diff --git a/develop/doc/_sources/howto/dev/new_op_en.md.txt b/develop/doc/_sources/howto/dev/new_op_en.md.txt
index 510233306c23ba0e8c83b8b057778ea6b875bf6a..fe86936bc12cc2fb88d653429e250f71a478dfb6 100644
--- a/develop/doc/_sources/howto/dev/new_op_en.md.txt
+++ b/develop/doc/_sources/howto/dev/new_op_en.md.txt
@@ -1,8 +1,8 @@
 # How to write a new operator
 
  - [Background](#background)
- - [Implementing C++ Types](#implementing-c++-types)
-   - [Defining ProtoMaker](#defining-protoMaker)
+ - [Implementing C++ Types](#implementing-c-types)
+   - [Defining ProtoMaker](#defining-protomaker)
    - [Defining Operator](#defining-operator)
    - [Registering Operator](#registering-operator)
    - [Compilation](#compilation)
@@ -41,7 +41,7 @@ Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePadd
 ## Implementing C++ Types
 
 
-### 1. Defining Class ProtoMaker
+### Defining ProtoMaker
 
 Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and pne output.
 
@@ -98,7 +98,7 @@ There are two changes in this example:
 - `AddAttr<AttrType>("scale", "...").SetDefault(1.0);`  adds `scale`constant as an attribute, and sets the default value to 1.0.
 
 
-### 2. Defining Operator
+### Defining Operator
 
 The following code defines the interface for MulOp:
 
@@ -147,7 +147,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
 
 Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, which also include the registration methods introduced later.
 
-### 3. Defining OpKernel
+### Defining OpKernel
 
 `MulKernel` inherits `framework::OpKernel`, which includes the following templates:
 
@@ -188,7 +188,7 @@ This concludes the forward implementation of an operator. Next its operation and
 
 The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**.
 
-### 4. Registering Operator
+### Registering Operator
 
 - In `.cc` files, register forward and backward operator classes and the CPU kernel.
 
@@ -220,7 +220,7 @@ The definition of its corresponding backward operator, if applicable, is similar
                            ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
     ```
 
-### 5. Compilation
+### Compilation
 
 Run the following commands to compile.
 
@@ -284,8 +284,7 @@ A forward operator unit test inherits `unittest.TestCase` and defines metaclass
       def test_check_grad_ingore_y(self):
           self.check_grad(
               ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
-
-    ```
+  ```
 Get its output, and compare it with the forward operator's own output.
 
 The code above first loads required packages. In addition, we have
@@ -294,6 +293,8 @@ The code above first loads required packages. In addition, we have
 - `self.inputs` defines input, with type `numpy.array` and initializes it.
 - `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script.
 
+### Testing Backward Operators
+
 Some key points in checking gradient above include:
 
