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  • triplet_sample_layer的实现

    千次阅读 热门讨论 2016-09-09 10:29:21
    博客中写了triplet_loss_layer的详细实现过程。但是,很多人(包括我)留言,不知道怎么用?不知道三元组怎么组装? 后来索性仿照网上一个python的实现思路,自己在前面加了一层triplet_sample_layer作为接口的...

    http://blog.csdn.net/tangwei2014/article/details/46812153

    博客中写了triplet_loss_layer的详细实现过程。但是,很多人(包括我)留言,不知道怎么用?不知道三元组怎么组装?


    后来索性仿照网上一个python的实现思路,自己在前面加了一层triplet_sample_layer作为接口的过渡。现分享给大家。

    功能就是,实现一个blob分解成对应的triplet_loss_layer需要的三个blob。

    输出四个blob中,最后一个blob是为了适应上面博文的接口,作为冗余。

    输入两个blob,也有冗余,可以不用输入标签的。

    data_layer层读入的应该是三元组顺序正确(而不是随机乱序的)的路径、标签对文件。

    其实,本质上仅仅实现了类似slice的功能。并没有做什么计算。

    第一次向caffe中添加layer,特开通博客,留作纪念。

    希望能对像我一样的菜鸟入门时,有所帮助。


    1.triplet_sample_layer.hpp文件:

    #ifndef CAFFE_TRIPLET_SAMPLE_LAYER_HPP_

    #define CAFFE_TRIPLET_SAMPLE_LAYER_HPP_


    #include <vector>
    #include "caffe/blob.hpp"
    #include "caffe/layer.hpp"
    #include "caffe/proto/caffe.pb.h"


    namespace caffe {
    /**
    * @brief Computes the triplet sample
    */
    template <typename Dtype>
    class TripletSampleLayer : public Layer < Dtype > {
    public:
    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top);


    explicit TripletSampleLayer(const LayerParameter& param)
    :Layer<Dtype>(param){}
    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top);


    virtual inline int ExactNumBottomBlobs() const { return 2; }
    virtual inline int ExactNumTopBlobs() const { return 4; }
    virtual inline const char* type() const { return "TripletSample"; }


    virtual inline bool AllowForceBackward(const int bottom_index) const {
    return bottom_index != 4;
    }


    protected:
    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top);
    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top);


    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
    };


    }  // namespace caffe


    #endif  // CAFFE_TRIPLET_SAMPLE_LAYER_HPP_


    2.triplet_sample_layer.cpp文件:

    /*
    * triplet_sample_layer.cpp
    *
    *  Created on: 2016.09.01
    *      Author: hecunxin
    */


    #include <algorithm>  
    #include <vector>  


    #include "caffe/layer.hpp"  
    #include "caffe/layers/triplet_sample_layer.hpp"  
    #include "caffe/util/io.hpp"  
    #include "caffe/util/math_functions.hpp"  


    namespace caffe {


    template <typename Dtype>
    void TripletSampleLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    Layer<Dtype>::LayerSetUp(bottom, top);


    CHECK_EQ(bottom[0]->num(), bottom[1]->num());


    CHECK_EQ(bottom[0]->height(), 1);
    CHECK_EQ(bottom[0]->width(), 1);


    CHECK_EQ(bottom[1]->channels(), 1);
    CHECK_EQ(bottom[1]->height(), 1);
    CHECK_EQ(bottom[1]->width(), 1);

    return;
    }


    template <typename Dtype>
    void TripletSampleLayer<Dtype>::Forward_cpu(
    const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) 
    {
    int j = 0;

