Deploy to GitHub Pages: 298dc895

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

@@ -467,7 +467,7 @@ lambda_cost
     :noindex:
 
 square_error_cost
---------
+-----------------
 ..  autoclass:: paddle.v2.layer.square_error_cost
     :noindex:
 
@@ -533,7 +533,7 @@ Miscs
 =====
 
 dropout
---------------
+--------
 ..  autoclass:: paddle.v2.layer.dropout
     :noindex:
 

develop/doc/_sources/api/v2/fluid/layers.rst.txt

@@ -19,17 +19,17 @@ dynamic_lstm
     :noindex:
 
 data
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.data
     :noindex:
 
 mean
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.mean
     :noindex:
 
 mul
----------
+---
 ..  autofunction:: paddle.v2.fluid.layers.mul
     :noindex:
 
@@ -45,13 +45,13 @@ elementwise_div
 
 
 dropout
----------
+-------
 ..  autofunction:: paddle.v2.fluid.layers.dropout
     :noindex:
 
 
 reshape
----------
+--------
 ..  autofunction:: paddle.v2.fluid.layers.reshape
     :noindex:
 
@@ -81,67 +81,67 @@ transpose
 
 
 sigmoid_cross_entropy_with_logits
----------
+---------------------------------
 ..  autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits
     :noindex:
 
 
 cast
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.cast
     :noindex:
 
 
 concat
----------
+-------
 ..  autofunction:: paddle.v2.fluid.layers.concat
     :noindex:
 
 
 sums
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.sums
     :noindex:
 
 
 linear_chain_crf
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.linear_chain_crf
     :noindex:
 
 
 assign
----------
+-------
 ..  autofunction:: paddle.v2.fluid.layers.embedding
     :noindex:
 
 
 split_lod_tensor
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.split_lod_tensor
     :noindex:
 
 
 merge_lod_tensor
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
     :noindex:
 
 cos_sim
----------
+--------
 ..  autofunction:: paddle.v2.fluid.layers.cos_sim
     :noindex:
 
 
 cross_entropy
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.cross_entropy
     :noindex:
 
 
 
 square_error_cost
----------
+-----------------
 ..  autofunction:: paddle.v2.fluid.layers.square_error_cost
     :noindex:
 
@@ -153,68 +153,68 @@ accuracy
 
 
 sequence_conv
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_conv
     :noindex:
 
 
 conv2d
----------
+------
 ..  autofunction:: paddle.v2.fluid.layers.conv2d
     :noindex:
 
 
 sequence_pool
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_pool
     :noindex:
 
 
 pool2d
----------
+------
 ..  autofunction:: paddle.v2.fluid.layers.pool2d
     :noindex:
 
 
 batch_norm
----------
+----------
 ..  autofunction:: paddle.v2.fluid.layers.batch_norm
     :noindex:
 
 
 beam_search_decode
----------
+------------------
 ..  autofunction:: paddle.v2.fluid.layers.beam_search_decode
     :noindex:
 
 
 lod_rank_table
----------
+--------------
 ..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
     :noindex:
 
 
 max_sequence_len
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.max_sequence_len
     :noindex:
 
 
 topk
----------
+-----
 ..  autofunction:: paddle.v2.fluid.layers.topk
     :noindex:
 
 
 lod_tensor_to_array
----------
+-------------------
 ..  autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
     :noindex:
 
 
 
 array_to_lod_tensor
----------
+-------------------
 ..  autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
     :noindex:
 
@@ -222,26 +222,26 @@ array_to_lod_tensor
 
 
 fill_constant
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.fill_constant
     :noindex:
 
 
 
 fill_constant_batch_size_like
----------
+-----------------------------
 ..  autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
     :noindex:
 
 
 ones
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.ones
     :noindex:
 
 
 zeros
----------
+-----
 ..  autofunction:: paddle.v2.fluid.layers.zeros
     :noindex:
 
@@ -253,14 +253,14 @@ increment
 
 
 array_write
----------
+-----------
 ..  autofunction:: paddle.v2.fluid.layers.array_write
     :noindex:
 
 
 
 create_array
----------
+------------
 ..  autofunction:: paddle.v2.fluid.layers.create_array
     :noindex:
 
@@ -272,31 +272,31 @@ less_than
 
 
 array_read
----------
+----------
 ..  autofunction:: paddle.v2.fluid.layers.array_read
     :noindex:
 
 
 shrink_memory
----------
+--------------
 ..  autofunction:: paddle.v2.fluid.layers.shrink_memory
     :noindex:
 
 
 array_length
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.array_length
     :noindex:
 
 
 conv2d_transpose
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
     :noindex:
 
 
 sequence_expand
----------
+---------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_expand
     :noindex:
 
