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今天是2019年的第一天。2019年的第一天，深圳有点冷！好在一大早就能收到亲人打来的电话，中午可以和爸妈唠会嗑，从喧嚣的青旅搬到充满生活气息的小区，一切都是陌生的，一切又是充满希望的。过去的一年是失意的一年，过去的一年是成长的一年，过去的一年是回忆起来最有味道的一年……
不以己悲还记得那个四月份的
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CrossEntropy Loss熵的概念来源于信息论，表示的是系统内部的紊乱程度；与之相关的几个概念有：信息熵，相对熵，散度。
熵信息熵可以看成事件的信息量，事件越不确定表示系统内部越紊乱，产生的结果越有多样性，每种结果发生的概率越小，不确定性越大，信息量也就越大。假设有一事件$X$，可能的结果
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问题引入原来的做法：提取图片特征，建立特征之间相互关联的模型，然后利用SVM进行分类判断（例如：有四个轮子的方形物体就是汽车，有翅膀的就是鸟）。这种分类器一来类别比较少，通过不同特征之间的组合就能确定；二来不同类别的特征之间差距较大（例如：人就不可能有轮子）做人脸识别时，不同的人就是一个类别，而且每
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<a class="post-title-link" href="/2018/10/28/TensorFlow-MTCNN模型训练到实例应用/" itemprop="url">MTCNN模型学习</a></h1>
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模型理解MTCNN（Tutil-Task CNN）：这里的多任务指的是人脸检测、人脸对齐、特征点定位，严格来说这里并不做人脸识别。模型采用级联结构，由三个不同的层组成：
Proposal-Net：生成候选框Refine-Net：优化候选框，去掉重叠率较小的Output-Net：继续优化，并定位特征
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