    const Dtype* bottom_data = bottom[0]->cpu_data();
    Dtype* top_data_anchor = top[0]->mutable_cpu_data(); //anchor,
    Dtype* top_data_positive = top[1]->mutable_cpu_data(); //positive,
    Dtype* top_data_negative = top[2]->mutable_cpu_data(); //negative,
    Dtype* top_data_w = top[3]->mutable_cpu_data(); //w
    const int count = top[0]->num();
    for (int i = 0; i < count; ++ i)
    {
    caffe_copy(top[0]->channels() * top[0]->height() * top[0]->width(), bottom_data + i * 3 + 0, top_data_anchor + i); //anchor,
    caffe_copy(top[0]->channels() * top[0]->height() * top[0]->width(), bottom_data + i * 3 + 1, top_data_positive + i); //positive,
    caffe_copy(top[0]->channels() * top[0]->height() * top[0]->width(), bottom_data + i * 3 + 2, top_data_negative + i); //negative,
    top_data_w[i] = Dtype(1.);
    }
    }


    template <typename Dtype>
    void TripletSampleLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom)
    {
    if (propagate_down[0]) 
    {
    const int count = top[0]->num();
    const Dtype* top_diff_anchor = top[0]->cpu_diff(); //anchor,
    const Dtype* top_diff_positive = top[1]->cpu_diff(); //positive,
    const Dtype* top_diff_negative = top[2]->cpu_diff(); //negative,
    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    for (int i = 0; i < count; ++ i)
    {
    caffe_copy(top[0]->channels() * top[0]->height() * top[0]->width(), top_diff_anchor + i, bottom_diff + i * 3 + 0); //anchor,
    caffe_copy(top[0]->channels() * top[0]->height() * top[0]->width(), top_diff_positive + i, bottom_diff + i * 3 + 1); //positive,
    caffe_copy(top[0]->channels() * top[0]->height() * top[0]->width(), top_diff_negative + i, bottom_diff + i * 3 + 2); //negative,
    }
    }
    }


    template <typename Dtype>
    void TripletSampleLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
    int count_ = bottom[0]->count() / 3;
    vector<int> shape;
    shape.push_back(bottom[0]->num() / 3);
    shape.push_back(bottom[0]->channels());
    shape.push_back(bottom[0]->height());
    shape.push_back(bottom[0]->width());


    for (int i = 0; i < top.size() - 1; ++i)
    {
    CHECK_NE(top[i], bottom[0]) << this->type() << " Layer does not "
    "allow in-place computation.";
    top[i]->Reshape(shape);
    CHECK_EQ(count_, top[i]->count());
    }
    vector<int> shape3;
    shape3.push_back(bottom[0]->num() / 3);
    shape3.push_back(1);
    shape3.push_back(1);
    shape3.push_back(1);
    top[3]->Reshape(shape3);


    LOG(INFO) << "bottom0 shape:" << bottom[0]->shape_string();
    LOG(INFO) << "bottom1 shape:" << bottom[1]->shape_string();




    LOG(INFO) << "Top0 shape:" << top[0]->shape_string();
    LOG(INFO) << "Top1 shape:" << top[1]->shape_string();
    LOG(INFO) << "Top2 shape:" << top[2]->shape_string();
    LOG(INFO) << "Top3 shape:" << top[3]->shape_string();
    return;
    }


    #ifdef CPU_ONLY  
    STUB_GPU(TripletSampleLayer);
    #endif  


    INSTANTIATE_CLASS(TripletSampleLayer);
    REGISTER_LAYER_CLASS(TripletSample);


    }  // namespace caffe  


    3.triplet_sample_layer.cu文件:

    /*
    * triplet_sample_layer.cpp
    *
    *  Created on: 2016.09.08
    *      Author: hecunxin
    */