 - `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
diff --git a/develop/doc/howto/dev/new_op_en.html b/develop/doc/howto/dev/new_op_en.html
index a5e88e5efbff993a8a93a3f99000f1fab6252e25..f779138d340a873d2a5acbbeb5fcc316378b3e5b 100644
--- a/develop/doc/howto/dev/new_op_en.html
+++ b/develop/doc/howto/dev/new_op_en.html
@@ -207,8 +207,8 @@
 <span id="how-to-write-a-new-operator"></span><h1>How to write a new operator<a class="headerlink" href="#how-to-write-a-new-operator" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
 <li><a class="reference external" href="#background">Background</a></li>
-<li><a class="reference external" href="#implementing-c++-types">Implementing C++ Types</a><ul>
-<li><a class="reference external" href="#defining-protoMaker">Defining ProtoMaker</a></li>
+<li><a class="reference external" href="#implementing-c-types">Implementing C++ Types</a><ul>
+<li><a class="reference external" href="#defining-protomaker">Defining ProtoMaker</a></li>
 <li><a class="reference external" href="#defining-operator">Defining Operator</a></li>
 <li><a class="reference external" href="#registering-operator">Registering Operator</a></li>
 <li><a class="reference external" href="#compilation">Compilation</a></li>
@@ -244,8 +244,8 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 </div>
 <div class="section" id="implementing-c-types">
 <span id="implementing-c-types"></span><h2>Implementing C++ Types<a class="headerlink" href="#implementing-c-types" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="defining-class-protomaker">
-<span id="defining-class-protomaker"></span><h3>1. Defining Class ProtoMaker<a class="headerlink" href="#defining-class-protomaker" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="defining-protomaker">
+<span id="defining-protomaker"></span><h3>Defining ProtoMaker<a class="headerlink" href="#defining-protomaker" title="Permalink to this headline">¶</a></h3>
 <p>Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and pne output.</p>
 <p>First, define <code class="docutils literal"><span class="pre">ProtoMaker</span></code> to describe the Operator&#8217;s input, output, and additional comments:</p>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOpMaker</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
@@ -293,7 +293,7 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 </ul>
 </div>
 <div class="section" id="defining-operator">
-<span id="defining-operator"></span><h3>2. Defining Operator<a class="headerlink" href="#defining-operator" title="Permalink to this headline">¶</a></h3>
+<span id="defining-operator"></span><h3>Defining Operator<a class="headerlink" href="#defining-operator" title="Permalink to this headline">¶</a></h3>
 <p>The following code defines the interface for MulOp:</p>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOp</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span> <span class="p">{</span>
  <span class="k">public</span><span class="o">:</span>
@@ -336,7 +336,7 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 <p>Usually <code class="docutils literal"><span class="pre">OpProtoMaker</span></code> and <code class="docutils literal"><span class="pre">Op</span></code>&#8216;s type definitions are written in <code class="docutils literal"><span class="pre">.cc</span></code> files, which also include the registration methods introduced later.</p>
 </div>
 <div class="section" id="defining-opkernel">
-<span id="defining-opkernel"></span><h3>3. Defining OpKernel<a class="headerlink" href="#defining-opkernel" title="Permalink to this headline">¶</a></h3>
+<span id="defining-opkernel"></span><h3>Defining OpKernel<a class="headerlink" href="#defining-opkernel" title="Permalink to this headline">¶</a></h3>
 <p><code class="docutils literal"><span class="pre">MulKernel</span></code> inherits <code class="docutils literal"><span class="pre">framework::OpKernel</span></code>, which includes the following templates:</p>
 <ul class="simple">
 <li><code class="docutils literal"><span class="pre">typename</span> <span class="pre">DeviceContext</span></code> denotes device context type. When different devices, namely the CPUDeviceContext and the CUDADeviceContext, share the same kernel, this template needs to be added. If they don&#8217;t share kernels, this must not be added. An example of a non-sharing kernel is <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43"><code class="docutils literal"><span class="pre">OnehotCrossEntropyOpKernel</span></code></a>.</li>
@@ -370,7 +370,7 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 <p>The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. <strong>Note that a backward operator does not include a <code class="docutils literal"><span class="pre">ProtoMaker</span></code></strong>.</p>
 </div>
 <div class="section" id="registering-operator">
-<span id="registering-operator"></span><h3>4. Registering Operator<a class="headerlink" href="#registering-operator" title="Permalink to this headline">¶</a></h3>
+<span id="registering-operator"></span><h3>Registering Operator<a class="headerlink" href="#registering-operator" title="Permalink to this headline">¶</a></h3>
 <ul>
 <li><p class="first">In <code class="docutils literal"><span class="pre">.cc</span></code> files, register forward and backward operator classes and the CPU kernel.</p>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
@@ -406,7 +406,7 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 </ul>
 </div>
 <div class="section" id="compilation">
-<span id="compilation"></span><h3>5. Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h3>
+<span id="compilation"></span><h3>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h3>
 <p>Run the following commands to compile.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">mul_op</span>
 </pre></div>
@@ -462,7 +462,6 @@ Registering the Op           | Ops are registered in <code class="docutils liter
     <span class="k">def</span> <span class="nf">test_check_grad_ingore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
             <span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s1">&#39;Y&#39;</span><span class="p">))</span>
-
 </pre></div>
 </div>
 <p>Get its output, and compare it with the forward operator&#8217;s own output.</p>
@@ -472,6 +471,9 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 <li><code class="docutils literal"><span class="pre">self.inputs</span></code> defines input, with type <code class="docutils literal"><span class="pre">numpy.array</span></code> and initializes it.</li>
 <li><code class="docutils literal"><span class="pre">self.outputs</span></code> defines output and completes the same operator computation in the Python script, and returns its result from the Python script.</li>
 </ul>
+</div>
+<div class="section" id="testing-backward-operators">
+<span id="testing-backward-operators"></span><h3>Testing Backward Operators<a class="headerlink" href="#testing-backward-operators" title="Permalink to this headline">¶</a></h3>
 <p>Some key points in checking gradient above include:</p>
 <ul class="simple">
 <li><code class="docutils literal"><span class="pre">test_normal</span></code> calls <code class="docutils literal"><span class="pre">check_grad</span></code> to validate scaling tests&#8217; correctness and stability through numeric methods.<ul>
diff --git a/develop/doc/operators.json b/develop/doc/operators.json
index 8f4e5b2640acf40a99cebca125a3d6d87807802e..a4459cb94d149d6a6a30d3f1911a6a91bfc77cb9 100644
--- a/develop/doc/operators.json
+++ b/develop/doc/operators.json
@@ -728,44 +728,43 @@
    "generated" : 0
  } ] 
 },{
- "type" : "auc",
- "comment" : "\nArea Under The Curve (AUC) Operator.\n\nThis implementation computes the AUC according to forward output and label.\nIt is used very widely in binary classification evaluation. As a note:\nIf input label contains values other than 0 and 1, it will be cast\nto bool. You can find the relevant definitions here:\nhttps://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve\n\nThere are two types of possible curves:\n1. ROC: Receiver operating characteristic\n2. PR: Precision Recall\n",
+ "type" : "accuracy",
+ "comment" : "\nAccuracy Operator. \n\nIt will print accuracy rate for classification.\nThe accuracy is calculated as follows:\n\n$$accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples}$$\n\nBoth the input Out and Label can carry the LoD (Level of Details)\ninformation, or not. But the output only shares the LoD information \nwith the input Out(Inference).\n\n",
  "inputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "A floating point 2D tensor, values are in the range [0, 1].Each row is sorted in descending order. This input should be theoutput of topk.Typically, this tensor indicates the probability of each label",
