Deploy to GitHub Pages: df927768

develop/doc/api/v2/fluid/layers.html

@@ -1628,7 +1628,8 @@ obtained from the <cite>input</cite> tensor.</p>
 </tbody>
 </table>
 <p class="rubric">Examples</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fill_constant</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fill_constant_batch_size_like</span><span class="p">(</span>
+    <span class="nb">input</span><span class="o">=</span><span class="n">like</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </dd></dl>

develop/doc/operators.json

@@ -3133,7 +3133,7 @@
  } ] 
 },{
  "type" : "norm",
- "comment" : "\n       \"Input shape: $(N, C, H, W)$\n        Sclae shape: $(C, 1)$\n        Output shape: $(N, C, H, W)$\n        Where\n        forward\n          $$\n            [\\frac {x_{1}}{\\sqrt{\\sum{x_{i}^{2}}}} \\frac {x_{2}}{\\sqrt{\\sum{x_{i}^{2}}}} \\frac {x_{3}}{\\sqrt{\\sum{x_{i}^{2}}}} \\cdot  \\cdot  \\cdot \\frac {x_{n}}{\\sqrt{\\sum{x_{i}^{2}}}}]\n          $$\n        backward\n          $$\n            \\frac{\\frac{\\mathrm{d}L }{\\mathrm{d}y_{1}} - \\frac {x_{1}\\sum {\\frac{\\mathrm{d} L}{\\mathrm{d} y_{j}}}x_{j}}{\\sum x_{j}^{2}} }{\\sqrt{\\sum{x_{j}^{2}}}}\n          $$\n        ",
+ "comment" : "\n       \"Input shape: $(N, C, H, W)$\n        Scale shape: $(C, 1)$\n        Output shape: $(N, C, H, W)$\n        Where\n        forward\n          $$\n            [\\frac {x_{1}}{\\sqrt{\\sum{x_{i}^{2}}}} \\frac {x_{2}}{\\sqrt{\\sum{x_{i}^{2}}}} \\frac {x_{3}}{\\sqrt{\\sum{x_{i}^{2}}}} \\cdot  \\cdot  \\cdot \\frac {x_{n}}{\\sqrt{\\sum{x_{i}^{2}}}}]\n          $$\n        backward\n          $$\n            \\frac{\\frac{\\mathrm{d}L }{\\mathrm{d}y_{1}} - \\frac {x_{1}\\sum {\\frac{\\mathrm{d} L}{\\mathrm{d} y_{j}}}x_{j}}{\\sum x_{j}^{2}} }{\\sqrt{\\sum{x_{j}^{2}}}}\n          $$\n        ",
  "inputs" : [ 
  { 
    "name" : "X",

develop/doc_cn/api/v2/fluid/layers.html

@@ -1641,7 +1641,8 @@ obtained from the <cite>input</cite> tensor.</p>
 </tbody>
 </table>
 <p class="rubric">Examples</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fill_constant</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fill_constant_batch_size_like</span><span class="p">(</span>
+    <span class="nb">input</span><span class="o">=</span><span class="n">like</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </dd></dl>