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--- a/develop/doc/_sources/design/var_desc.md.txt
+++ b/develop/doc/_sources/design/var_desc.md.txt
+## Background
+PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
+
+PaddlePaddle use proto message to describe compile time graph for
+
+1. Computation graph should be able to be saved to a file.
+1. In distributed training, the graph will be serialized and send to multiple workers.
+
+The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.
+
+| |compile time|runtime|
+|---|---|---|
+|Data|VarDesc(proto)|Variable(cpp)|
+|Operation|OpDesc(proto)|Operator(cpp)|
+
+
+## Definition of VarDesc
+
+A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.
+
+```proto
+message VarDesc {
+  required string name = 1;
+  optional LoDTensorDesc lod_tensor = 2;
+}
+```
+
+## Definition of LodTensorDesc
+
+```proto
+enum DataType {
+  BOOL = 0;
+  INT16 = 1;
+  INT32 = 2;
+  INT64 = 3;
+  FP16 = 4;
+  FP32 = 5;
+  FP64 = 6;
+}
+
+message LoDTensorDesc {
+  required DataType data_type = 1;
+  repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
+  optional int32 lod_level = 3 [default=0];
+}
+```
+
+## Definition of Variable in Python
+
+In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
+
+```python
+image = Variable(dims=[-1, 640, 480])
+# fc1 and fc2 are both Variable
+fc1 = layer.fc(input=image, output_size=10)
+fc2 = layer.fc(input=fc1, output_size=20)
+```
+### what should class `Variable` Have
+1. `name`.a name of string type is used to mark the value of the Variable.
+1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.
+1. `operator`. Variable should record which operator produce itself. The reaon is:
+  - we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
+
+In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.
+
+```python
+import VarDesc
+import LoDTensorDesc
+import framework
+
+def AddInitialOperator(variable, initializer):
+	# add an initialize Operator to block to init this Variable
+
+class Variable(object):
+   def __init__(self, name, dims, type, initializer):
+      self._block = get_default_block()
+      self._name = name
+      self.op = None
+
+      tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
+      _var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
+      self._var = framework.CreateVar(_var_desc)
+      self._block.add_var(self)
+
+      # add initial op according to initializer
+      if initializer is not None:
+          AddInitialOperator(self, initializer)
+
+   def dims(self):
+      return self._var.dims()
+
+   def data_type(self):
+       return self._var.data_type()
+
+   def to_proto(self):
+       pass
+```
+
+Then we can use this Variable to create a fc layer in Python.
+
+```python
+import paddle as pd
+
+def flatten_size(X, num_flatten_dims):
+  prod = 1 # of last num_flatten_dims
+  for i in xrange(num_flatten_dims):
+    prod = prod * X.dims[-i-1]
+  return prod
+
+def layer.fc(X, output_size, num_flatten_dims):
+  W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
+  b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
+  out = Variable(type=FP32)
+  y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
+  pd.InferShape(y)
+  return out
+
+x = Variable(dims=[-1, 640, 480])
+y = layer.fc(x, output_size=100)
+z = layer.fc(y, output_size=200)
+
+paddle.eval(targets=[z], ...)