@@ -308,13 +308,13 @@ lstm_unit
 
 
 sequence_softmax
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
     :noindex:
 
 
 reduce_sum
----------
+----------
 ..  autofunction:: paddle.v2.fluid.layers.reduce_sum
     :noindex:
 

develop/doc/_sources/api/v2/fluid/nets.rst.txt

@@ -3,19 +3,19 @@ Nets
 ===========
 
 simple_img_conv_pool
------------
+--------------------
 ..  autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
     :noindex:
 
 
 img_conv_group
------------
+---------------
 ..  autofunction:: paddle.v2.fluid.nets.img_conv_group
     :noindex:
 
 
 sequence_conv_pool
------------
+------------------
 ..  autofunction:: paddle.v2.fluid.nets.sequence_conv_pool
     :noindex:
 

develop/doc/_sources/api/v2/fluid/optimizer.rst.txt

@@ -18,7 +18,7 @@ SGDOptimizer
 
 
 MomentumOptimizer
------------
+-----------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: MomentumOptimizer
     :noindex:
@@ -26,14 +26,14 @@ MomentumOptimizer
 
 
 AdagradOptimizer
------------
+----------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: AdagradOptimizer
     :noindex:
 
 
 AdamOptimizer
------------
+-------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: AdamOptimizer
     :noindex:
@@ -47,7 +47,7 @@ AdamaxOptimizer
 
 
 DecayedAdagradOptimizer
------------
+-----------------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: DecayedAdagradOptimizer
     :noindex:

develop/doc/_sources/api/v2/fluid/regularizer.rst.txt

@@ -3,14 +3,14 @@ Regularizer
 ===========
 
 WeightDecayRegularizer
------------
+----------------------
 ..  automodule:: paddle.v2.fluid.regularizer
     :members: WeightDecayRegularizer
     :noindex:
 
 
 L2DecayRegularizer
------------
+------------------
 ..  automodule:: paddle.v2.fluid.regularizer
     :members: L2DecayRegularizer
     :noindex:
@@ -18,7 +18,7 @@ L2DecayRegularizer
 
 
 L1DecayRegularizer
------------
+-------------------
 ..  automodule:: paddle.v2.fluid.regularizer
     :members: L1DecayRegularizer
 