    #include <algorithm>  
    #include <vector>  


    #include "caffe/layer.hpp"  
    #include "caffe/util/io.hpp"  
    #include "caffe/util/math_functions.hpp"  
    #include "caffe/layers/triplet_sample_layer.hpp"


    namespace caffe {


    template <typename Dtype>
    void TripletSampleLayer<Dtype>::Forward_gpu(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    /*Forward_cpu(bottom, top);
    return;*/


    int j = 0;


    const Dtype* bottom_data = bottom[0]->gpu_data();
    Dtype* top_data_anchor = top[0]->mutable_gpu_data(); //anchor,
    Dtype* top_data_positive = top[1]->mutable_gpu_data(); //positive,
    Dtype* top_data_negative = top[2]->mutable_gpu_data(); //negative,
    Dtype* top_data_w = top[3]->mutable_gpu_data(); //w
    const int count = top[0]->num();
    for (int i = 0; i < count; ++i)
    {
    caffe_gpu_memcpy(top[0]->channels() * top[0]->height() * top[0]->width(), bottom_data + i * 3 + 0, top_data_anchor   + i); //anchor,
    caffe_gpu_memcpy(top[0]->channels() * top[0]->height() * top[0]->width(), bottom_data + i * 3 + 1, top_data_positive + i); //positive,
    caffe_gpu_memcpy(top[0]->channels() * top[0]->height() * top[0]->width(), bottom_data + i * 3 + 2, top_data_negative + i); //negative,
    caffe_gpu_set(top[3]->channels() * top[3]->height() * top[3]->width(), Dtype(1.), top_data_w + i);
    //top_data_w[i] = Dtype(1.);
    }
    return;
    }



    template <typename Dtype>
    void TripletSampleLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {

    /*Backward_cpu(top, propagate_down, bottom);
    return;*/


    if (propagate_down[0])
    {
    const int count = top[0]->num();
    const Dtype* top_diff_anchor = top[0]->gpu_diff(); //anchor,
    const Dtype* top_diff_positive = top[1]->gpu_diff(); //positive,
    const Dtype* top_diff_negative = top[2]->gpu_diff(); //negative,
    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
    for (int i = 0; i < count; ++i)
    {
    caffe_gpu_memcpy(top[0]->channels() * top[0]->height() * top[0]->width(), top_diff_anchor + i,   bottom_diff + i * 3 + 0); //anchor,
    caffe_gpu_memcpy(top[0]->channels() * top[0]->height() * top[0]->width(), top_diff_positive + i, bottom_diff + i * 3 + 1); //positive,
    caffe_gpu_memcpy(top[0]->channels() * top[0]->height() * top[0]->width(), top_diff_negative + i, bottom_diff + i * 3 + 2); //negative,
    }
    }
    return;
    }


    INSTANTIATE_LAYER_GPU_FUNCS(TripletSampleLayer);


    }  // namespace caffe  



    展开全文
  • read_parasitics[-force][-starN]{[{{[-all_rc_cornerlist_of_rc_corners]...

    read_parasitics   

    [-force] 
    [-starN] 
    {[{{[-all_rc_corner list_of_rc_corners ] 
    [-rc_corner rc_corner_name ]} | [-statistical string ]}]}


    这个命令可以读取spef或者RCdb的信息


    -force  在blocklevel rc db和spef缺失的情况下,也能读取rc信息

    -starN  读取spef文件中的*N声明,*N里面记录了rc node的位置信息

    -all_rc_corner  指定读取所有rc corner的rcdb.对于hierarchical design的话,需要指定top和block level的rcdb

    -rc_corner  指定rc corner和对应的spef信息

    -statistical  指定统计学rcdb和spef信息

     

    例子:

    在C1,C2 corner下读取top, block1, block2的rc信息

    read_parasitics\ 
    -rc_corner C1 Top_C1.spef Block1_C1.spef Block2_C1.spef.gz \ 
    -rc_corner C2 Top_C2.rcdb.d Block1_C2.spef Block2_C2.rcdb.d



    往期回顾

    静态时序分析STA合集一

    静态时序分析STA合集二

    时序基本概念介绍<sdc合集>

    数字后端基本概念合集(一)

    数字后端基本概念合集(二)

    数字后端基本概念合集(三)

    Low Power概念介绍合集

    数字后端dbGet使用方法合集

    号外,数字后端交流群招人啦

    IC圈的世界杯 | 论芯片设计的胜利十一人

    知否?知否?Block PD应该提交哪些数据?