+print(z)
+```
--- a/develop/doc/design/var_desc.html
+++ b/develop/doc/design/var_desc.html
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+            
+  <div class="section" id="background">
+<span id="background"></span><h1>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h1>
+<p>PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.</p>
+<p>PaddlePaddle use proto message to describe compile time graph for</p>
+<ol class="simple">
+<li>Computation graph should be able to be saved to a file.</li>
+<li>In distributed training, the graph will be serialized and send to multiple workers.</li>
+</ol>
+<p>The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.</p>
+<p>| |compile time|runtime|
+|&#8212;|&#8212;|&#8212;|
+|Data|VarDesc(proto)|Variable(cpp)|
+|Operation|OpDesc(proto)|Operator(cpp)|</p>
+</div>
+<div class="section" id="definition-of-vardesc">
+<span id="definition-of-vardesc"></span><h1>Definition of VarDesc<a class="headerlink" href="#definition-of-vardesc" title="Permalink to this headline">¶</a></h1>
+<p>A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.</p>
+<div class="highlight-proto"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">VarDesc</span> <span class="p">{</span>
+  <span class="k">required</span> <span class="kt">string</span> <span class="na">name</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
+  <span class="k">optional</span> <span class="n">LoDTensorDesc</span> <span class="na">lod_tensor</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="definition-of-lodtensordesc">
+<span id="definition-of-lodtensordesc"></span><h1>Definition of LodTensorDesc<a class="headerlink" href="#definition-of-lodtensordesc" title="Permalink to this headline">¶</a></h1>
+<div class="highlight-proto"><div class="highlight"><pre><span></span><span class="kd">enum</span> <span class="n">DataType</span> <span class="p">{</span>
+  <span class="na">BOOL</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
+  <span class="na">INT16</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
+  <span class="na">INT32</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
+  <span class="na">INT64</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
+  <span class="na">FP16</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
+  <span class="na">FP32</span> <span class="o">=</span> <span class="mi">5</span><span class="p">;</span>
+  <span class="na">FP64</span> <span class="o">=</span> <span class="mi">6</span><span class="p">;</span>
+<span class="p">}</span>
+
+<span class="kd">message</span> <span class="nc">LoDTensorDesc</span> <span class="p">{</span>
+  <span class="k">required</span> <span class="n">DataType</span> <span class="na">data_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
+  <span class="k">repeated</span> <span class="kt">int32</span> <span class="na">dims</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span> <span class="c1">// [UNK, 640, 480] is saved as [-1, 640, 480]</span>
+  <span class="k">optional</span> <span class="kt">int32</span> <span class="na">lod_level</span> <span class="o">=</span> <span class="mi">3</span> <span class="p">[</span><span class="k">default</span><span class="o">=</span><span class="mi">0</span><span class="p">];</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="definition-of-variable-in-python">
+<span id="definition-of-variable-in-python"></span><h1>Definition of Variable in Python<a class="headerlink" href="#definition-of-variable-in-python" title="Permalink to this headline">¶</a></h1>
+<p>In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class <code class="docutils literal"><span class="pre">Variable</span></code> in python to help create and manage Variable.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">480</span><span class="p">])</span>
+<span class="c1"># fc1 and fc2 are both Variable</span>
+<span class="n">fc1</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">image</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">fc2</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">fc1</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="section" id="what-should-class-variable-have">
+<span id="what-should-class-variable-have"></span><h2>what should class <code class="docutils literal"><span class="pre">Variable</span></code> Have<a class="headerlink" href="#what-should-class-variable-have" title="Permalink to this headline">¶</a></h2>
+<ol class="simple">
+<li><code class="docutils literal"><span class="pre">name</span></code>.a name of string type is used to mark the value of the Variable.</li>
+<li><code class="docutils literal"><span class="pre">initializer</span></code>. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.</li>
+<li><code class="docutils literal"><span class="pre">operator</span></code>. Variable should record which operator produce itself. The reaon is:</li>
+</ol>
+<ul class="simple">
+<li>we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.</li>
+</ul>
+<p>In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">VarDesc</span>
+<span class="kn">import</span> <span class="nn">LoDTensorDesc</span>
+<span class="kn">import</span> <span class="nn">framework</span>
+
+<span class="k">def</span> <span class="nf">AddInitialOperator</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span>
+    <span class="c1"># add an initialize Operator to block to init this Variable</span>
+
+<span class="k">class</span> <span class="nc">Variable</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+   <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">dims</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_block</span> <span class="o">=</span> <span class="n">get_default_block</span><span class="p">()</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="bp">None</span>
+