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

@@ -222,55 +222,83 @@
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fc</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>num_flatten_dims=1</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>act=None</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><p><strong>Fully Connected Layer</strong></p>
-<p>This layer accepts multiple inputs and applies a linear transformation to each input.
-If activation type is provided, the corresponding activation function is applied to the
-output of the linear transformation. For each input <span class="math">\(X\)</span>, the equation is:</p>
+<dd><blockquote>
+<div><p><strong>Fully Connected Layer</strong></p>
+<p>The fully connected layer can take multiple tensors as its inputs. It
+creates a variable (one for each input tensor) called weights for each input
+tensor, which represents a fully connected weight matrix from each input
+unit to each output unit. The fully connected layer multiplies each input
+tensor with its coresponding weight to produce an output Tensor. If
+multiple input tensors are given, the results of multiple multiplications
+will be sumed up. If bias_attr is not None, a biases variable will be
+created and added to the output. Finally, if activation is not None,
+it will be applied to the output as well.</p>
+<p>This process can be formulated as follows:</p>
 <div class="math">
-\[Out = Act(WX + b)\]</div>
-<p>In the above equation:</p>
-<blockquote>
-<div><ul class="simple">
-<li><span class="math">\(X\)</span>: Input value, a tensor with rank at least 2.</li>
-<li><span class="math">\(W\)</span>: Weight, a 2-D tensor with shape [M, N].</li>
-<li><span class="math">\(b\)</span>: Bias, a 2-D tensor with shape [M, 1].</li>
-<li><span class="math">\(Act\)</span>: Activation function.</li>
-<li><span class="math">\(Out\)</span>: Output value, same shape with <span class="math">\(X\)</span>.</li>
-</ul>
+\[Out = Act\left({\sum_{i=0}^{N-1}W_iX_i + b}\]</div>
 </div></blockquote>
-<p>All the input variables are passed in as local variables to the LayerHelper
-constructor.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>input</strong> (<em>Variable|list</em>) &#8211; Input tensors. Each tensor has a rank of atleast 2</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; Output size</li>
-<li><strong>num_flatten_dims</strong> (<em>int</em>) &#8211; Number of columns in input</li>
-<li><strong>param_attr</strong> (<em>ParamAttr|list</em>) &#8211; The parameters/weights to the FC Layer</li>
-<li><strong>bias_attr</strong> (<em>ParamAttr|list</em>) &#8211; Bias parameter for the FC layer</li>
-<li><strong>act</strong> (<em>str</em>) &#8211; Activation type</li>
-<li><strong>name</strong> (<em>str</em>) &#8211; Name/alias of the function</li>
+<p>ight)</p>
+<blockquote>
+<div><p>In the above equation:</p>
+<ul class="simple">
+<li><span class="math">\(N\)</span>: Number of the input.</li>
+<li><span class="math">\(X_i\)</span>: The input tensor.</li>
+<li><span class="math">\(W\)</span>: The weights created by this layer.</li>
+<li><span class="math">\(b\)</span>: The bias parameter created by this layer (if needed).</li>
+<li><span class="math">\(Act\)</span>: The activation funtion.</li>
+<li><span class="math">\(Out\)</span>: The output tensor.</li>
 </ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The tensor variable storing the transformation and                   non-linearity activation result.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first">Variable</p>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><p class="first last"><code class="xref py py-exc docutils literal"><span class="pre">ValueError</span></code> &#8211; If rank of input tensor is less than 2.</p>
-</td>
-</tr>
-</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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
+<dl class="docutils">
+<dt>Args:</dt>
+<dd><p class="first">input(Variable|list): The input tensor(s) to the fully connected layer.
+size(int): The number of output units in the fully connected layer.
+num_flatten_dims(int): The fc layer can accept an input tensor with more</p>
+<blockquote>
+<div>than two dimensions. If this happens, the
+multidimensional tensor will first be flattened
+into a 2-dimensional matrix. The parameter
+<cite>num_flatten_dims</cite> determines how the input tensor
+is flattened: the first <cite>num_flatten_dims</cite>
+dimensions will be flatten to form the first
+dimension of the final matrix (height of the
+matrix), and the rest <cite>rank(X) - num_col_dims</cite>
+dimensions are flattened to form the second
+dimension of the final matrix (width of the matrix).
+For example, suppose <cite>X</cite> is a 6-dimensional tensor
+with a shape [2, 3, 4, 5, 6], and
+<cite>x_num_col_dims</cite> = 3. Then, the flattened matrix
+will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
+By default, <cite>x_num_col_dims</cite> is set to 1.</div></blockquote>
+<dl class="docutils">
+<dt>param_attr(ParamAttr|list): The parameter attribute for learnable</dt>
+<dd>parameters/weights of the fully connected
+layer.</dd>
+<dt>param_initializer(ParamAttr|list): The initializer used for the</dt>
+<dd>weight/parameter. If set None,
+XavierInitializer() will be used.</dd>
+<dt>bias_attr(ParamAttr|list): The parameter attribute for the bias parameter</dt>
+<dd>for this layer. If set None, no bias will be
+added to the output units.</dd>
+<dt>bias_initializer(ParamAttr|list): The initializer used for the bias.</dt>
+<dd>If set None, then ConstantInitializer()
+will be used.</dd>
+<dt>act(str): Activation to be applied to the output of the fully connected</dt>
+<dd>layer.</dd>
+</dl>
+<p class="last">name(str): Name/alias of the fully connected layer.</p>
+</dd>
+<dt>Returns:</dt>
+<dd>Variable: The output tensor variable.</dd>
+<dt>Raises:</dt>
+<dd>ValueError: If rank of the input tensor is less than 2.</dd>
+<dt>Examples:</dt>
+<dd><div class="first last 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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
 <span class="n">fc</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">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s2">&quot;tanh&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</dd>
+</dl>
+</div></blockquote>
 </dd></dl>
 
