifier in 5 Min - Duration: 5:47. pytorch. So if we want to use a common loss function such as MSE or Categorical Cross-entropy, we can easily do so by passing the appropriate name. 虽然具体的算法细节没有透露，但宣称他们使用了 Tensorflow 来实现和学习神经网络结构，其中的损失函数则选用了 ArcFace Loss。 朝鲜研发的人脸识别方案展示 CSDN问答为您找到深度学习loss下降，精度不提高，怎么解？相关问题答案，如果想了解更多关于深度学习loss下降，精度不提高 Showing 1006 solutions within top 20 on each competition. Wide ResNet (CIFAR) by ritchieng. 824237, test loss: 0. 0. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. • Multiple factors need to be considered: deep learning frameworks, GPU platforms, deep network models, and datasets. Dense, Conv1D, Conv2D and Conv3D) have a Arcface github Arcface github Arcface github. Usually treating the digital image as a two-dimensional signal (or multidimensional). Created by Yangqing Jia Lead Developer Evan Shelhamer. Zafeiriou, "ArcFace: Additive angular margin loss for deep face recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. This is a tensorflow implementation of paper "ArcFace: Additive Angular Margin Loss for Deep Face Recognition". Lukman Ramsey in Google InsightFace-tensorflow. 1750353 -0. Darknet framework with YOLOv3 Tiny CNN and Tensorflow with alignments utilizing triplet and ArcFace losses in training. At the face detection stage, the the module will output the x,y,w,h coordinations as well as 5 facial landmarks for further alignment. Caffe. Reason for the request *. 1. You can record and post programming tips, know-how and notes here. org roboticvision. Train with 1000 arccos triplet loss . Show more Show less A year ago, I started learning neural network with Tensorflow. 2Install Wright Parts, Wright Stander Mower Parts and Accessories. " ArcFace: Additive Angular Margin Loss for Deep Face Recognition" Published in   combine losses contributed by RogerLo. ArcFace: Additive Angular Margin Loss for Deep Face Recognition J Deng 저술 – ‎2018. Apr 18, 2020 · ArcFace: Additive Angular Margin Loss for Deep Face Recognition Pointnet++ modules implemented as tensorflow 2 keras layers Edward2 => A lightweight probabilistic programming language in NumPy or TensorFlow Feb 03, 2017 · The primary software tool of deep learning is TensorFlow. For example,TensorFlowofficially supports GPU acceleration for Linux, Mac OX and Windows at present. These penalties are summed into the loss function that the network optimizes. However, the shape of your face also has a big impact. If I try to train it on a subset (~50 labels) of images, the loss does start to change, but it didn’t seem to converge to a useful model. 027159. Trained on MegaDepth, removing conflicts with the test data. In this notebook, I will show you Tensorflow implementation of ArcFace layer, GeM pooling layer and how to train everything on TPU. " ArcFace: Additive Angular Margin Loss for Deep Face Recognition" Published in   Tensoflow implementation of InsightFace (ArcFace: Additive Angular Margin Loss for Deep Face Recognition). 18 – ‎19회 인용[참고 논문]1. Google Scholar The installation instructions of TensorFlow are written to be very detailed onTensorFlowwebsite. Proposed an effective loss function to recover discriminative facial features. 80％+とMegaface 98％+を単一のモデルで簡単に達成できます。 Pytorch Loss Function Empire Outlets is New York City’s premier outdoor shopping and dining center. Jan 05, 2018 · ArcFace: Additive Angular Margin Loss for Deep Face Recognition - Duration: 12:14. . Detection; Alignment: Facial Landmark Detection; Normalization 특정 스케일로 변환하거나 영상/이미지를 회전시켜 정규화시킴. The ArcFace: Additive Angular Margin Loss for Deep Face Recognition. this one, I was actually able to make it work! The authors of #retinaface originally worked on the more broader problem of #face #recognition so this step is just a precursor to their other model #arcface ([[20200903233242]] ArcFace). , Softmax, Focal, SphereFace, CosFace, AmSoftmax, ArcFace, ArcNegFac,cavaface. 2019. The architecture chosen is a modified version of ResNet50 and the loss function used is ArcFace,   30 Jan 2019 ArcFace: Additive Angular Margin Loss for Deep Face Recognition You can also find reimplementations in TensorFlow, PyTorch and Caffe. Training Tips(Continual updates). 管理番号：a1237 【商品説明】 シャネルの 「ヴィンテージ」 カフスです。 定番人気のココマーク入り☆リッチなゴールドカラーです· ランク 【7】 【7】多少の傷、汚れがあるが程度良好の美品 【状態】 ヴィンテージ品の中では良コンディションです。 Loss function for 1d segment estimation. arcface loss tensorflow

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