+      <span class="n">tensor_desc</span> <span class="o">=</span> <span class="n">LoDTensorDesc</span><span class="p">(</span><span class="n">data_type</span><span class="o">=</span><span class="nb">type</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="n">dims</span><span class="p">)</span>
+      <span class="n">_var_desc</span> <span class="o">=</span> <span class="n">VarDesc</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">lod_tensor</span><span class="o">=</span><span class="n">tensor_desc</span><span class="p">)</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="n">framework</span><span class="o">.</span><span class="n">CreateVar</span><span class="p">(</span><span class="n">_var_desc</span><span class="p">)</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">add_var</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+      <span class="c1"># add initial op according to initializer</span>
+      <span class="k">if</span> <span class="n">initializer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
+          <span class="n">AddInitialOperator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="p">)</span>
+
+   <span class="k">def</span> <span class="nf">dims</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+      <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">dims</span><span class="p">()</span>
+
+   <span class="k">def</span> <span class="nf">data_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+       <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">data_type</span><span class="p">()</span>
+
+   <span class="k">def</span> <span class="nf">to_proto</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+       <span class="k">pass</span>
+</pre></div>
+</div>
+<p>Then we can use this Variable to create a fc layer in Python.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</span> <span class="kn">as</span> <span class="nn">pd</span>
+
+<span class="k">def</span> <span class="nf">flatten_size</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">):</span>
+  <span class="n">prod</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># of last num_flatten_dims</span>
+  <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">num_flatten_dims</span><span class="p">):</span>
+    <span class="n">prod</span> <span class="o">=</span> <span class="n">prod</span> <span class="o">*</span> <span class="n">X</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+  <span class="k">return</span> <span class="n">prod</span>
+
+<span class="k">def</span> <span class="nf">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">):</span>
+  <span class="n">W</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(),</span> <span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="n">flatten_size</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">),</span> <span class="n">output_size</span><span class="p">])</span>
+  <span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(),</span> <span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="n">output_size</span><span class="p">])</span>
+  <span class="n">out</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">)</span>
+  <span class="n">y</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="n">out</span><span class="p">)</span> <span class="c1"># fc will put fc op input into out</span>
+  <span class="n">pd</span><span class="o">.</span><span class="n">InferShape</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+  <span class="k">return</span> <span class="n">out</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">480</span><span class="p">])</span>
+<span class="n">y</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="n">x</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">z</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="n">y</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">paddle</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">targets</span><span class="o">=</span><span class="p">[</span><span class="n">z</span><span class="p">],</span> <span class="o">...</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
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--- a/develop/doc/objects.inv
+++ b/develop/doc/objects.inv
--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
--- a/develop/doc_cn/_sources/design/var_desc.md.txt
+++ b/develop/doc_cn/_sources/design/var_desc.md.txt
+## Background
+PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
+
+PaddlePaddle use proto message to describe compile time graph for
+
+1. Computation graph should be able to be saved to a file.
+1. In distributed training, the graph will be serialized and send to multiple workers.
+
+The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.
+
+| |compile time|runtime|
+|---|---|---|
+|Data|VarDesc(proto)|Variable(cpp)|
+|Operation|OpDesc(proto)|Operator(cpp)|
+
+
+## Definition of VarDesc
+
+A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.
+
+```proto
+message VarDesc {
+  required string name = 1;
+  optional LoDTensorDesc lod_tensor = 2;
+}
+```
+
+## Definition of LodTensorDesc
+
+```proto
+enum DataType {
+  BOOL = 0;
+  INT16 = 1;
+  INT32 = 2;
+  INT64 = 3;
+  FP16 = 4;
+  FP32 = 5;
+  FP64 = 6;
+}
+
+message LoDTensorDesc {
+  required DataType data_type = 1;
+  repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
+  optional int32 lod_level = 3 [default=0];
+}
+```
+
+## Definition of Variable in Python
+
+In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
+
+```python
+image = Variable(dims=[-1, 640, 480])
+# fc1 and fc2 are both Variable
+fc1 = layer.fc(input=image, output_size=10)
+fc2 = layer.fc(input=fc1, output_size=20)
+```
+### what should class `Variable` Have
+1. `name`.a name of string type is used to mark the value of the Variable.
+1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.
+1. `operator`. Variable should record which operator produce itself. The reaon is:
+  - we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
+
+In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.
