AI神经网络-CNN基本结构分析（ Conv层、Pooling层、FCN层/softmax层）
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AI：IPPR的数学表示-CNN基本结构分析（ Conv层、Pooling层、FCN层/softmax层）
name: “conv2”
type: “Convolution”
bottom: “norm1”
top: “conv2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: “gaussian”
std: 0.01
}
bias_filler {
type: “constant”
value: 1
}
}
}
layer {
name: “relu2”
type: “ReLU”
bottom: “conv2”
top: “conv2”
}
CNN的结构分析—Pooling层
使用卷积核提取的大量特征，产生超高的维度，面临着表示困难的问题，且直接叠加的卷积层会产生更庞大的卷积特征集合。Pooling层一般作为显著性选取和降维的作用存在。
Pooling明显地降低了特征图的维度
使用MeanPooling的方式，相当于平均化特性，用于简化函数模型，同时丧失了一些特异性准确性，增加泛化性能，即是把复杂模型转化为一个平均模型，得失都很明显。而maxpooling则描述为提取特征本身的显著性作用，同时进行数据压缩。
MeanPooling可以用网络加深来替换其数据压缩的作用，一个MeanPooling层相当于网络深度增加两倍，而MeanPooling自身模型简单化的特点丧失了准确性表示，逐渐被取代一般不再被使用。上图中，同样地采用一个22的filter，max pooling是在每一个区域中寻找最大值，这里的stride=2,最终在原特征图中提取主要特征得到右图。概率意义上， MaxPooling 过程之后，特征更小且相对表示性更强。
参考文章：http://ufldl.stanford.edu/wiki/index.php/池化
池化的平移不变性：如果人们选择图像中的连续范围作为池化区域，并且只是池化相同(重复)的隐藏单元产生的特征，那么，这些池化单元就具有平移不变性 (translation invariant)。这就意味着即使图像经历了一个小的平移之后，依然会产生相同的 (池化的) 特征。在很多任务中 (例如物体检测、声音识别)，我们都更希望得到具有平移不变性的特征，因为即使图像经过了平移，样例(图像)的标记仍然保持不变。例如，如果你处理一个MNIST数据集的数字，把它向左侧或右侧平移，那么不论最终的位置在哪里，你都会期望你的分类器仍然能够精确地将其分类为相同的数字。(MNIST 是一个手写数字库识别库: http://yann.lecun.com/exdb/mnist/)
池化的方式：可使用划分池化的形式，也可以使用Overlap池化的形式。此外可以使用金字塔池化的形式，每层使用不同的池化单元，形成一个金字塔特征，也用于缩放不变性，同时可以处理一定的形变。
金字塔池化，可用于处理一定的仿射形变。
CNN的结构分析—全链接层
使用卷积核提取的大量特征，产生超高的维度，同时使用MaxPooling层进行维度压缩同时选取明显特征。CNN网络通常反复堆叠Conv+MaxPooling层，变得更深，因此能提取更加全局更加高层的特征，同时不会产生太高的特征维度。对一个图片输入产生一个特征集合。
全链接层，连接所有的特征，把多个Map压缩为1个X维向量，将输出值送给分类器（如softmax分类器）
layer {
name: “fc7”
type: “InnerProduct”
bottom: “fc6”
top: “fc7”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: “gaussian”
std: 0.005
}
bias_filler {
type: “constant”
value: 1
}
}
}
layer {
name: “relu7”
type: “ReLU”
bottom: “fc7”
top: “fc7”
}
layer {
name: “drop7”
type: “Dropout”
bottom: “fc7”
top: “fc7”
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: “fc8”
type: “InnerProduct”
bottom: “fc7”
top: “fc8”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: “gaussian”
std: 0.01
}
bias_filler {
type: “constant”
value: 0
}
}
}第7层全链接层输出参数为4096，默认表示输出4096个维度向量。此外，设置的dropout率为0.5，则意味着使用了另外0.5的链接冗余，用于增强泛化能力。
CNN的结构分析—SoftMax分类器
CNN多数分类模型最终选择了MLP+SoftMax分类器，使用MLP-全连接层进行特征降维，SoftMax函数进行分类。是否因为SoftMax分类器在多分类上的无偏性，便利性？训练时参数更新的更快。
为什么一定要把最后的分类器设置为处理向量空间的SoftMax分类器，而不是直接使用xx—>11或者x1—>11的卷积方式呢？。
使用Softmax回归模型，该模型是logistic回归模型在多分类问题上的推广，在多分类问题中，类标签 可以取两个以上的值。 Softmax回归模型对于诸如MNIST手写数字分类等问题是很有用的，该问题的目的是辨识10个不同的单个数字。Softmax回归是有监督的。
SoftMax分类器： http://ufldl.stanford.edu/wiki/index.php/Softmax…
SoftMax层计算过程：
Caffe配置文件：
layer {
name: “fc8”
type: “InnerProduct”
bottom: “fc7”
top: “fc8”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: “gaussian”
std: 0.01
}
bias_filler {
type: “constant”
value: 0
}
}
}
layer {
name: “accuracy”
type: “Accuracy”
bottom: “fc8”
bottom: “label”
top: “accuracy”
include {
phase: TEST
}
}
layer {
name: “loss”
type: “SoftmaxWithLoss”
bottom: “fc8”
bottom: “label”
top: “loss”
}CNN结构总结
CNN方法对输入图像不停的卷积、pooling，提取更多的特征图，使用全链接层映射到特定维度的特征向量空间，再通过MLP或者softmax分类器获得图像目标分类。
检测可以视为选取BoundingBox和分类的结合，而后出现的DarkNet更是直接产生了回归模型。
下图为典型的DeepID模型。
Car的图片经过CNN层层特征提取和Polling过程，最后生成的Map经过压缩为m维向量，经过SoftMax函数，压缩为n维浮点数，然后经过Max()函数，取得分类结果。
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卷积神经网络系列之softmax，softmax loss和cross entropy的讲解 - ai之路(计算机视觉、深度学习、机器学习爱好者，欢迎交流！)