 </div>
@@ -392,32 +420,45 @@ Duplicable: False  Optional: False</td>
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Mul Operator.</p>
-<p>This operator is used to perform matrix multiplication for input X and Y.</p>
+<p>This operator is used to perform matrix multiplication for input $X$ and $Y$.</p>
 <p>The equation is:</p>
 <blockquote>
 <div>$$Out = X * Y$$</div></blockquote>
-<p>Both the input <cite>X</cite> and <cite>Y</cite> can carry the LoD (Level of Details) information,
-or not. But the output only shares the LoD information with input <cite>X</cite>.</p>
+<p>Both the input $X$ and $Y$ can carry the LoD (Level of Details) information,
+or not. But the output only shares the LoD information with input $X$.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>x</strong> &#8211; The first input of mul op
+<li><strong>x</strong> &#8211; (Tensor), The first input tensor of mul op.
 Duplicable: False  Optional: False</li>
-<li><strong>y</strong> &#8211; The second input of mul op
+<li><strong>y</strong> &#8211; (Tensor), The second input tensor of mul op.
 Duplicable: False  Optional: False</li>
-<li><strong>x_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1) mul_op can take tensors with more than two dimensions as input <cite>X</cite>,
-in that case, tensors will be reshaped to a matrix. The matrix&#8217;s first
-dimension(column length) will be the product of tensor&#8217;s last
-<cite>num_col_dims</cite> dimensions, and the matrix&#8217;s second dimension(row length)
-will be the product of tensor&#8217;s first <cite>rank - num_col_dims</cite> dimensions.</li>
-<li><strong>y_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1) mul_op can take tensors with more than two dimensions as input <cite>Y</cite>,
-in that case, tensors will be reshaped to a matrix. Just like input <cite>X</cite>.</li>
+<li><strong>x_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1), The mul_op can take tensors with more than two
+dimensions as its inputs. If the input $X$ is a tensor with more
+than two dimensions, $X$ will be flattened into a two-dimensional
+matrix first. The flattening rule is: the first <cite>num_col_dims</cite>
+will be flattened to form the first dimension of the final matrix
+(the height of the matrix), and the rest <cite>rank(X) - num_col_dims</cite>
+dimensions are flattened to form the second dimension of the final
+matrix (the width of the matrix). As a result, height of the
+flattened matrix is equal to the product of $X$&#8217;s first
+<cite>x_num_col_dims</cite> dimensions&#8217; sizes, and width of the flattened
+matrix is equal to the product of $X$&#8217;s last <cite>rank(x) - num_col_dims</cite>
+dimensions&#8217; size. For example, suppose $X$ is a 6-dimensional
+tensor with the shape [2, 3, 4, 5, 6], and <cite>x_num_col_dims</cite> = 3.
+Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] =
+[24, 30].</li>
+<li><strong>y_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1), The mul_op can take tensors with more than two,
+dimensions as its inputs. If the input $Y$ is a tensor with more
+than two dimensions, $Y$ will be flattened into a two-dimensional
+matrix first. The attribute <cite>y_num_col_dims</cite> determines how $Y$ is
+flattened. See comments of <cite>x_num_col_dims</cite> for more details.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of mul op</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">(Tensor), The output tensor of mul op.</p>
 </td>
 </tr>
 </tbody>

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

@@ -241,9 +241,9 @@ of its implementations</p>
 </dd></dl>
 
 </div>
-<div class="section" id="l1decayregularizer">
-<h2>L1DecayRegularizer<a class="headerlink" href="#l1decayregularizer" title="Permalink to this headline">¶</a></h2>
-<span class="target" id="module-paddle.v2.fluid.regularizer"></span><dl class="class">
+<div class="section" id="module-paddle.v2.fluid.regularizer">
+<span id="l1decayregularizer"></span><h2>L1DecayRegularizer<a class="headerlink" href="#module-paddle.v2.fluid.regularizer" title="Permalink to this headline">¶</a></h2>
+<dl class="class">
 <dt id="paddle.v2.fluid.regularizer.L1DecayRegularizer">
 <em class="property">class </em><code class="descclassname">paddle.v2.fluid.regularizer.</code><code class="descname">L1DecayRegularizer</code><span class="sig-paren">(</span><em>regularization_coeff=0.0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.fluid.regularizer.L1DecayRegularizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements the L1 Weight Decay Regularization</p>

develop/doc/operators.json

@@ -1212,23 +1212,23 @@
  } ] 
 },{
  "type" : "mul",
- "comment" : "\nMul Operator. \n\nThis operator is used to perform matrix multiplication for input X and Y.\n\nThe equation is:\n\n    $$Out = X * Y$$\n\nBoth the input `X` and `Y` can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input `X`.\n\n",
+ "comment" : "\nMul Operator.\n\nThis operator is used to perform matrix multiplication for input $X$ and $Y$.\n\nThe equation is:\n\n    $$Out = X * Y$$\n\nBoth the input $X$ and $Y$ can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input $X$.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The first input of mul op",
+   "comment" : "(Tensor), The first input tensor of mul op.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "The second input of mul op",
+   "comment" : "(Tensor), The second input tensor of mul op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of mul op",
+   "comment" : "(Tensor), The output tensor of mul op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
@@ -1236,12 +1236,12 @@
  { 
    "name" : "x_num_col_dims",
    "type" : "int",
-   "comment" : "(int, default 1) mul_op can take tensors with more than two dimensions as input `X`,\n            in that case, tensors will be reshaped to a matrix. The matrix's first\n            dimension(column length) will be the product of tensor's last\n            `num_col_dims` dimensions, and the matrix's second dimension(row length)\n            will be the product of tensor's first `rank - num_col_dims` dimensions.\n        ",
+   "comment" : "(int, default 1), The mul_op can take tensors with more than two\n              dimensions as its inputs. If the input $X$ is a tensor with more\n              than two dimensions, $X$ will be flattened into a two-dimensional\n              matrix first. The flattening rule is: the first `num_col_dims`\n              will be flattened to form the first dimension of the final matrix\n              (the height of the matrix), and the rest `rank(X) - num_col_dims`\n              dimensions are flattened to form the second dimension of the final\n              matrix (the width of the matrix). As a result, height of the\n              flattened matrix is equal to the product of $X$'s first\n              `x_num_col_dims` dimensions' sizes, and width of the flattened\n              matrix is equal to the product of $X$'s last `rank(x) - num_col_dims`\n              dimensions' size. For example, suppose $X$ is a 6-dimensional\n              tensor with the shape [2, 3, 4, 5, 6], and `x_num_col_dims` = 3.\n              Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] =\n              [24, 30].\n        ",
    "generated" : 0
  }, { 
    "name" : "y_num_col_dims",
    "type" : "int",
-   "comment" : "(int, default 1) mul_op can take tensors with more than two dimensions as input `Y`,\n             in that case, tensors will be reshaped to a matrix. Just like input `X`.\n        ",
+   "comment" : "(int, default 1), The mul_op can take tensors with more than two,\n              dimensions as its inputs. If the input $Y$ is a tensor with more\n              than two dimensions, $Y$ will be flattened into a two-dimensional\n              matrix first. The attribute `y_num_col_dims` determines how $Y$ is\n              flattened. See comments of `x_num_col_dims` for more details.\n        ",
    "generated" : 0
  } ] 
 },{