    Timing ECO手修攻略

    数字后端面试100问(2019全新版)

    数字后端面试100问(校招版)

    简历请戳邮箱:taozhang3260@163.com

    觉得有用的话,给我点个好看吧

    展开全文
  • Code:https://github.com/SpikeKing/triplet-loss-mnist 记录一下在跑这个代码时遇到的一些问题以及解决办法: Environment: python2.7 keras ‘2.2.4’ tensorflow ‘1.13.1’ numpy ‘1.16.4’ pydot ‘1.2.4’ ...

    参考链接:https://github.com/SpikeKing/triplet-loss-mnist

    本文用于记录一下在跑这个代码时遇到的一些问题以及解决办法:

    Environment

    • python2.7
    • keras ‘2.2.4’
    • tensorflow ‘1.13.1’
    • numpy ‘1.16.4’
    • pydot ‘1.2.4’
    • graphviz ‘2.40.1’
    • bunch ‘1.0.1’

    问题一:

    OSError: `pydot` failed to call GraphViz.Please install GraphViz (https://www.graphviz.org/) and ensure that its executables are in the $PATH.
    

    解决办法:

    pip install pydot-ng
    
    pip install pydot==1.2.4
    
    conda install graphviz
    

    问题二:

    TypeError: Required Group, str or dict. Received: <type 'unicode'>.
    

    解决办法:
    修改代码:
    triplet_trainer.py 87行

    self.model.save(str(os.path.join(self.config.cp_dir,"triplet_loss_model.h5")))
    

    triplet_infer.py 36行

    model_path = str(os.path.join(self.config.cp_dir, "triplet_loss_model.h5"))
    

    运行python main_train.py -c configs/triplet_config.json
    训练截图:
    在这里插入图片描述

    运行python main_test.py -c configs/triplet_config.json
    测试截图:
    在这里插入图片描述

    在这里插入图片描述

    展开全文
  • Triplet Loss入门

    千次阅读 2018-12-10 14:39:36
    Triplet Loss入门 Face verification vs. face recogntion Verification Input image, name/ID Output whether the input image is that of the claimed person. Recognition Has a database of K peosons(or...

    Triplet Loss入门


    Face verification vs. face recogntion

    Verification

    • Input image, name/ID
    • Output whether the input image is that of the claimed person.

    Recognition

    • Has a database of K peosons(or not recognized)

    Relations

    We can use a face verification system to make a face recognition system. The accuracy of the verification system has to be high (around 99.9% or more) to be use accurately within a recognition system because the recognition system accuracy will be less than the verification system given K persons.

    One Shot Learning

    • One of the face recognition challenges is to solve one shot learning problem.
    • One Shot Learning: A recognition system is able to recognize a person, learning from one image.
    • Historically deep learning doesn’t work well with a small number of data.
      Instead to make this work, we will learn a similarity function:
      d ( i m g 1 , i m g 2 ) d( img1, img2 ) d(img1,img2) = degree of difference between images.
      We want d result to be low in case of the same faces.
      We use τ \tau τ as a threshold for d:
      If d ( i m g 1 , i m g 2 ) &lt; = τ d( img1, img2 ) &lt;= \tau d(img1,img2)<=τ Then the faces are the same.
    • Similarity function helps us solving the one shot learning. Also its robust to new inputs.