+   "comment" : "The network output of topk (inferences)",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Indices",
-   "comment" : "An int 2D tensor, indicating the indices of originaltensor before sorting. Typically, this tensor indicates which label the probability stands for.",
+   "comment" : "The the network output of topk (indices)",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Label",
-   "comment" : "A 2D int tensor indicating the label of the training data.The height is batch size and width is always 1.",
+   "comment" : "Label of the training data",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "AUC",
-   "comment" : "A scalar representing the current area-under-the-curve.",
+   "name" : "Accuracy",
+   "comment" : "The accuracy of current batch",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [ 
- { 
-   "name" : "curve",
-   "type" : "string",
-   "comment" : "Curve type, can be 'ROC' or 'PR'.",
-   "generated" : 0
  }, { 
-   "name" : "num_thresholds",
-   "type" : "int",
-   "comment" : "The number of thresholds to use when discretizing the roc curve.",
-   "generated" : 0
- } ] 
+   "name" : "Correct",
+   "comment" : "The correct samples count of current batch",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Total",
+   "comment" : "The samples count of current batch",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
 },{
  "type" : "hard_sigmoid",
  "comment" : "\nHardSigmoid Activation Operator.\n\nSegment-wise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391), \nwhich is much faster than sigmoid.\n\n$y = \\max(0, \\min(1, slope * x + shift))$\n\nThe slope should be positive. The offset can be either positive or negative.\nThe default slope and shift are set according to the above reference.\nIt is recommended to use the defaults for this activation.\n\n",
@@ -993,624 +992,585 @@
    "generated" : 0
  } ] 
 },{
- "type" : "top_k",
- "comment" : "\nTop K operator\n\nIf the input is a vector (1d tensor), this operator finds the k largest \nentries in the vector and outputs their values and indices as vectors. \nThus values[j] is the j-th largest entry in input, and its index is indices[j].\n\nFor matrices, this operator computes the top k entries in each row. ",
+ "type" : "softsign",
+ "comment" : "\nSoftsign Activation Operator.\n\n$$y = \\frac{x}{1 + |x|}$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input of Topk op",
+   "comment" : "Input of Softsign operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor) The output tensor of Topk op",
+   "name" : "Y",
+   "comment" : "Output of Softsign operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "logical_xor",
+ "comment" : "logical_xor Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(LoDTensor) Left hand operand of logical_xor operator",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Indices",
-   "comment" : "(Tensor) The indices of Topk elements of input",
+   "name" : "Y",
+   "comment" : "(LoDTensor) Right hand operand of logical_xor operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
+ "outputs" : [ 
  { 
-   "name" : "k",
-   "type" : "int",
-   "comment" : "(int, default 1) Number of top elements to look for along the last dimension (along each row for matrices).",
-   "generated" : 0
- } ] 
+   "name" : "Out",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
 },{
- "type" : "sequence_softmax",
- "comment" : "\nSequence Softmax Operator.\n\nSequenceSoftmaxOp computes the softmax activation among all time-steps for each\nsequence. The dimension of each time-step should be 1. Thus, the shape of\ninput Tensor can be either [N, 1] or [N], where N is the sum of the length\nof all sequences.\n\nThe algorithm works as follows:\n    for i-th sequence in a mini-batch:\n        $$Out(X[lod[i]:lod[i+1]], :) =\n            \\frac{\\exp(X[lod[i]:lod[i+1], :])}\n            {\\sum(\\exp(X[lod[i]:lod[i+1], :]))}$$\n\nFor example, for a mini-batch of 3 sequences with variable-length,\neach containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],\nthen softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :]\nand N turns out to be 7.\n\n",
+ "type" : "softmax",
+ "comment" : "\nSoftmax Operator.\n\nThe input of the softmax operator is a 2-D tensor with shape N x K (N is the\nbatch_size, K is the dimension of input feature). The output tensor has the\nsame shape as the input tensor.\n\nFor each row of the input tensor, the softmax operator squashes the\nK-dimensional vector of arbitrary real values to a K-dimensional vector of real\nvalues in the range [0, 1] that add up to 1.\nIt computes the exponential of the given dimension and the sum of exponential\nvalues of all the other dimensions in the K-dimensional vector input.\nThen the ratio of the exponential of the given dimension and the sum of\nexponential values of all the other dimensions is the output of the softmax\noperator.\n\nFor each row $i$ and each column $j$ in Input(X), we have:\n    $$Y[i, j] = \\frac{\\exp(X[i, j])}{\\sum_j(exp(X[i, j])}$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1.",
+   "comment" : "The input tensor of softmax. 2-D with shape [batch_size, input_feature_dimensions].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.",
+   "name" : "Y",
+   "comment" : "The normalized values with the same shape as X.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "decayed_adagrad",
- "comment" : "\nDecayed Adagrad Optimizer.\n\nThe update is done as follows:\n\n$$\nmoment\\_out = decay * moment + (1 - decay) * grad * grad \\\\\nparam\\_out = param - \\frac{learning\\_rate * grad}{\\sqrt{moment\\_out} + epsilon}\n$$\n\nThe original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\ndoes not have an epsilon attribute. It is added here for numerical\nstability to avoid the division by zero error.\n\n",
+ "type" : "lod_array_length",
+ "comment" : "\nLoDArrayLength Operator.\n\nThis operator obtains the length of lod tensor array:\n\n$$Out = len(X)$$\n\nNOTE: The output is a CPU Tensor since the control variable should be only in\nCPU and the length of LoDTensorArray should be used as control variables.\n\n",
  "inputs" : [ 
  { 
-   "name" : "Param",
-   "comment" : "(Tensor) Input parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Grad",
-   "comment" : "(Tensor) Input gradient",
+   "name" : "X",
+   "comment" : "(LoDTensorArray) The input tensor array.",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "Moment",
-   "comment" : "(Tensor) Second moment",
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(Tensor) 1x1 CPU Tensor of length, int64_t",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "LearningRate",
-   "comment" : "(Tensor) Learning rate",
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "top_k",
+ "comment" : "\nTop K operator\n\nIf the input is a vector (1d tensor), this operator finds the k largest \nentries in the vector and outputs their values and indices as vectors. \nThus values[j] is the j-th largest entry in input, and its index is indices[j].\n\nFor matrices, this operator computes the top k entries in each row. ",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(Tensor) The input of Topk op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "ParamOut",
-   "comment" : "(Tensor) Output parameter",
+   "name" : "Out",
+   "comment" : "(Tensor) The output tensor of Topk op",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "MomentOut",
-   "comment" : "(Tensor) Output second moment",
+   "name" : "Indices",
+   "comment" : "(Tensor) The indices of Topk elements of input",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "decay",
-   "type" : "float",
-   "comment" : "(float, default 0.95) Discounting factor for coming gradient",
-   "generated" : 0
- }, { 
-   "name" : "epsilon",
-   "type" : "float",
-   "comment" : "(float, default 1.0e-6) Constant for numerical stability",
+   "name" : "k",
+   "type" : "int",
+   "comment" : "(int, default 1) Number of top elements to look for along the last dimension (along each row for matrices).",
    "generated" : 0
  } ] 
 },{
- "type" : "scale",
- "comment" : "\nScale operator\n\n$$Out = scale*X$$\n",
+ "type" : "clip",
+ "comment" : "\nClip Operator.\n\nThe clip operator limits the value of given input within an interval. The interval is\nspecified with arguments 'min' and 'max':\n\n$$\nOut = \\min(\\max(X, min), max)\n$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) Input tensor of scale operator.",
+   "comment" : "(Tensor)The input of clip op.The number of dimensions must be between [1, 9].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) Output tensor of scale operator.",
+   "comment" : "(Tensor)The output of clip op with shape as input(X)",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "scale",
+   "name" : "min",
    "type" : "float",