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

@@ -467,7 +467,7 @@ lambda_cost
     :noindex:
 
 square_error_cost
---------
+-----------------
 ..  autoclass:: paddle.v2.layer.square_error_cost
     :noindex:
 
@@ -533,7 +533,7 @@ Miscs
 =====
 
 dropout
---------------
+--------
 ..  autoclass:: paddle.v2.layer.dropout
     :noindex:
 

develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt

@@ -19,17 +19,17 @@ dynamic_lstm
     :noindex:
 
 data
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.data
     :noindex:
 
 mean
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.mean
     :noindex:
 
 mul
----------
+---
 ..  autofunction:: paddle.v2.fluid.layers.mul
     :noindex:
 
@@ -45,13 +45,13 @@ elementwise_div
 
 
 dropout
----------
+-------
 ..  autofunction:: paddle.v2.fluid.layers.dropout
     :noindex:
 
 
 reshape
----------
+--------
 ..  autofunction:: paddle.v2.fluid.layers.reshape
     :noindex:
 
@@ -81,67 +81,67 @@ transpose
 
 
 sigmoid_cross_entropy_with_logits
----------
+---------------------------------
 ..  autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits
     :noindex:
 
 
 cast
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.cast
     :noindex:
 
 
 concat
----------
+-------
 ..  autofunction:: paddle.v2.fluid.layers.concat
     :noindex:
 
 
 sums
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.sums
     :noindex:
 
 
 linear_chain_crf
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.linear_chain_crf
     :noindex:
 
 
 assign
----------
+-------
 ..  autofunction:: paddle.v2.fluid.layers.embedding
     :noindex:
 
 
 split_lod_tensor
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.split_lod_tensor
     :noindex:
 
 
 merge_lod_tensor
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
     :noindex:
 
 cos_sim
----------
+--------
 ..  autofunction:: paddle.v2.fluid.layers.cos_sim
     :noindex:
 
 
 cross_entropy
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.cross_entropy
     :noindex:
 
 
 
 square_error_cost
----------
+-----------------
 ..  autofunction:: paddle.v2.fluid.layers.square_error_cost
     :noindex:
 
@@ -153,68 +153,68 @@ accuracy
 
 
 sequence_conv
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_conv
     :noindex:
 
 
 conv2d
----------
+------
 ..  autofunction:: paddle.v2.fluid.layers.conv2d
     :noindex:
 
 
 sequence_pool
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_pool
     :noindex:
 
 
 pool2d
----------
+------
 ..  autofunction:: paddle.v2.fluid.layers.pool2d
     :noindex:
 
 
 batch_norm
----------
+----------
 ..  autofunction:: paddle.v2.fluid.layers.batch_norm
     :noindex:
 
 
 beam_search_decode
----------
+------------------
 ..  autofunction:: paddle.v2.fluid.layers.beam_search_decode
     :noindex:
 
 
 lod_rank_table
----------
+--------------
 ..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
     :noindex:
 
 
 max_sequence_len
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.max_sequence_len
     :noindex:
 
 
 topk
----------
+-----
 ..  autofunction:: paddle.v2.fluid.layers.topk
     :noindex:
 
 
 lod_tensor_to_array
----------
+-------------------
 ..  autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
     :noindex:
 