08-17 6.5万
我们知道卷积神经网络（CNN）在图像领域的应用已经非常广泛了，一般一个CNN网络主要包含卷积层，池化层（pooling），全连接层，损失层等。虽然现在已经开源了很多深度学习框架（比如MxNet，Caf... 来自： AI之路
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深度学习笔记8：<em>softmax</em>层的实现 - l691899397的博客 </h4>
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<span class="desc oneline">如果有什么疑问或者发现什么错误，欢迎在评论区留言，有时间我会一一回复
softmax简介
Softmax回归模型是logistic回归模型在多分类问题上的推广，在多分类问题中，待分类的类别数量大于…
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<em>softmax</em>层（无loss）是什么样的？ - 有信念，才能走的更远 </h4>
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<span class="desc oneline">名称：softmax_layer
连接：softmax层一般连接的是全连接层和loss层
这里有softmax层的来历解释，我感觉解释的很好：http://zhidao.baidu.com/lin…
来自： 有信念，才能走的更远
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<span class="desc oneline">这里我们简单介绍一下Caffe是如何实现Softmax层的，通常我们使用的是SoftmaxWithLossLayer，这里我们仅仅讲讲Caffe的SoftmaxLayer
定义输入
在Caffe的…
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卷积神经网络<em>CNN</em>（3）—— <em>FCN</em>(Fully <em>Conv</em>olutional Networks)要点解释 - fate_fjh的博客 </h4>
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<span class="desc oneline">FCN作为图像语义分割的先河，实现像素级别的分类（即end to end，pixel-wise），作者尽量用浅白的方式讲述FCN的原理与过程。...</span>
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<span class="desc oneline">1. 前言
下载AR人脸数据库，用Caffe-face中的face_example中的模型去学习，用一体机CPU方式，感觉没多久就死机了似的。觉得前一段时间急于得到成效，中间看到的很多东西都没消化…
来自： tkyjqh的专栏
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<span class="desc oneline">1. 池化(Pooling)概念
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比 像素 更高级的特…
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Abstract
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用ReLU代替sigmoid
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<span class="desc oneline">本文整理了网上几位大牛的博客，详细地讲解了CNN的基础结构与核心思想，欢迎交流。
[1]Deep learning简介
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<span class="desc oneline">首发于知乎专栏：
卷积神经网络（CNN）入门讲解
个人公众号：follow_bobo
时隔一个月，我又回来了
好了，好了
（做出停止鼓掌的手势
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SoftmaxWithLoss的由来
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<span class="desc oneline">sigmoid函数（也叫逻辑斯谛函数）：
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<span class="desc oneline"> 在之前的博文中我们着重分析了convolutional_layer类的代码结构，在这篇博文中分析对应的下采样层average_pooling_layer类： 一、下采样层的作用 下采样层的作用...</span>
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<span class="desc oneline">首先放链接：https://www.zhihu.com/question/54149221
首先，初次接触这个问题是在做图像分割遇到的。
pooling为什么可以提高感受野？
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<span class="desc oneline">一、卷积神经网络层级结构
卷积神经网络层次结构包括：
数据输入层/ Input layer
卷积计算层/ CONV layer
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池化层 / Pooling la…
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<span class="desc oneline">遇到的问题
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<span class="desc oneline">因博主非计算机专业，代码大神可忽略此文。
代码经过博主测试，准确无误！…
来自： samylee的博客
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<em>Softmax</em> 函数及其作用（含推导） - qq_26222859的博客 </h4>
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<span class="desc oneline">Softmax函数的定义及作用
Softmax是一种形如下式的函数：
P(i)=exp(θTix)∑Kk=1exp(θTkx)
其中θi和x是列向量,θTix可能被换成函数关于x…
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