-   "comment" : "(float, default 0)The scaling factor of the scale operator.",
+   "comment" : "(float)Minimum value, under which element is replaced by min.",
+   "generated" : 0
+ }, { 
+   "name" : "max",
+   "type" : "float",
+   "comment" : "(float)Maximum value, above which element is replaced by max",
    "generated" : 0
  } ] 
 },{
- "type" : "increment",
- "comment" : "\nIncrement Operator.\n\nThe equation is: \n$$Out = X + step$$\n\n",
+ "type" : "margin_rank_loss",
+ "comment" : "\nMarginRankLoss Operator.\n\nThis operator measures the loss given a pair of training sample\n{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1` \nindicating X1 is ranked higher than `X2` and `Label = -1` otherwise. The loss \nis calculated as:\n\n$loss(X1, X2, Label) = \\max(0, -Label * (X1 - X2) + margin)$\n\nThe attribute `margin` here helps make the predictions more robust.\nDenote the item ranked higher as the positive sample, otherwise the negative \nsample. If the score of the two samples satisfies \n\n$positive sample - negative sample < margin$\n\nthe pair of samples will contribute to the final loss, which will backpropagate \nand train the ranking model to enlarge the difference between the two scores.\n\nFor batch input with size `batch_size`, `X1`, `X2` and `Label`\nall have the same shape [batch_size x 1].\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(Tensor) The input tensor of increment operator",
+   "name" : "X1",
+   "comment" : "(2-D tensor with shape [batch_size x 1]) The score for one item X1 to be ranked, from pairwise ranking model.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "X2",
+   "comment" : "(2-D tensor with shape [batch_size x 1]) The score for another item X2 to be ranked, from pairwise ranking model.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Label",
+   "comment" : "(2-D tensor with shape [batch_size x 1]) The label indicating X1 ranked higher than X2 or not, can only be +1 or -1.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
+   "name" : "Activated",
+   "comment" : "(2-D tensor with shape [batch_size x 1]) Intermediate tensor to indicate whether each element of Output(Out) is activated.",
+   "duplicable" : 0,
+   "intermediate" : 1
+ }, { 
    "name" : "Out",
-   "comment" : "(Tensor) The output tensor of increment operator.",
+   "comment" : "(2-D tensor with shape [batch_size x 1]) The output loss of MarginRankLoss operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "step",
+   "name" : "margin",
    "type" : "float",
-   "comment" : "(float, default 1.0) The step size by which the input tensor will be incremented.",
+   "comment" : "(scalar, default 0) Margin for MarginRankLossOp.",
    "generated" : 0
  } ] 
 },{
- "type" : "expand",
- "comment" : "\nExpand operator tiles the input by given times number. You should set times\nnumber for each dimension by providing attribute 'expand_times'. The rank of X\nshould be in [1, 6]. Please notice that size of 'expand_times' must be same with\nX's rank. Following is a using case:\n\nInput(X) is a 3-D tensor with shape [2, 3, 1]:\n\n        [\n           [[1], [2], [3]],\n           [[4], [5], [6]]\n        ]\n\nAttr(expand_times):  [1, 2, 2]\n\nOutput(Out) is a 3-D tensor with shape [2, 6, 2]:\n\n        [\n            [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],\n            [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]\n        ]\n\n",
+ "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",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank in [1, 6].X is the input tensor to be expanded.",
+   "comment" : "The first input of mul op",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Y",
+   "comment" : "The second input of mul op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank in [1, 6].The rank of Output(Out) is same as Input(X) except that each dimension size of Output(Out) is equal to corresponding dimension size of Input(X) multiplying corresponding value of Attr(expand_times).",
+   "comment" : "The output of mul op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "expand_times",
-   "type" : "int array",
-   "comment" : "Expand times number for each dimension.",
+   "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        ",
+   "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        ",
    "generated" : 0
  } ] 
 },{
- "type" : "lod_array_length",
- "comment" : "\nLoDArrayLength Operator.\n\nThis operator obtains the length of lod tensor array:\n\n$$Out = len(X)$$\n\nNOTE: The output is a CPU Tensor since the control variable should be only in\nCPU and the length of LoDTensorArray should be used as control variables.\n\n",
+ "type" : "minus",
+ "comment" : "\nMinus Operator.\n\nEquation:\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" : "(LoDTensorArray) The input tensor array.",
+   "comment" : "The left tensor of minus operator.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Y",
+   "comment" : "The right tensor of minus operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) 1x1 CPU Tensor of length, int64_t",
+   "comment" : "The output tensor of minus operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "reduce_sum",
- "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the sum of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
+ "type" : "max_sequence_len",
+ "comment" : "Calculate the max sequence length through lod_rank_table.",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
+   "name" : "RankTable",
+   "comment" : "The lod_rank_table.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) The result tensor.",
+   "comment" : "The max sequence length.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "dim",
-   "type" : "int",
-   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
-   "generated" : 0
- }, { 
-   "name" : "keep_dim",
-   "type" : "bool",
-   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "tanh_shrink",
- "comment" : "\nTanhShrink Activation Operator.\n\n$$y = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\n\n",
+ "type" : "multiplex",
+ "comment" : "\nMultiplex Operator.\n\nMultiplex multiple tensors according to the index provided by the index tensor.\n\nIds: the index tensor.\nX[0 : N - 1]: the candidate tensors for output (N >= 2).\nFor each index i from 0 to batchSize - 1, the output is the i-th row of the\nthe (Ids[i])-th tensor.\n\nFor i-th row of the output tensor:\n\n$$y[i] = x_{k}[i]$$\n\nwhere `y` is the output tensor, `x_{k}` is the k-th input tensor,\nand `k = Ids[i]`.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "Input of TanhShrink operator",
+   "name" : "Ids",
+   "comment" : "The index tensor of multiplex operator.",
    "duplicable" : 0,
    "intermediate" : 0
+ }, { 
+   "name" : "X",
+   "comment" : "The candidate tensors of multiplex operator.",
+   "duplicable" : 1,
+   "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of TanhShrink operator",
+   "name" : "Out",
+   "comment" : "The output tensor of multiplex operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "adam",
- "comment" : "\nAdam Optimizer.\n\nThis implements the Adam optimizer from Section 2 of the Adam\npaper : https://arxiv.org/abs/1412.6980.\nAdam is a first-order gradient-based optimization method based on\nadaptive estimates of lower-order moments.\n\nAdam updates:\n\n$$\nmoment\\_1\\_out = \\beta_1 * moment\\_1 + (1 - \\beta_1) * grad \\\\\nmoment\\_2_\\out = \\beta_2 * moment\\_2 + (1 - \\beta_2) * grad * grad \\\\\nlearning\\_rate = learning\\_rate *\n                  \\frac{\\sqrt{1 - \\beta_{2\\_pow}}}{1 - \\beta_{1\\_pow}} \\\\\nparam\\_out = param - learning\\_rate * \\frac{moment\\_1}{\\sqrt{moment\\_2} + \\epsilon}\n$$\n\n",
+ "type" : "pool3d_cudnn",
+ "comment" : "\nPool3d Operator.\n\nThe pooling3d operation calculates the output based on\nthe input, pooling_type, ksize, strides, and paddings parameters.\nInput(X) and output(Out) are in NCDHW format, where N is batch\nsize, C is the number of channels, and D, H and W are the depth, height and\nwidth of the feature, respectively. Parameters(ksize, strides, paddings) \nare three elements. These three elements represent depth, height and \nwidth, respectively. The input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       X shape: $(N, C, D_{in}, H_{in}, W_{in})$\n  Output:\n       Out shape: $(N, C, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out} = \\frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\\\\n       H_{out} = \\frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\\\\n       W_{out} = \\frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1\n  $$\n\n",
  "inputs" : [ 
  { 
-   "name" : "Param",
-   "comment" : "(Tensor) Input parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Grad",