 
 
 array_to_lod_tensor
----------
+-------------------
 ..  autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
     :noindex:
 
@@ -222,26 +222,26 @@ array_to_lod_tensor
 
 
 fill_constant
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.fill_constant
     :noindex:
 
 
 
 fill_constant_batch_size_like
----------
+-----------------------------
 ..  autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
     :noindex:
 
 
 ones
----------
+----
 ..  autofunction:: paddle.v2.fluid.layers.ones
     :noindex:
 
 
 zeros
----------
+-----
 ..  autofunction:: paddle.v2.fluid.layers.zeros
     :noindex:
 
@@ -253,14 +253,14 @@ increment
 
 
 array_write
----------
+-----------
 ..  autofunction:: paddle.v2.fluid.layers.array_write
     :noindex:
 
 
 
 create_array
----------
+------------
 ..  autofunction:: paddle.v2.fluid.layers.create_array
     :noindex:
 
@@ -272,31 +272,31 @@ less_than
 
 
 array_read
----------
+----------
 ..  autofunction:: paddle.v2.fluid.layers.array_read
     :noindex:
 
 
 shrink_memory
----------
+--------------
 ..  autofunction:: paddle.v2.fluid.layers.shrink_memory
     :noindex:
 
 
 array_length
----------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.array_length
     :noindex:
 
 
 conv2d_transpose
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
     :noindex:
 
 
 sequence_expand
----------
+---------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_expand
     :noindex:
 
@@ -308,13 +308,13 @@ lstm_unit
 
 
 sequence_softmax
----------
+----------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
     :noindex:
 
 
 reduce_sum
----------
+----------
 ..  autofunction:: paddle.v2.fluid.layers.reduce_sum
     :noindex:
 

develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt

@@ -3,19 +3,19 @@ Nets
 ===========
 
 simple_img_conv_pool
------------
+--------------------
 ..  autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
     :noindex:
 
 
 img_conv_group
------------
+---------------
 ..  autofunction:: paddle.v2.fluid.nets.img_conv_group
     :noindex:
 
 
 sequence_conv_pool
------------
+------------------
 ..  autofunction:: paddle.v2.fluid.nets.sequence_conv_pool
     :noindex:
 

develop/doc_cn/_sources/api/v2/fluid/optimizer.rst.txt

@@ -18,7 +18,7 @@ SGDOptimizer
 
 
 MomentumOptimizer
------------
+-----------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: MomentumOptimizer
     :noindex:
@@ -26,14 +26,14 @@ MomentumOptimizer
 
 
 AdagradOptimizer
------------
+----------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: AdagradOptimizer
     :noindex:
 
 
 AdamOptimizer
------------
+-------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: AdamOptimizer
     :noindex:
@@ -47,7 +47,7 @@ AdamaxOptimizer
 
 
 DecayedAdagradOptimizer
------------
+-----------------------
 ..  automodule:: paddle.v2.fluid.optimizer
     :members: DecayedAdagradOptimizer
     :noindex:

develop/doc_cn/_sources/api/v2/fluid/regularizer.rst.txt

@@ -3,14 +3,14 @@ Regularizer
 ===========
 
 WeightDecayRegularizer
------------
+----------------------
 ..  automodule:: paddle.v2.fluid.regularizer
     :members: WeightDecayRegularizer
     :noindex:
 
 
 L2DecayRegularizer
------------
+------------------
 ..  automodule:: paddle.v2.fluid.regularizer
     :members: L2DecayRegularizer
     :noindex:
@@ -18,7 +18,7 @@ L2DecayRegularizer
 
 
 L1DecayRegularizer
------------
+-------------------
 ..  automodule:: paddle.v2.fluid.regularizer
     :members: L1DecayRegularizer
 