    Siamese Network

    • We will implement the similarity function using a type of NNs called Siamease Network in which we can pass multiple inputs to the two or more networks with the same architecture and parameters.
    • The loss function will be d ( x 1 , x 2 ) = ∣ ∣ f ( x 1 ) − f ( x 2 ) ∣ ∣ 2 d(x^1, x^2) = || f(x^1) - f(x^2) ||^2 d(x1,x2)=f(x1)f(x2)2
      2.png-97.3kB

    Triplet Loss

    Firstly

    • Triplet Loss is one of the loss functions we can use to solve the similarity distance in a Siamese network.
    • ∣ ∣ f ( A ) − f ( P ) ∣ ∣ 2 &lt; = ∣ ∣ f ( A ) − f ( N ) ∣ ∣ 2 ||f(A) - f(P)||^2 &lt;= ||f(A) - f(N)||^2 f(A)f(P)2<=f(A)f(N)2
    • ∣ ∣ f ( A ) − f ( P ) ∣ ∣ 2 − ∣ ∣ f ( A ) − f ( N ) ∣ ∣ 2 &lt; = 0 ||f(A) - f(P)||^2 - ||f(A) - f(N)||^2 &lt;= 0 f(A)f(P)2f(A)f(N)2<=0
    • ∣ ∣ f ( A ) − f ( P ) ∣ ∣ 2 − ∣ ∣ f ( A ) − f ( N ) ∣ ∣ 2 &lt; = − α ||f(A) - f(P)||^2 - ||f(A) - f(N)||^2 &lt;= -\alpha f(A)f(P)2f(A)f(N)2<=α to make sure the NN won’t get an output if zero

    Secondly

    • Given 3 images (A, P, N)
    • L ( A , P , N ) = m a x ( ∣ ∣ f ( A ) − f ( P ) ∣ ∣ 2 − ∣ ∣ f ( A ) − f ( N ) ∣ ∣ 2 + a l p h a , 0 ) L(A, P, N) = max (||f(A) - f(P)||^2 - ||f(A) - f(N)||^2 + alpha , 0) L(A,P,N)=max(f(A)f(P)2f(A)f(N)2+alpha,0)
    • $J = \sum(L(A[i], P[i], N[i]) , i) $for all triplets of images.

    Thirdly

    • During training if A, P, N are chosen randomly (Subjet to A and P are the same and A and N aren’t the same) then one of the problems this constrain is easily satisfied
    • What we want to do is choose triplets that are hard to train on.

    Offline triplet mining

    1.最简单的想法就是离线算法,先找到B个triplets计算它们的loss然后再送进网络学习,但是这样很低效,要遍历整个网络。

    Online triplet mining

    1.对于一个有B个样本的batch,我们最多可以产生 B 3 B^3 B3个triplets。这里面虽然有很多无效的(没有两个P,一个N),但是却可以在一个batch中产生更多的triplets。
    2.Batch hard strategy
    找到每个anchor最hardest的P和N

    • 计算一个2D距离矩阵然后将无效的设置为0,将有效的pair留下来( a ≠ p , a\neq p, a̸=p, a和p有着相同的label),然后在修改后的矩阵计算每一行的最大值。
    • 计算最小值N的时候不能将无效的设置为0(无效的是a和
      n有着相同的label)。
    def batch_hard_triplet_loss(labels, embeddings, margin, squared=False):
        """Build the triplet loss over a batch of embeddings.
    
        For each anchor, we get the hardest positive and hardest negative to form a triplet.
    
        Args:
            labels: labels of the batch, of size (batch_size,)
            embeddings: tensor of shape (batch_size, embed_dim)
            margin: margin for triplet loss
            squared: Boolean. If true, output is the pairwise squared euclidean distance matrix.
                     If false, output is the pairwise euclidean distance matrix.
    
