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caffe 层次结构
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<p>Caffe从四个层次来理解：Blob，Layer,Net,Solver。</p>
<h3 id="Blob"><a href="#Blob" class="headerlink" title="Blob"></a>Blob</h3><p>Caffe的基本数据结构，用四维矩阵<code>Batch\*Channel\*Height\*Width</code>表示，存储了包括神经元的激活值、参数、以及相应的梯度<code>（dW,db）</code>。其中包含有<code>cpu_data、gpu_data、cpu_diff、gpu_diff、
mutable_cpu_data、mutable_gpu_data、mutable_cpu_diff、mutable_gpu_diff</code>这一堆很像的东西，分别表示存储在CPU和GPU上的数据（印象中二者的值好像是会自动同步成一致的）。其中带data的里面存<br>储的是激活值和<code>W、b</code>，diff中存储的是残差和<code>dW、db</code>。另外带<code>mutable</code>和不带<code>mutable</code>的一对指针所指的位置是相同的，只是不带<code>mutable</code>的只读，而带<code>mutable</code>的可写。</p>
<p><a href="http://imbinwang.github.io/blog/inside-caffe-code-blob">for more details</a><br>
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tensorflow执行流程
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<h1 id="计算图"><a href="#计算图" class="headerlink" title="计算图"></a>计算图</h1><p>在TensorFlow中，算法都被表示成计算图（computational graphs）。计算图也叫数据流图，可以把计算图看做是一种有向图，图中的节点表示操作，图中的边代表在不同操作之间的数据流动。</p>
<h2 id="数据流图"><a href="#数据流图" class="headerlink" title="数据流图"></a>数据流图</h2><p><img src="http://img.blog.csdn.net/20161008141454511" style="zoom:75%" /><br>
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caffe fc层源码注释
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<h2 id="基本原理"><a href="#基本原理" class="headerlink" title="基本原理"></a>基本原理</h2><p>note : 注意理解<strong>反向传播过程中，修改的对象是bottom， 这一点与前向相反，前向修改的top</strong><br><a href="http://ufldl.stanford.edu/wiki/index.php/%E5%8F%8D%E5%90%91%E4%BC%A0%E5%AF%BC%E7%AE%97%E6%B3%95">backpropgatation</a><br><a href="http://blog.csdn.net/seven_first/article/details/47378697">矩阵乘法函数</a></p>
<h2 id="成员变量"><a href="#成员变量" class="headerlink" title="成员变量"></a>成员变量</h2><figure class="highlight cpp"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">protected</span>:</div><div class="line"> <span class="keyword">int</span> M_;<span class="comment">//样本数量</span></div><div class="line"> <span class="keyword">int</span> K_;<span class="comment">//单个输入特征长度</span></div><div class="line"> <span class="keyword">int</span> N_;<span class="comment">//输出神经元数量</span></div><div class="line"> <span class="keyword">bool</span> bias_term_;<span class="comment">//是否添加偏置，上图中的（+1）。</span></div><div class="line"> Blob<Dtype> bias_multiplier_;<span class="comment">//偏置的乘子</span></div></pre></td></tr></table></figure>
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tensorflow custom learning rate decay
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<h2 id="基本原理"><a href="#基本原理" class="headerlink" title="基本原理"></a>基本原理</h2><p>通过传递一个常量tensor lr给optimizer</p>
<figure class="highlight elm"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div><div class="line">19</div><div class="line">20</div><div class="line">21</div><div class="line">22</div><div class="line">23</div><div class="line">24</div><div class="line">25</div><div class="line">26</div><div class="line">27</div><div class="line">28</div><div class="line">29</div><div class="line">30</div></pre></td><td class="code"><pre><div class="line"># <span class="keyword">import</span> related module</div><div class="line"><span class="title">from</span> tensorflow.python.framework <span class="keyword">import</span> constant_op</div><div class="line"><span class="title">from</span> tensorflow.python.framework <span class="keyword">import</span> ops</div><div class="line"><span class="title">from</span> tensorflow.python.ops <span class="keyword">import</span> control_flow_ops</div><div class="line"><span class="title">from</span> tensorflow.python.ops <span class="keyword">import</span> math_ops</div><div class="line"></div><div class="line"># implementation <span class="keyword">of</span> inv decay <span class="keyword">in</span> caffe base_lr*（<span class="number">1</span>+gamm*itear）^(-power)</div><div class="line"></div><div class="line"><span class="title">def</span> inv_decay(learning_rate, global_step, gamma, power, name=<span class="type">None</span>):</div><div class="line"> <span class="keyword">if</span> global_step is <span class="type">None</span>:</div><div class="line"> raise <span class="type">ValueError</span>(<span class="string">"global_step is required for inv_decay."</span>)</div><div class="line"> with ops.name_scope(name, <span class="string">"InvDecay"</span>,</div><div class="line"> [learning_rate, global_step,</div><div class="line"> gamma, power]) <span class="keyword">as</span> name:</div><div class="line"> learning_rate = ops.convert_to_tensor(learning_rate, name=<span class="string">"learning_rate"</span>)</div><div class="line"> d<span class="keyword">type</span> = learning_rate.dtype</div><div class="line"> global_step = math_ops.cast(global_step, d<span class="keyword">type</span>)</div><div class="line"> gamma = math_ops.cast(gamma, d<span class="keyword">type</span>)</div><div class="line"> power = math_ops.cast(power, d<span class="keyword">type</span>)</div><div class="line"> base = math_ops.multiply(gamma, global_step)</div><div class="line"></div><div class="line"> return math_ops.multiply(learning_rate, math_ops.pow(<span class="number">1</span>+base, -power),</div><div class="line"> name=name)</div><div class="line"><span class="title">lr</span> = inv_decay(learning_rate=<span class="number">0.0005</span>, global_step=get_global_step_var()<span class="number">-2800000</span>,</div><div class="line"> gamma=<span class="number">0.0001</span>,</div><div class="line"> power=<span class="number">0.75</span>)</div><div class="line"></div><div class="line"><span class="title">tf</span>.summary.scalar('lr', lr)</div><div class="line"></div><div class="line"><span class="title">optimizer</span>=tf.train.<span class="type">GradientDescentOptimizer</span>(lr)</div></pre></td></tr></table></figure>
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