-   "comment" : "(Tensor) Input gradient",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "LearningRate",
-   "comment" : "(Tensor) Learning rate",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Moment1",
-   "comment" : "(Tensor) Input first moment",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Moment2",
-   "comment" : "(Tensor) Input second moment",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Beta1Pow",
-   "comment" : "(Tensor) Input beta1 power accumulator",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Beta2Pow",
-   "comment" : "(Tensor) Input beta2 power accumulator",
+   "name" : "X",
+   "comment" : "(Tensor) The input tensor of pooling operator. The format of input tensor is NCDHW, where N is batch size, C is the number of channels, and D, H and W is the depth, height and width of the feature, respectively.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "ParamOut",
-   "comment" : "(Tensor) Output parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Moment1Out",
-   "comment" : "(Tensor) Output first moment",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Moment2Out",
-   "comment" : "(Tensor) Output second moment",
+   "name" : "Out",
+   "comment" : "(Tensor) The output tensor of pooling operator.The format of output tensor is also NCDHW, where N is batch size, C is the number of channels, and D, H and W is the depth, height and width of the feature, respectively.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "beta1",
-   "type" : "float",
-   "comment" : "(float, default 0.9) Exponential decay rate for the first moment estimates.",
+   "name" : "pooling_type",
+   "type" : "string",
+   "comment" : "(string) Pooling type, can be \"max\" for max-pooling and \"avg\" for average-pooling.",
    "generated" : 0
  }, { 
-   "name" : "beta2",
-   "type" : "float",
-   "comment" : "(float, default 0.999) exponential decay rate for the second moment estimates.",
+   "name" : "ksize",
+   "type" : "int array",
+   "comment" : "(vector<int>) The pooling window size(depth, height, width) of pooling operator. If global_pooling = true, ksize and paddings will be ignored.",
    "generated" : 0
  }, { 
-   "name" : "epsilon",
-   "type" : "float",
-   "comment" : "(float, default 1.0e-8) Constant for numerical stability",
+   "name" : "global_pooling",
+   "type" : "bool",
+   "comment" : "(bool, default false) Whether to use the global pooling. If global_pooling = true, ksize and paddings wille be ignored.",
+   "generated" : 0
+ }, { 
+   "name" : "strides",
+   "type" : "int array",
+   "comment" : "(vector<int>, default {1,1,1}) Strides(depth, height, width) of the pooling operator.",
+   "generated" : 0
+ }, { 
+   "name" : "paddings",
+   "type" : "int array",
+   "comment" : "(vector<int>, default {0,0,0}), paddings(depth, height, width) of pooling operator. If global_pooling = true, ksize and paddings will be ignored.",
    "generated" : 0
  } ] 
 },{
- "type" : "reduce_min",
- "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the min of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
+ "type" : "pow",
+ "comment" : "\nPow Activation Operator.\n\n$y = x^{factor}$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
+   "comment" : "Input of Pow operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor) The result tensor.",
+   "name" : "Y",
+   "comment" : "Output of Pow operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "dim",
-   "type" : "int",
-   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
-   "generated" : 0
- }, { 
-   "name" : "keep_dim",
-   "type" : "bool",
-   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
+   "name" : "factor",
+   "type" : "float",
+   "comment" : "The exponential factor of Pow",
    "generated" : 0
  } ] 
 },{
- "type" : "lod_reset",
- "comment" : "LoDReset operator\n\nReset LoD of Input(X) into a new one specified by Input(TargetLoD) or\nAttr(target_lod), or set LoD for Input(X) if it doesn't have one.\nCurrently the lod_reset operator only supports the reset of level 0 LoD.\nAt least one of Input(TargetLoD) and Attr(target_lod) must be set,\nand if both of them are set, Input(TargetLoD) will be chosen as the\ntarget LoD.\n\nAn example:\nGiven a float LoDTensor X with shape (6, 1), its transpose form represents\n\n    [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],\n\nwith LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like\n\n    [1.0, 2.0], [3.0, 4.0, 5.0], [6.0].\n\nIf target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and\nthe sequences that the LoDTensor Output(Out) contains becomes:\n\n    [1.0, 2.0, 3.0, 4.0], [5.0, 6.0].\n\n",
+ "type" : "l1_norm",
+ "comment" : "\nL1 Norm Operator.\n\nComputes the L1 norm of a tensor.\n\n$$Out = \\sum{|X|}$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) The input tensor of lod_reset operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "TargetLoD",
-   "comment" : "(Tensor, optional) The target level 0 LoD from Input().",
+   "comment" : "(Tensor) The input of l1_norm op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) The output tensor of lod_reset operator.",
+   "comment" : "(Scalar) The output of l1_norm op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "target_lod",
-   "type" : "int array",
-   "comment" : "The target level 0 LoD from Attr().",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "write_to_array",
- "comment" : "\nWriteToArray Operator.\n\nThis operator writes a LoDTensor to a LoDTensor array.\n\nAssume $T$ is LoDTensor, $i$ is the subscript of the array, and $A$ is the array. The\nequation is\n\n$$A[i] = T$$\n\n",
+ "type" : "sqrt",
+ "comment" : "\nSqrt Activation Operator.\n\n$y = \\sqrt{x}$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) the tensor will be written to tensor array",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "I",
-   "comment" : "(Tensor) the subscript index in tensor array. The number of element should be 1",
+   "comment" : "Input of Sqrt operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(TensorArray) the tensor array will be written",
+   "name" : "Y",
+   "comment" : "Output of Sqrt operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "reshape",
- "comment" : "\nReshape Operator.\n\nReshape Input(X) into the shape specified by Attr(shape).\n\nAn example:\nGiven a 2-D tensor X with 2 rows and 2 columns\n\n    [[1, 2], [3, 4]]\n\nand target shape = [1, 4], the reshape operator will transform\nthe tensor X into a 1-D tensor:\n\n    [1, 2, 3, 4]\n\n",
+ "type" : "lookup_table",
+ "comment" : "\nLookup Table Operator.\n\nThis operator is used to perform lookups on the parameter W,\nthen concatenated into a dense tensor.\n\nThe input Ids can carry the LoD (Level of Details) information,\nor not. And the output only shares the LoD information with input Ids.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input tensor of reshape operator.",
+   "name" : "W",
+   "comment" : "An input represents embedding tensors, which is a learnable parameter.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Ids",
+   "comment" : "An input with type int32 or int64 contains the ids to be looked up in W. Ids must be a column vector with rank = 2. The 2nd dimension size must be 1.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output tensor of reshape operator.",
+   "comment" : "The lookup results, which have the same type as W.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "shape",
-   "type" : "int array",
-   "comment" : "(vector<int>) Target shape of reshape operator.",
+   "name" : "is_sparse",
+   "type" : "bool",
+   "comment" : "(boolean, default false) Sparse update",
    "generated" : 0
  } ] 
 },{
- "type" : "fill_constant",
- "comment" : "\nFillConstantBatchSizeLike Operator.\n\nFill up a variable with specified constant value.\n\n",
- "inputs" : [  ], 
- "outputs" : [ 
+ "type" : "positive_negative_pair",
+ "comment" : "\n        PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) \n        model performance. \n        Within some context, e.g. the \"query\", a LTR model generates scores\n        for a list of items, which gives a partial order of the items.\n        PositiveNegativePairOp takes a list of reference rank order \n        (Input(\"Label\")) and the model generated scores (Input(Score)) as \n        inputs and counts the pairs that ranked correctly and incorrectly.\n",
+ "inputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor) Tensor of specified shape will be filled with the specified value",
+   "name" : "Score",
+   "comment" : "(Tensor, float) Model Score on an item (with respect to QueryID). It's a 2-D tensor with shape [batch_size, depth], where the column specified by the attribute \"column\" is used as item score.",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [ 
- { 
-   "name" : "dtype",
-   "type" : "int",