        Returns:
            triplet_loss: scalar tensor containing the triplet loss
        """
        # Get the pairwise distance matrix
        pairwise_dist = _pairwise_distances(embeddings, squared=squared)
    
        # For each anchor, get the hardest positive
        # First, we need to get a mask for every valid positive (they should have same label)
        mask_anchor_positive = _get_anchor_positive_triplet_mask(labels)
        mask_anchor_positive = tf.to_float(mask_anchor_positive)
    
        # We put to 0 any element where (a, p) is not valid (valid if a != p and label(a) == label(p))
        anchor_positive_dist = tf.multiply(mask_anchor_positive, pairwise_dist)
    
        # shape (batch_size, 1)
        hardest_positive_dist = tf.reduce_max(anchor_positive_dist, axis=1, keepdims=True)
    
        # For each anchor, get the hardest negative
        # First, we need to get a mask for every valid negative (they should have different labels)
        mask_anchor_negative = _get_anchor_negative_triplet_mask(labels)
        mask_anchor_negative = tf.to_float(mask_anchor_negative)
    
        # We add the maximum value in each row to the invalid negatives (label(a) == label(n))
        max_anchor_negative_dist = tf.reduce_max(pairwise_dist, axis=1, keepdims=True)
        anchor_negative_dist = pairwise_dist + max_anchor_negative_dist * (1.0 - mask_anchor_negative)
    
        # shape (batch_size,)
        hardest_negative_dist = tf.reduce_min(anchor_negative_dist, axis=1, keepdims=True)
    
        # Combine biggest d(a, p) and smallest d(a, n) into final triplet loss
        triplet_loss = tf.maximum(hardest_positive_dist - hardest_negative_dist + margin, 0.0)
    
        # Get final mean triplet loss
        triplet_loss = tf.reduce_mean(triplet_loss)
    
        return triplet_loss
    
    展开全文
  • 关于triplet loss的原理。目标函数和梯度推导在上一篇博客中已经讲过了。详细见:triplet loss原理以及梯度推导。这篇博文主要是讲caffe下实现triplet loss。编程菜鸟。假设有写的不优化的地方,欢迎指出。 1....
  • 对于Facenet进行人脸特征提取,算法内容较为核心和比较难以理解的地方在于三元损失函数Triplet-loss。 神经网络所要学习的目标是:使得Anchor到Positive的距离要比Anchor到Negative的距离要短(Anchor为一个样本,...
  • :type nums: List[int] :rtype: bool """ if nums == None or len(nums) return False else: min = nums[0] mid = None for n in nums[1:]: if mid != None:#找第三个数 if n > mid:#比第二个数大,符合...
  • insightface tripletloss源码阅读

    千次阅读 2018-09-11 14:04:23
    一:insighteface tripletloss实现中的dataiter部分解读 前言: mxnet的dataiter一般为继承io.DataIter类,实现其中主要的几个函数。 需要实现的主要函数为:__init__(),reset(),next()。以及几个在fit...
  • R tm

    千次阅读 2013-10-23 16:48:14
    > tdm (doc.corpus) Error in simple_triplet_matrix(i = i, j = j, v = as.numeric(v), nrow = length(allTerms), : 'i, j, v' different lengths In addition: Warning messages: 1: In
  • MXNet/Gluon 中 Triplet Loss 算法

    千次阅读 2018-05-18 12:46:00
    Triplet Loss,即三元组损失,用于训练差异性较小的数据集,数据集中标签较多,标签的样本较少。输入数据包括锚(Anchor)示例⚓️、正(Positive)示例和负(Negative)示例,通过优化模型,使得锚示例与正示例的...
  • 这里只讲述怎么 用bert来处理 我们文本分类的问题: 具体 代码 见github : 具体的bert 模型可以参考 github 上面的bert 以及肖博士的bert 服务 首先说一下 bert 里面 的 run_classifier.py 用来进行文本分类 ...
  • tripletloss

    2016-04-06 16:19:00
    http://blog.csdn.net/tangwei2014/article/details/46788025 转载于:https://www.cnblogs.com/Wanggcong/p/5359821.html
  • 先把我们所用到的代码说一下,主要是train_tripletloss和classifier 首先我们需要准备好数据集,这个数据集要求的尺寸是160*160,可以是非人脸数据,然后放到指定的文件夹,将主要的修改代码的位置贴出来: 一般来讲...
  • 采用keras训练自己定义的triplet时出现报错 Traceback (most recent call last): File "train_similarity.py", line 52, in <module> main() File "train_similarity.py", line 48, in main **train_...
  • typedef ElemType* Triplet; //由InitTriplet分配3个元素存储空间 基本操作的函数原型说明 //----基本操作的函数原型说明---- Status InitTriplet(Triplet& T, ElemType v1, ElemType v2, ElemType v3); //操作...
  • Triplet Loss、Coupled Cluster Loss 探究

