Deploy to GitHub Pages: f66c17b6

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

@@ -719,18 +719,29 @@ Duplicable: False  Optional: False</li>
 <dd><p>Transpose Operator.</p>
 <p>The input tensor will be permuted according to the axis values given.
 The op functions is similar to how numpy.transpose works in python.</p>
-<p>For example: input = numpy.arange(6).reshape((2,3))
-the input is:
-array([[0, 1, 2],</p>
+<p>For example:</p>
 <blockquote>
-<div>[3, 4, 5]])</div></blockquote>
-<p>given axis is: [1, 0]</p>
-<p>output = input.transpose(axis)
+<div><div class="highlight-text"><div class="highlight"><pre><span></span>input = numpy.arange(6).reshape((2,3))
+
+the input is:
+
+array([[0, 1, 2],
+       [3, 4, 5]])
+
+given axis is:
+
+[1, 0]
+
+output = input.transpose(axis)
+
 then the output is:
-array([[0, 3],</p>
-<blockquote>
-<div>[1, 4],
-[2, 5]])</div></blockquote>
+
+array([[0, 3],
+       [1, 4],
+       [2, 5]])
+</pre></div>
+</div>
+</div></blockquote>
 <p>So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
 the output tensor shape will be (N, H, W, C)</p>
 <table class="docutils field-list" frame="void" rules="none">

develop/doc/operators.json

@@ -2214,7 +2214,7 @@
  } ] 
 },{
  "type" : "transpose",
- "comment" : "\nTranspose Operator.\n\nThe input tensor will be permuted according to the axis values given.\nThe op functions is similar to how numpy.transpose works in python.\n\nFor example: input = numpy.arange(6).reshape((2,3))\nthe input is:\narray([[0, 1, 2],\n      [3, 4, 5]])\ngiven axis is: [1, 0]\n\noutput = input.transpose(axis)\nthen the output is:\narray([[0, 3],\n       [1, 4],\n       [2, 5]])\nSo, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},\nthe output tensor shape will be (N, H, W, C)\n\n",
+ "comment" : "\nTranspose Operator.\n\nThe input tensor will be permuted according to the axis values given.\nThe op functions is similar to how numpy.transpose works in python.\n\nFor example:\n\n    .. code-block:: text\n\n      input = numpy.arange(6).reshape((2,3))\n\n      the input is:\n\n      array([[0, 1, 2],\n             [3, 4, 5]])\n\n      given axis is:\n\n      [1, 0]\n\n      output = input.transpose(axis)\n\n      then the output is:\n\n      array([[0, 3],\n             [1, 4],\n             [2, 5]])\n\nSo, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},\nthe output tensor shape will be (N, H, W, C)\n\n",
  "inputs" : [ 
  { 
    "name" : "X",

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

@@ -732,18 +732,29 @@ Duplicable: False  Optional: False</li>
 <dd><p>Transpose Operator.</p>
 <p>The input tensor will be permuted according to the axis values given.
 The op functions is similar to how numpy.transpose works in python.</p>
-<p>For example: input = numpy.arange(6).reshape((2,3))
-the input is:
-array([[0, 1, 2],</p>
+<p>For example:</p>
 <blockquote>
-<div>[3, 4, 5]])</div></blockquote>
-<p>given axis is: [1, 0]</p>
-<p>output = input.transpose(axis)
+<div><div class="highlight-text"><div class="highlight"><pre><span></span>input = numpy.arange(6).reshape((2,3))
+
+the input is:
+
+array([[0, 1, 2],
+       [3, 4, 5]])
+
+given axis is:
+
+[1, 0]
+
+output = input.transpose(axis)
+
 then the output is:
-array([[0, 3],</p>
-<blockquote>
-<div>[1, 4],
-[2, 5]])</div></blockquote>
+
+array([[0, 3],
+       [1, 4],
+       [2, 5]])
+</pre></div>
+</div>
+</div></blockquote>
 <p>So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
 the output tensor shape will be (N, H, W, C)</p>
 <table class="docutils field-list" frame="void" rules="none">