-   "comment" : "(int, default 5 (FP32)) Output data type",
-   "generated" : 0
  }, { 
-   "name" : "shape",
-   "type" : "int array",
-   "comment" : "(vector<int>) The shape of the output",
-   "generated" : 0
+   "name" : "Label",
+   "comment" : "(Tensor, float) Label of an item (with repsect to QueryId). It's a 2-D tensor with shape [batch_size, 1].",
+   "duplicable" : 0,
+   "intermediate" : 0
  }, { 
-   "name" : "value",
-   "type" : "float",
-   "comment" : "(float, default 0) The value to be filled",
-   "generated" : 0
+   "name" : "QueryID",
+   "comment" : "(Tensor, int64) Query ID that indicates the context. Its shape should be the same as Label.",
+   "duplicable" : 0,
+   "intermediate" : 0
  }, { 
-   "name" : "force_cpu",
-   "type" : "bool",
-   "comment" : "(bool, default false) Force fill output variable to cpu memory. Otherwise, fill output variable to the running device",
-   "generated" : 0
- } ] 
-},{
- "type" : "elementwise_div",
- "comment" : "\nLimited Elementwise Div Operator.\n\nThe equation is:\n\n$Out = X / Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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" : "(Tensor) The first input tensor of elementwise op",
+   "name" : "AccumulatePositivePair",
+   "comment" : "(float) Optional. The accumulated number of positive pairs over a stream of data. If provided, the output PositivePair will be initialized with this number rather than 0. it won't be modified in place.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "(Tensor) The second input tensor of elementwise op",
+   "name" : "AccumulateNegativePair",
+   "comment" : "(float) Optional. The accumulated number of negative pairs over a stream of data. If provided, the output NegativePair will be initialized with this number rather than 0. it won't be modified in place.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "AccumulateNeutralPair",
+   "comment" : "(float) Optional. The accumulated number of neutral pairs over a stream of data. If provided, the output NeutralPair will be initialized with this number rather than 0. it won't be modified in place.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Weight",
+   "comment" : "(float) Optional. Weight of current item. If specified, its shape should be the same as Label, and the meaning of the output changes from numbers of pairs to the total sum of pairs' weights. Weight of a pair of items is the average of their weights.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of elementwise op",
+   "name" : "PositivePair",
+   "comment" : "(float) Number of positive pairs, i.e. the pairs of items that are ranked correctly.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "NegativePair",
+   "comment" : "(float) Number of negative pairs, i.e. the pairs of items that are ranked incorrectly.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "NeutralPair",
+   "comment" : "(float) Number of neutral pairs, i.e. the pairs of items that have the same score.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "axis",
+   "name" : "column",
    "type" : "int",
-   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
+   "comment" : "(int, default -1) The column position of Score used to rank items in descending order. It must be in the range of [-rank(Score), rank(Score)). If `dim < 0`, the dim to reduce is `rank + dim`. Noting that reducing on the first dim will make the LoD info lost.",
    "generated" : 0
  } ] 
 },{
- "type" : "conv2d_cudnn",
- "comment" : "\nConvolution Operator.\n\nThe convolution operation calculates the output based on the input, filter\nand strides, paddings, dilations, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and Output(Output) are in NCHW format. Where N is batch\nsize, C is the number of channels, H is the height of the feature, and W is\nthe width of the feature.\nFilters(Input) is MCHW format. Where M is the number of output image channels, C is\nthe number of input image channels, H is the height of the filter, and W\nis the width of the filter.\nParameters(strides, paddings, dilations) are two elements. These two elements represent\nheight and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       Input shape: $(N, C_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{out}, C_{in}, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, H_{out}, W_{out})$\n  Where\n$$\n       H_{out}= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\\\\n       W_{out}= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1\n$$\n",
+ "type" : "proximal_gd",
+ "comment" : "\nProximalGD Operator.\n\nOptimizer that implements the proximal gradient descent algorithm:\n\n$$\nprox\\_param = param - learning\\_rate * grad \\\\\nparam = sign(prox\\_param) / (1 + learning\\_rate * l2) *\n        \\max(|prox\\_param| - learning\\_rate * l1, 0)\n$$        \n\nThe paper that proposed Proximal Gradient Descent:\n(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)\n\n",
  "inputs" : [ 
  { 
-   "name" : "Input",
-   "comment" : "(Tensor) The input tensor of convolution operator. The format of input tensor is NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.",
+   "name" : "Param",
+   "comment" : "(Tensor, default Tensor<float>) Input parameter value that has to be updated.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Filter",
-   "comment" : "(Tensor) The filter tensor of convolution operator. The format of the filter tensor is MCHW, where M is the number of output image channels, C is the number of input image channels, H is the height of the filter, and W is the width of the filter. If the groups attribute is greater than 1, C equals the number of input image channels divided by the groups.",
+   "name" : "Grad",
+   "comment" : "(Tensor, default Tensor<float>) Input gradient of the parameter.",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "outputs" : [ 
+ }, { 
+   "name" : "LearningRate",
+   "comment" : "(Tensor, default Tensor<float>) The learning rate should be a tensor of size 1.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
  { 
-   "name" : "Output",
-   "comment" : "(Tensor) The output tensor of convolution operator. The format of output tensor is also NCHW.",
+   "name" : "ParamOut",
+   "comment" : "(Tensor) Output updated parameter value.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "strides",
-   "type" : "int array",
-   "comment" : "(vector<int> default:{1, 1}), the strides(h_stride, w_stride) of convolution operator.",
-   "generated" : 0
- }, { 
-   "name" : "paddings",
-   "type" : "int array",
-   "comment" : "(vector<int> default:{0, 0}), the paddings(h_pad, w_pad) of convolution operator.",
-   "generated" : 0
- }, { 
-   "name" : "groups",
-   "type" : "int",
-   "comment" : "(int default:1), the groups number of the convolution operator. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels.",
-   "generated" : 0
- }, { 
-   "name" : "dilations",
-   "type" : "int array",
-   "comment" : "(vector<int> default:{1, 1}), the dilations(h_dilation, w_dilation) of convolution operator.",
+   "name" : "l1",
+   "type" : "float",
+   "comment" : "(float, default 0.0) L1 regularization strength.",
    "generated" : 0
  }, { 
-   "name" : "workspace_size_MB",
-   "type" : "int",
-   "comment" : "workspace size for cudnn, in MB, workspace is a section of GPU memory which will be allocated/freed each time the operator runs, larger workspace size can increase performance but also requires better hardware. This size should be chosen carefully.",
+   "name" : "l2",
+   "type" : "float",
+   "comment" : "(float, default 0.0) L2 regularization strength.",
    "generated" : 0
  } ] 
 },{
- "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",
+ "type" : "prelu",
+ "comment" : "\nPRelu Operator.\n\nThe equation is:\n\n$$\nf(x) =\n\\begin{cases}\n\\alpha * x, \\quad  \\text{if} \\ x < 0 \\\\\nx,         \\qquad  \\text{if} \\ x >= 0\n\\end{cases}\n$$\n\nThe input `X` can carry the LoD (Level of Details) information,\nor not. And the output shares the LoD information with input `X`.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The first input of mul op",
+   "comment" : "The input tensor of prelu operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "The second input of mul op",
+   "name" : "Alpha",
+   "comment" : "The alpha weight of prelu operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of mul op",
+   "comment" : "The output tensor of prelu operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "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        ",
-   "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        ",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "margin_rank_loss",