+
+```python
+import VarDesc
+import LoDTensorDesc
+import framework
+
+def AddInitialOperator(variable, initializer):
+	# add an initialize Operator to block to init this Variable
+
+class Variable(object):
+   def __init__(self, name, dims, type, initializer):
+      self._block = get_default_block()
+      self._name = name
+      self.op = None
+
+      tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
+      _var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
+      self._var = framework.CreateVar(_var_desc)
+      self._block.add_var(self)
+
+      # add initial op according to initializer
+      if initializer is not None:
+          AddInitialOperator(self, initializer)
+
+   def dims(self):
+      return self._var.dims()
+
+   def data_type(self):
+       return self._var.data_type()
+
+   def to_proto(self):
+       pass
+```
+
+Then we can use this Variable to create a fc layer in Python.
+
+```python
+import paddle as pd
+
+def flatten_size(X, num_flatten_dims):
+  prod = 1 # of last num_flatten_dims
+  for i in xrange(num_flatten_dims):
+    prod = prod * X.dims[-i-1]
+  return prod
+
+def layer.fc(X, output_size, num_flatten_dims):
+  W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
+  b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
+  out = Variable(type=FP32)
+  y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
+  pd.InferShape(y)
+  return out
+
+x = Variable(dims=[-1, 640, 480])
+y = layer.fc(x, output_size=100)
+z = layer.fc(y, output_size=200)
+
+paddle.eval(targets=[z], ...)
+print(z)
+```
--- a/develop/doc_cn/design/var_desc.html
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+  <div class="section" id="background">
+<span id="background"></span><h1>Background<a class="headerlink" href="#background" title="永久链接至标题">¶</a></h1>
+<p>PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.</p>
+<p>PaddlePaddle use proto message to describe compile time graph for</p>
+<ol class="simple">
+<li>Computation graph should be able to be saved to a file.</li>
+<li>In distributed training, the graph will be serialized and send to multiple workers.</li>
+</ol>
+<p>The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.</p>
+<p>| |compile time|runtime|
+|&#8212;|&#8212;|&#8212;|
+|Data|VarDesc(proto)|Variable(cpp)|
+|Operation|OpDesc(proto)|Operator(cpp)|</p>
+</div>
+<div class="section" id="definition-of-vardesc">
+<span id="definition-of-vardesc"></span><h1>Definition of VarDesc<a class="headerlink" href="#definition-of-vardesc" title="永久链接至标题">¶</a></h1>
+<p>A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.</p>
+<div class="highlight-proto"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">VarDesc</span> <span class="p">{</span>
+  <span class="k">required</span> <span class="kt">string</span> <span class="na">name</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
+  <span class="k">optional</span> <span class="n">LoDTensorDesc</span> <span class="na">lod_tensor</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="definition-of-lodtensordesc">
+<span id="definition-of-lodtensordesc"></span><h1>Definition of LodTensorDesc<a class="headerlink" href="#definition-of-lodtensordesc" title="永久链接至标题">¶</a></h1>
+<div class="highlight-proto"><div class="highlight"><pre><span></span><span class="kd">enum</span> <span class="n">DataType</span> <span class="p">{</span>
+  <span class="na">BOOL</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
+  <span class="na">INT16</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
+  <span class="na">INT32</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
+  <span class="na">INT64</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
+  <span class="na">FP16</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
+  <span class="na">FP32</span> <span class="o">=</span> <span class="mi">5</span><span class="p">;</span>
+  <span class="na">FP64</span> <span class="o">=</span> <span class="mi">6</span><span class="p">;</span>
+<span class="p">}</span>
+
+<span class="kd">message</span> <span class="nc">LoDTensorDesc</span> <span class="p">{</span>
+  <span class="k">required</span> <span class="n">DataType</span> <span class="na">data_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
+  <span class="k">repeated</span> <span class="kt">int32</span> <span class="na">dims</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span> <span class="c1">// [UNK, 640, 480] is saved as [-1, 640, 480]</span>
+  <span class="k">optional</span> <span class="kt">int32</span> <span class="na">lod_level</span> <span class="o">=</span> <span class="mi">3</span> <span class="p">[</span><span class="k">default</span><span class="o">=</span><span class="mi">0</span><span class="p">];</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="definition-of-variable-in-python">
+<span id="definition-of-variable-in-python"></span><h1>Definition of Variable in Python<a class="headerlink" href="#definition-of-variable-in-python" title="永久链接至标题">¶</a></h1>
+<p>In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class <code class="docutils literal"><span class="pre">Variable</span></code> in python to help create and manage Variable.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">480</span><span class="p">])</span>
+<span class="c1"># fc1 and fc2 are both Variable</span>
+<span class="n">fc1</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">image</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">fc2</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">fc1</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="section" id="what-should-class-variable-have">
+<span id="what-should-class-variable-have"></span><h2>what should class <code class="docutils literal"><span class="pre">Variable</span></code> Have<a class="headerlink" href="#what-should-class-variable-have" title="永久链接至标题">¶</a></h2>