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

@@ -235,55 +235,83 @@
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fc</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>num_flatten_dims=1</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>act=None</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><p><strong>Fully Connected Layer</strong></p>
-<p>This layer accepts multiple inputs and applies a linear transformation to each input.
-If activation type is provided, the corresponding activation function is applied to the
-output of the linear transformation. For each input <span class="math">\(X\)</span>, the equation is:</p>
+<dd><blockquote>
+<div><p><strong>Fully Connected Layer</strong></p>
+<p>The fully connected layer can take multiple tensors as its inputs. It
+creates a variable (one for each input tensor) called weights for each input
+tensor, which represents a fully connected weight matrix from each input
+unit to each output unit. The fully connected layer multiplies each input
+tensor with its coresponding weight to produce an output Tensor. If
+multiple input tensors are given, the results of multiple multiplications
+will be sumed up. If bias_attr is not None, a biases variable will be
+created and added to the output. Finally, if activation is not None,
+it will be applied to the output as well.</p>
+<p>This process can be formulated as follows:</p>
 <div class="math">
-\[Out = Act(WX + b)\]</div>
-<p>In the above equation:</p>
-<blockquote>
-<div><ul class="simple">
-<li><span class="math">\(X\)</span>: Input value, a tensor with rank at least 2.</li>
-<li><span class="math">\(W\)</span>: Weight, a 2-D tensor with shape [M, N].</li>
-<li><span class="math">\(b\)</span>: Bias, a 2-D tensor with shape [M, 1].</li>
-<li><span class="math">\(Act\)</span>: Activation function.</li>
-<li><span class="math">\(Out\)</span>: Output value, same shape with <span class="math">\(X\)</span>.</li>
-</ul>
+\[Out = Act\left({\sum_{i=0}^{N-1}W_iX_i + b}\]</div>
 </div></blockquote>
-<p>All the input variables are passed in as local variables to the LayerHelper
-constructor.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
-<li><strong>input</strong> (<em>Variable|list</em>) &#8211; Input tensors. Each tensor has a rank of atleast 2</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; Output size</li>
-<li><strong>num_flatten_dims</strong> (<em>int</em>) &#8211; Number of columns in input</li>
-<li><strong>param_attr</strong> (<em>ParamAttr|list</em>) &#8211; The parameters/weights to the FC Layer</li>
-<li><strong>bias_attr</strong> (<em>ParamAttr|list</em>) &#8211; Bias parameter for the FC layer</li>
-<li><strong>act</strong> (<em>str</em>) &#8211; Activation type</li>
-<li><strong>name</strong> (<em>str</em>) &#8211; Name/alias of the function</li>
+<p>ight)</p>
+<blockquote>
+<div><p>In the above equation:</p>
+<ul class="simple">
+<li><span class="math">\(N\)</span>: Number of the input.</li>
+<li><span class="math">\(X_i\)</span>: The input tensor.</li>
+<li><span class="math">\(W\)</span>: The weights created by this layer.</li>
+<li><span class="math">\(b\)</span>: The bias parameter created by this layer (if needed).</li>
+<li><span class="math">\(Act\)</span>: The activation funtion.</li>
+<li><span class="math">\(Out\)</span>: The output tensor.</li>
 </ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The tensor variable storing the transformation and                   non-linearity activation result.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first">Variable</p>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><p class="first last"><code class="xref py py-exc docutils literal"><span class="pre">ValueError</span></code> &#8211; If rank of input tensor is less than 2.</p>
-</td>
-</tr>
-</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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
+<dl class="docutils">
+<dt>Args:</dt>
+<dd><p class="first">input(Variable|list): The input tensor(s) to the fully connected layer.
+size(int): The number of output units in the fully connected layer.
+num_flatten_dims(int): The fc layer can accept an input tensor with more</p>
+<blockquote>
+<div>than two dimensions. If this happens, the
+multidimensional tensor will first be flattened
+into a 2-dimensional matrix. The parameter
+<cite>num_flatten_dims</cite> determines how the input tensor
+is flattened: the first <cite>num_flatten_dims</cite>
+dimensions will be flatten to form the first
+dimension of the final matrix (height of the
+matrix), and the rest <cite>rank(X) - num_col_dims</cite>
+dimensions are flattened to form the second
+dimension of the final matrix (width of the matrix).
+For example, suppose <cite>X</cite> is a 6-dimensional tensor
+with a shape [2, 3, 4, 5, 6], and
+<cite>x_num_col_dims</cite> = 3. Then, the flattened matrix
+will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
+By default, <cite>x_num_col_dims</cite> is set to 1.</div></blockquote>
+<dl class="docutils">
+<dt>param_attr(ParamAttr|list): The parameter attribute for learnable</dt>
+<dd>parameters/weights of the fully connected
+layer.</dd>
+<dt>param_initializer(ParamAttr|list): The initializer used for the</dt>
+<dd>weight/parameter. If set None,
+XavierInitializer() will be used.</dd>
+<dt>bias_attr(ParamAttr|list): The parameter attribute for the bias parameter</dt>
+<dd>for this layer. If set None, no bias will be
+added to the output units.</dd>
+<dt>bias_initializer(ParamAttr|list): The initializer used for the bias.</dt>
+<dd>If set None, then ConstantInitializer()
+will be used.</dd>
+<dt>act(str): Activation to be applied to the output of the fully connected</dt>
+<dd>layer.</dd>
+</dl>
+<p class="last">name(str): Name/alias of the fully connected layer.</p>
+</dd>
+<dt>Returns:</dt>
+<dd>Variable: The output tensor variable.</dd>
+<dt>Raises:</dt>
+<dd>ValueError: If rank of the input tensor is less than 2.</dd>
+<dt>Examples:</dt>
+<dd><div class="first last 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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
 <span class="n">fc</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">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s2">&quot;tanh&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</dd>
+</dl>
+</div></blockquote>
 </dd></dl>
 