- "comment" : "\nMarginRankLoss Operator.\n\nThis operator measures the loss given a pair of training sample\n{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1` \nindicating X1 is ranked higher than `X2` and `Label = -1` otherwise. The loss \nis calculated as:\n\n$loss(X1, X2, Label) = \\max(0, -Label * (X1 - X2) + margin)$\n\nThe attribute `margin` here helps make the predictions more robust.\nDenote the item ranked higher as the positive sample, otherwise the negative \nsample. If the score of the two samples satisfies \n\n$positive sample - negative sample < margin$\n\nthe pair of samples will contribute to the final loss, which will backpropagate \nand train the ranking model to enlarge the difference between the two scores.\n\nFor batch input with size `batch_size`, `X1`, `X2` and `Label`\nall have the same shape [batch_size x 1].\n\n",
+ "type" : "proximal_adagrad",
+ "comment" : "\nProximal Adagrad Optimizer.\n\nOptimizer that implements the proximal adagrad algorithm:\n\n$$\nmoment = moment + grad * grad \\\\\nprox\\_param = param - learning\\_rate * grad * (1 / \\sqrt{moment}) \\\\\nparam = sign(prox\\_param) / (1 + learning\\_rate * l2) *\n        \\max(|prox\\_param| - learning\\_rate * l1 , 0)\n$$\n\nThe paper that proposed Proximal GD: \n(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)\nHere, we use the adagrad learning rate as specified here: \n(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n\n",
  "inputs" : [ 
  { 
-   "name" : "X1",
-   "comment" : "(2-D tensor with shape [batch_size x 1]) The score for one item X1 to be ranked, from pairwise ranking model.",
+   "name" : "Param",
+   "comment" : "(Tensor, default Tensor<float>) Input parameter that has to be updated.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "X2",
-   "comment" : "(2-D tensor with shape [batch_size x 1]) The score for another item X2 to be ranked, from pairwise ranking model.",
+   "name" : "Moment",
+   "comment" : "(Tensor, default Tensor<float>) Moment parameter that has to be updated.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "(2-D tensor with shape [batch_size x 1]) The label indicating X1 ranked higher than X2 or not, can only be +1 or -1.",
+   "name" : "Grad",
+   "comment" : "(Tensor, default Tensor<float>) Input gradient of the parameter.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "LearningRate",
+   "comment" : "(Tensor, default Tensor<float>) The learning rate should be a tensor of size 1.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Activated",
-   "comment" : "(2-D tensor with shape [batch_size x 1]) Intermediate tensor to indicate whether each element of Output(Out) is activated.",
+   "name" : "ParamOut",
+   "comment" : "(Tensor) Output updated parameter value.",
    "duplicable" : 0,
-   "intermediate" : 1
+   "intermediate" : 0
  }, { 
-   "name" : "Out",
-   "comment" : "(2-D tensor with shape [batch_size x 1]) The output loss of MarginRankLoss operator.",
+   "name" : "MomentOut",
+   "comment" : "(Tensor) Output updated moment value.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "margin",
+   "name" : "l1",
    "type" : "float",
-   "comment" : "(scalar, default 0) Margin for MarginRankLossOp.",
+   "comment" : "(float, default 0.0) L1 regularization strength.",
    "generated" : 0
- } ] 
-},{
- "type" : "greater_equal",
- "comment" : "greater_equal Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X >= Y\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "(LoDTensor) the left hand operand of greater_equal operator",
-   "duplicable" : 0,
-   "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "(LoDTensor) the right hand operand of greater_equal operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X >= Y",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
+   "name" : "l2",
+   "type" : "float",
+   "comment" : "(float, default 0.0) L2 regularization strength.",
+   "generated" : 0
+ } ] 
 },{
  "type" : "reciprocal",
  "comment" : "\nReciprocal Activation Operator.\n\n$$y = \\frac{1}{x}$$\n\n",
@@ -1630,438 +1590,406 @@
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "squared_l2_norm",
- "comment" : "\nSquaredL2Norm Operator.\n\nComputes the squared L2 norm of a tensor.\n\n$$Out = \\sum_{i} X_{i}^2$$\n\n",
+ "type" : "merge_lod_tensor",
+ "comment" : "\n        Merge True and False branches of LoDTensor into a single Output,\n        with a mask at certain lod level. X is used to obtain complete\n        lod information. Please refer to SplitLoDTensorOp.",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input of squared_l2_norm op.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "(Scalar) The output of squared_l2_norm op.",
+   "comment" : "The input LoDTensor, contains complete lod information to construct the output",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "shrink_rnn_memory",
- "comment" : "\n        In dynamic RNN, we are able to handle sequences of different lengths. \n        Because of the multiple lengths, the size of each step input can be \n        different, which may lead to a mismatching between the input of\n        the current step and the memory generated by the previous one. This \n        operator shrinks memory according to the size of the next step input, \n        to make sure that they can match each other.\n        ",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "(LoDTensor) The RNN step memory to be shrinked.",
+ }, { 
+   "name" : "Mask",
+   "comment" : "A bool column vector which mask the input",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "RankTable",
-   "comment" : "(LoDRankTable) The lod_rank_table of dynamic RNN.",
+   "name" : "InTrue",
+   "comment" : "The True branch to be merged",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "I",
-   "comment" : "(LoDTensor) The step index. The RNN step memory 'X' will be shrinked to match the size of the input of the index'th step.",
+   "name" : "InFalse",
+   "comment" : "The False branch to be merged",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) The shrinked RNN step memory.",
+   "comment" : "The merged output LoDTensor",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "level",
+   "type" : "int",
+   "comment" : "(int) the specific lod level to rank.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "conditional_block",
- "comment" : "Conditional block operator\n\nRun the sub-block if X is not empty. Params is the other inputs and Out is the\noutputs of the sub-block.\n",
+ "type" : "precision_recall",
+ "comment" : "\nPrecision Recall Operator.\n\nWhen given Input(Indices) and Input(Labels), this operator can be used\nto compute various metrics including:\n1. macro average precision\n2. macro average recall\n3. macro f1 score\n4. micro average precision\n5. micro average recall\n6. micro f1 score\n\nTo compute the above metrics, we need to do statistics for true positives,\nfalse positives and false negatives. Here the count of true negatives is not\nnecessary, but counting it may provide potential usage and the cost is\ntrivial, so the operator also provides the count of true negatives.\n\nWe define state as a 2-D tensor with shape [class_number, 4]. Each row of a\nstate contains statistic variables for corresponding class. Layout of each row\nis: TP(true positives), FP(false positives), TN(true negatives),\nFN(false negatives). If Input(Weights) is provided, TP, FP, TN, FN will be\ncalculated by given weight instead of the instance count.\n\nThis operator also supports metrics computing for cross-batch situation. To\nachieve this, Input(StatesInfo) should be provided. State of current batch\ndata will be accumulated to Input(StatesInfo) and Output(AccumStatesInfo)\nis the accumulation state.\n\nOutput(BatchMetrics) is metrics of current batch data while\nOutput(AccumStatesInfo) is metrics of accumulation data.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The conditional variable of this operator. If X is empty, the whole sub-block will not be executed.",
-   "duplicable" : 1,
+   "name" : "MaxProbs",
+   "comment" : "(Tensor, default Tensor<float>) A 2-D tensor with shape N x 1, where N is the batch size. Each row contains the max probability of an instance which computed by the previous top_k (k=1) operator.",
+   "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Params",
-   "comment" : "The input variables of the sub-block.",
-   "duplicable" : 1,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "The output variables of the sub-block.",
-   "duplicable" : 1,
+   "name" : "Indices",
+   "comment" : "(Tensor, default Tensor<int>) A 2-D tensor with shape N x 1, where N is the batch size. Each row contains the corresponding index which computed by the previous top_k (k=1) operator.",
+   "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Scope",
-   "comment" : "(std::vector<Scope*>) The step scope of conditional block. To unify the conditional block, rnn and while op, the type of scope is std::vector<Scope*>",