+<ol class="simple">
+<li><code class="docutils literal"><span class="pre">name</span></code>.a name of string type is used to mark the value of the Variable.</li>
+<li><code class="docutils literal"><span class="pre">initializer</span></code>. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.</li>
+<li><code class="docutils literal"><span class="pre">operator</span></code>. Variable should record which operator produce itself. The reaon is:</li>
+</ol>
+<ul class="simple">
+<li>we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.</li>
+</ul>
+<p>In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">VarDesc</span>
+<span class="kn">import</span> <span class="nn">LoDTensorDesc</span>
+<span class="kn">import</span> <span class="nn">framework</span>
+
+<span class="k">def</span> <span class="nf">AddInitialOperator</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span>
+    <span class="c1"># add an initialize Operator to block to init this Variable</span>
+
+<span class="k">class</span> <span class="nc">Variable</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+   <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">dims</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_block</span> <span class="o">=</span> <span class="n">get_default_block</span><span class="p">()</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="bp">None</span>
+
+      <span class="n">tensor_desc</span> <span class="o">=</span> <span class="n">LoDTensorDesc</span><span class="p">(</span><span class="n">data_type</span><span class="o">=</span><span class="nb">type</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="n">dims</span><span class="p">)</span>
+      <span class="n">_var_desc</span> <span class="o">=</span> <span class="n">VarDesc</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">lod_tensor</span><span class="o">=</span><span class="n">tensor_desc</span><span class="p">)</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="n">framework</span><span class="o">.</span><span class="n">CreateVar</span><span class="p">(</span><span class="n">_var_desc</span><span class="p">)</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">add_var</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+      <span class="c1"># add initial op according to initializer</span>
+      <span class="k">if</span> <span class="n">initializer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
+          <span class="n">AddInitialOperator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="p">)</span>
+
+   <span class="k">def</span> <span class="nf">dims</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+      <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">dims</span><span class="p">()</span>
+
+   <span class="k">def</span> <span class="nf">data_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+       <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">data_type</span><span class="p">()</span>
+
+   <span class="k">def</span> <span class="nf">to_proto</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+       <span class="k">pass</span>
+</pre></div>
+</div>
+<p>Then we can use this Variable to create a fc layer in Python.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</span> <span class="kn">as</span> <span class="nn">pd</span>
+
+<span class="k">def</span> <span class="nf">flatten_size</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">):</span>
+  <span class="n">prod</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># of last num_flatten_dims</span>
+  <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">num_flatten_dims</span><span class="p">):</span>
+    <span class="n">prod</span> <span class="o">=</span> <span class="n">prod</span> <span class="o">*</span> <span class="n">X</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+  <span class="k">return</span> <span class="n">prod</span>
+
+<span class="k">def</span> <span class="nf">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">):</span>
+  <span class="n">W</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(),</span> <span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="n">flatten_size</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">),</span> <span class="n">output_size</span><span class="p">])</span>
+  <span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(),</span> <span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="n">output_size</span><span class="p">])</span>
+  <span class="n">out</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">)</span>
+  <span class="n">y</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="n">out</span><span class="p">)</span> <span class="c1"># fc will put fc op input into out</span>
+  <span class="n">pd</span><span class="o">.</span><span class="n">InferShape</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+  <span class="k">return</span> <span class="n">out</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">480</span><span class="p">])</span>
+<span class="n">y</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="n">x</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">z</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="n">y</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">paddle</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">targets</span><span class="o">=</span><span class="p">[</span><span class="n">z</span><span class="p">],</span> <span class="o">...</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+
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--- a/develop/doc_cn/objects.inv
+++ b/develop/doc_cn/objects.inv
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