 </div>
@@ -405,32 +433,45 @@ Duplicable: False  Optional: False</td>
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Mul Operator.</p>
-<p>This operator is used to perform matrix multiplication for input X and Y.</p>
+<p>This operator is used to perform matrix multiplication for input $X$ and $Y$.</p>
 <p>The equation is:</p>
 <blockquote>
 <div>$$Out = X * Y$$</div></blockquote>
-<p>Both the input <cite>X</cite> and <cite>Y</cite> can carry the LoD (Level of Details) information,
-or not. But the output only shares the LoD information with input <cite>X</cite>.</p>
+<p>Both the input $X$ and $Y$ can carry the LoD (Level of Details) information,
+or not. But the output only shares the LoD information with input $X$.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
-<li><strong>x</strong> &#8211; The first input of mul op
+<li><strong>x</strong> &#8211; (Tensor), The first input tensor of mul op.
 Duplicable: False  Optional: False</li>
-<li><strong>y</strong> &#8211; The second input of mul op
+<li><strong>y</strong> &#8211; (Tensor), The second input tensor of mul op.
 Duplicable: False  Optional: False</li>
-<li><strong>x_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1) mul_op can take tensors with more than two dimensions as input <cite>X</cite>,
-in that case, tensors will be reshaped to a matrix. The matrix&#8217;s first
-dimension(column length) will be the product of tensor&#8217;s last
-<cite>num_col_dims</cite> dimensions, and the matrix&#8217;s second dimension(row length)
-will be the product of tensor&#8217;s first <cite>rank - num_col_dims</cite> dimensions.</li>
-<li><strong>y_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1) mul_op can take tensors with more than two dimensions as input <cite>Y</cite>,
-in that case, tensors will be reshaped to a matrix. Just like input <cite>X</cite>.</li>
+<li><strong>x_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1), The mul_op can take tensors with more than two
+dimensions as its inputs. If the input $X$ is a tensor with more
+than two dimensions, $X$ will be flattened into a two-dimensional
+matrix first. The flattening rule is: the first <cite>num_col_dims</cite>
+will be flattened to form the first dimension of the final matrix
+(the height of the matrix), and the rest <cite>rank(X) - num_col_dims</cite>
+dimensions are flattened to form the second dimension of the final
+matrix (the width of the matrix). As a result, height of the
+flattened matrix is equal to the product of $X$&#8217;s first
+<cite>x_num_col_dims</cite> dimensions&#8217; sizes, and width of the flattened
+matrix is equal to the product of $X$&#8217;s last <cite>rank(x) - num_col_dims</cite>
+dimensions&#8217; size. For example, suppose $X$ is a 6-dimensional
+tensor with the shape [2, 3, 4, 5, 6], and <cite>x_num_col_dims</cite> = 3.
+Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] =
+[24, 30].</li>
+<li><strong>y_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1), The mul_op can take tensors with more than two,
+dimensions as its inputs. If the input $Y$ is a tensor with more
+than two dimensions, $Y$ will be flattened into a two-dimensional
+matrix first. The attribute <cite>y_num_col_dims</cite> determines how $Y$ is
+flattened. See comments of <cite>x_num_col_dims</cite> for more details.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">The output of mul op</p>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">(Tensor), The output tensor of mul op.</p>
 </td>
 </tr>
 </tbody>

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

@@ -254,9 +254,9 @@ of its implementations</p>
 </dd></dl>
 
 </div>
-<div class="section" id="l1decayregularizer">
-<h2>L1DecayRegularizer<a class="headerlink" href="#l1decayregularizer" title="永久链接至标题">¶</a></h2>
-<span class="target" id="module-paddle.v2.fluid.regularizer"></span><dl class="class">
+<div class="section" id="module-paddle.v2.fluid.regularizer">
+<span id="l1decayregularizer"></span><h2>L1DecayRegularizer<a class="headerlink" href="#module-paddle.v2.fluid.regularizer" title="永久链接至标题">¶</a></h2>
+<dl class="class">
 <dt id="paddle.v2.fluid.regularizer.L1DecayRegularizer">
 <em class="property">class </em><code class="descclassname">paddle.v2.fluid.regularizer.</code><code class="descname">L1DecayRegularizer</code><span class="sig-paren">(</span><em>regularization_coeff=0.0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.fluid.regularizer.L1DecayRegularizer" title="永久链接至目标">¶</a></dt>
 <dd><p>Implements the L1 Weight Decay Regularization</p>