Deploy to GitHub Pages: d7b20584

develop/doc/_sources/api/v2/config/layer.rst.txt

@@ -434,9 +434,9 @@ lambda_cost
 ..  autoclass:: paddle.v2.layer.lambda_cost
     :noindex:
 
-mse_cost
+square_error_cost
 --------
-..  autoclass:: paddle.v2.layer.mse_cost
+..  autoclass:: paddle.v2.layer.square_error_cost
     :noindex:
 
 rank_cost

develop/doc/_sources/getstarted/basic_usage/index_en.rst.txt

@@ -49,7 +49,7 @@ To recover this relationship between ``X`` and ``Y``, we use a neural network wi
         x = data_layer(name='x', size=1)
         y = data_layer(name='y', size=1)
         y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
-        cost = mse_cost(input=y_predict, label=y)
+        cost = square_error_cost(input=y_predict, label=y)
         outputs(cost)
 
 Some of the most fundamental usages of PaddlePaddle are demonstrated:

develop/doc/api/v2/config/layer.html

@@ -3457,14 +3457,14 @@ entire list of get gradient.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="mse-cost">
-<h3>mse_cost<a class="headerlink" href="#mse-cost" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="square-error-cost">
+<h3>square_error_cost<a class="headerlink" href="#square-error-cost" title="Permalink to this headline">¶</a></h3>
 <dl class="class">
 <dt>
-<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">mse_cost</code></dt>
-<dd><p>mean squared error cost:</p>
+<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">square_error_cost</code></dt>
+<dd><p>sum of square error cost:</p>
 <div class="math">
-\[\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2\]</div>
+\[cost = \sum_{i=1}^N(t_i-y_i)^2\]</div>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />

develop/doc/getstarted/basic_usage/index_en.html

@@ -222,7 +222,7 @@
 <span class="n">x</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">y_predict</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">LinearActivation</span><span class="p">(),</span> <span class="n">bias_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">))</span>
-<span class="n">cost</span> <span class="o">=</span> <span class="n">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+<span class="n">cost</span> <span class="o">=</span> <span class="n">square_error_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
 <span class="n">outputs</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
 </pre></div>
 </div>

develop/doc_cn/_sources/api/v2/config/layer.rst.txt

@@ -434,9 +434,9 @@ lambda_cost
 ..  autoclass:: paddle.v2.layer.lambda_cost
     :noindex:
 
-mse_cost
+square_error_cost
 --------
-..  autoclass:: paddle.v2.layer.mse_cost
+..  autoclass:: paddle.v2.layer.square_error_cost
     :noindex:
 
 rank_cost

develop/doc_cn/_sources/getstarted/basic_usage/index_cn.rst.txt

@@ -55,7 +55,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     # 线性计算网络层: ȳ = wx + b
     ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
     # 计算误差函数，即  ȳ 和真实 y 之间的距离
-    cost = mse_cost(input= ȳ, label=y)
+    cost = square_error_cost(input= ȳ, label=y)
     outputs(cost)
 
 
@@ -69,7 +69,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     
     - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。
     - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。
-    - **回归误差代价层**：回归误差代价层 `mse_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
+    - **回归误差代价层**：回归误差代价层 `square_error_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
 
 定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令：
 

develop/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt

@@ -81,9 +81,9 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
 ..	code-block:: bash
 
     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
-    cost = paddle.layer.mse_cost(input=y_predict, label=y)
+    cost = paddle.layer.square_error_cost(input=y_predict, label=y)
 
-其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。
+其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上平方误差层。
 
 最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。
 
@@ -147,4 +147,4 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
 ..  literalinclude:: src/train.py
     :linenos:
 
-有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。
\ No newline at end of file
+有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。

develop/doc_cn/_sources/howto/usage/k8s/k8s_distributed_cn.md.txt

@@ -213,7 +213,7 @@ I1116 09:10:17.123440    50 Util.cpp:130] Calling runInitFunctions
 I1116 09:10:17.123764    50 Util.cpp:143] Call runInitFunctions done.
 [WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config.
 [INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating]
-[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__mse_cost_0__]
+[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__square_error_cost_0__]
 I1116 09:10:17.392917    50 Trainer.cpp:170] trainer mode: Normal
 I1116 09:10:17.613910    50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
 I1116 09:10:17.680917    50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process

develop/doc_cn/api/v2/config/layer.html

@@ -3464,14 +3464,14 @@ entire list of get gradient.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="mse-cost">
-<h3>mse_cost<a class="headerlink" href="#mse-cost" title="永久链接至标题">¶</a></h3>
+<div class="section" id="square-error-cost">
+<h3>square_error_cost<a class="headerlink" href="#square-error-cost" title="永久链接至标题">¶</a></h3>
 <dl class="class">
 <dt>
-<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">mse_cost</code></dt>
-<dd><p>mean squared error cost:</p>
+<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">square_error_cost</code></dt>
+<dd><p>sum of square error cost:</p>
 <div class="math">
-\[\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2\]</div>
+\[cost = \sum_{i=1}^N(t_i-y_i)^2\]</div>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />

develop/doc_cn/getstarted/basic_usage/index_cn.html

@@ -230,7 +230,7 @@ y = data_layer(name='y', size=1)
 # 线性计算网络层: ȳ = wx + b
 ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
 # 计算误差函数，即  ȳ 和真实 y 之间的距离
-cost = mse_cost(input= ȳ, label=y)
+cost = square_error_cost(input= ȳ, label=y)
 outputs(cost)
 </pre></div>
 </div>
@@ -245,7 +245,7 @@ outputs(cost)
 <div><ul class="simple">
 <li><strong>数据层</strong>：数据层 <cite>data_layer</cite> 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 <cite>x</cite> 和 <cite>y</cite>。</li>
 <li><strong>全连接层</strong>：全连接层 <cite>fc_layer</cite> 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。</li>
-<li><strong>回归误差代价层</strong>：回归误差代价层 <cite>mse_cost</cite> 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。</li>
+<li><strong>回归误差代价层</strong>：回归误差代价层 <cite>square_error_cost</cite> 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。</li>
 </ul>
 </div></blockquote>
 </li>

develop/doc_cn/getstarted/concepts/use_concepts_cn.html

@@ -276,10 +276,10 @@ paddle.init<span class="o">(</span><span class="nv">use_gpu</span><span class="o
 <p>在定义输入layer之后，我们可以使用其他layer进行组合。在组合时，需要指定layer的输入来源。</p>
 <p>例如，我们可以定义如下的layer组合：</p>
 <div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">y_predict</span> <span class="o">=</span> paddle.layer.fc<span class="o">(</span><span class="nv">input</span><span class="o">=</span>x, <span class="nv">size</span><span class="o">=</span><span class="m">1</span>, <span class="nv">act</span><span class="o">=</span>paddle.activation.Linear<span class="o">())</span>
-<span class="nv">cost</span> <span class="o">=</span> paddle.layer.mse_cost<span class="o">(</span><span class="nv">input</span><span class="o">=</span>y_predict, <span class="nv">label</span><span class="o">=</span>y<span class="o">)</span>
+<span class="nv">cost</span> <span class="o">=</span> paddle.layer.square_error_cost<span class="o">(</span><span class="nv">input</span><span class="o">=</span>y_predict, <span class="nv">label</span><span class="o">=</span>y<span class="o">)</span>
 </pre></div>
 </div>
-<p>其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。</p>
+<p>其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上平方误差层。</p>
 <p>最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。</p>
 </div>
 </div>
@@ -390,7 +390,7 @@ trainer.train<span class="o">(</span>
 <span class="n">x</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
 <span class="n">y_predict</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Linear</span><span class="p">())</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-<span class="n">cost</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+<span class="n">cost</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">square_error_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
 
 <span class="c1"># create parameters</span>
 <span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>

develop/doc_cn/howto/usage/k8s/k8s_distributed_cn.html

@@ -376,7 +376,7 @@ I1116 <span class="m">09</span>:10:17.123440    <span class="m">50</span> Util.c
 I1116 <span class="m">09</span>:10:17.123764    <span class="m">50</span> Util.cpp:143<span class="o">]</span> Call runInitFunctions <span class="k">done</span>.
 <span class="o">[</span>WARNING <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,227 default_decorators.py:40<span class="o">]</span> please use keyword arguments in paddle config.
 <span class="o">[</span>INFO <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,239 networks.py:1282<span class="o">]</span> The input order is <span class="o">[</span>movie_id, title, genres, user_id, gender, age, occupation, rating<span class="o">]</span>
-<span class="o">[</span>INFO <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,239 networks.py:1289<span class="o">]</span> The output order is <span class="o">[</span>__mse_cost_0__<span class="o">]</span>
+<span class="o">[</span>INFO <span class="m">2016</span>-11-16 <span class="m">09</span>:10:17,239 networks.py:1289<span class="o">]</span> The output order is <span class="o">[</span>__square_error_cost_0__<span class="o">]</span>
 I1116 <span class="m">09</span>:10:17.392917    <span class="m">50</span> Trainer.cpp:170<span class="o">]</span> trainer mode: Normal
 I1116 <span class="m">09</span>:10:17.613910    <span class="m">50</span> PyDataProvider2.cpp:257<span class="o">]</span> loading dataprovider dataprovider::process
 I1116 <span class="m">09</span>:10:17.680917    <span class="m">50</span> PyDataProvider2.cpp:257<span class="o">]</span> loading dataprovider dataprovider::process