    万次阅读 热门讨论 2016-07-25 20:49:46
    因为要区分相似图像,所以研究了一下 Triplet Loss,还有今年 CVPR 的一篇文章:《Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles》,这篇文章提出了 Coupled Cluster Loss 。...
  • home+'track_metadata.db') cur = conn.cursor() cur.execute("SELECT name FROM sqlite_master WHERE type='table'") cur.fetchall() 运行结果 [('songs',)] 查询歌曲子集的数据,保存为文件track_metadata_df_sub....
  • attention机制(SE-Net、CBAM及Triplet

    千次阅读 2020-10-20 12:22:01
    attention机制(SE-Net、CBAM及Triplet)简介空间维度引入attention通道维度引入attentionSE-Net模型(源于https://www.bilibili.com/video/BV1SA41147uA)squeeze(FsqF_{sq}Fsq​)excitation(FexF_{ex}Fex​)scale...
  • :type board: List[List[int]] :rtype: void Do not return anything, modify board in-place instead. """ n = len ( board ) m = len ( board [ 0 ] ) for i in range ( n ) : for j in ...
  • 颜色矩的特征提取

    千次阅读 2016-06-23 14:06:07
    It shows different types of colors appeared and the number of pixels in each type of the colors appeared. The relation between a color histogram and a luminance histogram is that a color histogram ...
  • 抽象数据类型三元组Triplet的表示和实现。 数据类型是一个值的集合和定义在这个值集上的一组操作的总称。按“值”的不同特性,高级程序语言中的数据类型可分为两类:一类是非结构的原子类型,原子类型的值是不可分解...
  • offboard_control_mode.ignore_position = (bool)(set_position_target_local_ned.type_mask & 0x7); offboard_control_mode.ignore_alt_hold = (bool)(set_position_target_local_ned.type_mask & 0x4); offboard_...
  • 数据库sql语句总结

    2020-08-28 10:29:50
    ‘testBack’, ‘c:\mssql7backup\MyNwind_1.dat’ — 开始 备份 BACKUP DATABASE pubs TO testBack 4、说明:创建新表 create table tabname(col1 type1 [not null] [primary key],col2 type2 [not null],…) 根据...
  • 我们在进行triplet loss 的计算时候,必须使得送入网络的mini_batch中的数据是按照N 个ID,每个ID存在K个不同个体的方式进行的,但是如果我们按照普通的dataloader 获取方式,无法保证这样的划分,因此需要进行处理...
  • 音乐推荐系统(协同过滤和SVD)

    千次阅读 2021-03-06 15:48:22
    python音乐推荐系统 协同过滤和SVD矩阵分解 import pandas as pd import numpy as np ...triplet_dataset = pd.read_csv(filepath_or_buffer=data_home+'train_triplets.txt', sep='\t', header=
  • 此模块共有两个主要部分,siamese class决定着神经网络的相关配置,将Triplet网络结构的相关参数、前向传播和损失函数等;siamese train主要为主接口face_inference.py提供调用的接口,每一次调用只遍历一轮数据并...
  • leetcode Increasing Triplet Subsequence Given an unsorted array return whether an increasing subsequence of length 3 exists or not in the array. Formally the function should: Return true if t
  • 关于triplet loss的原理,目标函数和梯度推导在上一篇博客中已经讲过了,具体见:triplet loss原理以及梯度推导,这篇博文主要是讲caffe下实现triplet loss,编程菜鸟,如果有写的不优化的地方,欢迎指出。...

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