+   "name" : "Labels",
+   "comment" : "(Tensor, default Tensor<int>) A 2-D tensor with shape N x 1, where N is the batch size. Each element is a label and the value should be in [0, class_number - 1].",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [ 
- { 
-   "name" : "block",
-   "type" : "block id",
-   "comment" : "The step block of conditional block operator",
-   "generated" : 0
- } ] 
-},{
- "type" : "lookup_table",
- "comment" : "\nLookup Table Operator.\n\nThis operator is used to perform lookups on the parameter W,\nthen concatenated into a dense tensor.\n\nThe input Ids can carry the LoD (Level of Details) information,\nor not. And the output only shares the LoD information with input Ids.\n\n",
- "inputs" : [ 
- { 
-   "name" : "W",
-   "comment" : "An input represents embedding tensors, which is a learnable parameter.",
+ }, { 
+   "name" : "Weights",
+   "comment" : "(Tensor, default Tensor<float>) A 2-D tensor with shape N x 1, where N is the batch size. This input is optional. If provided, weight of instance would be considered when computing metrics.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Ids",
-   "comment" : "An input with type int32 or int64 contains the ids to be looked up in W. Ids must be a column vector with rank = 2. The 2nd dimension size must be 1.",
+   "name" : "StatesInfo",
+   "comment" : "(Tensor, default Tensor<int>) A 2-D tensor with shape D x 4, where D is the number of classes. This input is optional. If provided, current state will be accumulated to this state and the accumulation state will be the output state.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The lookup results, which have the same type as W.",
+   "name" : "BatchMetrics",
+   "comment" : "(Tensor, default Tensor<float>) A 1-D tensor with shape {6}. This output tensor contains metrics for current batch data. The layout is [macro average precision, macro average recall, macro f1 score, micro average precision, micro average recall, micro f1 score].",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "AccumMetrics",
+   "comment" : "(Tensor, default Tensor<float>) A 1-D tensor with shape {6}. This output tensor contains metrics for accumulated data. The layout is [macro average precision, macro average recall, macro f1 score, micro average precision, micro average recall, micro f1 score].",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "AccumStatesInfo",
+   "comment" : "(Tensor, default Tensor<float>) A 2-D tensor with shape D x 4, where D is equal to class number. This output tensor contains accumulated state variables used to compute metrics. The layout for each class is [true positives, false positives, true negatives, false negatives].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "is_sparse",
-   "type" : "bool",
-   "comment" : "(boolean, default false) Sparse update",
+   "name" : "class_number",
+   "type" : "int",
+   "comment" : "(int) Number of classes to be evaluated.",
    "generated" : 0
  } ] 
 },{
- "type" : "pad",
- "comment" : "\nPad Operator.\n\nPad input into output, as specified by paddings and pad_value. \nThe input should be a k-D tensor(k > 0 and k < 7). As an example:\n\nGiven:\n\nX = [[1, 2],\n     [3, 4]],\n\npaddings = [0, 1, 1, 2],\n\nand\n\npad_value = 0,\n\nwe have:\n\nOut = [[0, 1, 2, 0, 0]\n       [0, 3, 4, 0, 0]\n       [0, 0, 0, 0, 0]]\n\n",
+ "type" : "conv3d_cudnn",
+ "comment" : "\nConvolution3D Operator.\n\nThe convolution operation calculates the output based on the input, filter\nand strides, paddings, dilations, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format, where N is batch\nsize, C is the number of channels,D is the depth of the feature, H is the height of\nthe feature, and W is the width of the feature.\nFilters(Input) is MCDHW format, where M is the number of output image channels,\nC is the number of input image channels, D is the depth of the filter,\nH is the height of the filter, and W is the width of the filter.\nParameters(strides, paddings, dilations) are three elements. These three elements\nrepresent depth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{out}, C_{in}, D_f, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out}= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\\\\n       H_{out}= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\\\\n       W_{out}= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1\n  $$\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input of pad op. The input should be a k-D tensor(k > 0 and k < 7)",
+   "name" : "Input",
+   "comment" : "(Tensor) The input tensor of convolution operator. The format of input tensor is NCDHW. Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Filter",
+   "comment" : "(Tensor) The filter tensor of convolution operator. The format of the filter tensor is MCDHW, where M is the number of output image channels, C is the number of input image channels, D is the depth of the filter, H is the height of the filter, and W is the width of the filter.If the groups attribute is greater than 1, C equals the number of input image channels divided by the groups.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of pad op. A tensor with the same shape as X.",
+   "name" : "Output",
+   "comment" : "(Tensor) The output tensor of convolution operator.The format of output tensor is also NCDHW.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "paddings",
+   "name" : "strides",
    "type" : "int array",
-   "comment" : "(vector<int>) A list<int> to describe the padding rules for each dimension. For 2-D image tensor, paddings=[0, 1, 2, 3] means padding 0 row to top, 1 row to bottom, 2 columns to left and 3 columns to right. Size of paddings should be equal to 2 * dimension size of the input tensor.",
+   "comment" : "(vector<int>, default:{1, 1, 1}), the strides(d_stride, h_stride, w_stride) of convolution operator.",
    "generated" : 0
  }, { 
-   "name" : "pad_value",
-   "type" : "float",
-   "comment" : "(float, default 0.0) The value to fill the padded areas.",
+   "name" : "paddings",
+   "type" : "int array",
+   "comment" : "(vector<int>, default:{0, 0, 0}), the paddings(d_pad, h_pad, w_pad) of convolution operator.",
+   "generated" : 0
+ }, { 
+   "name" : "groups",
+   "type" : "int",
+   "comment" : "(int default:1), the groups number of the convolution operator. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels.",
+   "generated" : 0
+ }, { 
+   "name" : "dilations",
+   "type" : "int array",
+   "comment" : "(vector<int> default:{1, 1, 1}), the dilations(d_dilation, h_dilation, w_dilation) of convolution operator.",
+   "generated" : 0
+ }, { 
+   "name" : "workspace_size_MB",
+   "type" : "int",
+   "comment" : "workspace size for cudnn, in MB, workspace is a section of GPU memory which will be allocated/freed each time the operator runs, larger workspace size can increase performance but also requires better hardware. This size should be chosen carefully.",
    "generated" : 0
  } ] 
 },{
- "type" : "split_lod_tensor",
- "comment" : "\n        Split a LoDTensor with a Mask at certain level. The input LoDTensor\n        has 3 sequence at certain lod level. The Mask is a bool column vector,\n        such as [0, 1, 0] at the same level. The first and third sequence will\n        be send to False Output LoDTensor; whereas the second sequence will\n        be send to True Output LoDTensor. Please refer to MergeLoDTensorOp.",
+ "type" : "sigmoid_cross_entropy_with_logits",
+ "comment" : "\nSigmoidCrossEntropyWithLogits Operator.\n\nThis measures the element-wise probability error in classification tasks\nin which each class is independent. This can be thought of as predicting labels\nfor a data-point, where labels are not mutually exclusive.\nFor example, a news article can be about politics, technology or sports\nat the same time or none of these.\n\nThe logistic loss is given as follows:\n\n       $$loss = -Labels * \\log(\\sigma(X)) - (1 - Labels) * \\log(1 - \\sigma(X))$$\n\nWe know that $$\\sigma(X) = (1 / (1 + \\exp(-X)))$$. By substituting this we get:\n\n       $$loss = X - X * Labels + \\log(1 + \\exp(-X))$$\n\nFor stability and to prevent overflow of $$\\exp(-X)$$ when X < 0,\nwe reformulate the loss as follows:\n\n       $$loss = \\max(X, 0) - X * Labels + \\log(1 + \\exp(-|X|))$$\n\nBoth the input `X` and `Labels` can carry the LoD (Level of Details) information.\nHowever the output only shares the LoD with input `X`.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input LoDTensor",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a tensor of logits computed by the previous  operator. Logits are unscaled log probabilities given as log(p/(1-p)).",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Mask",
-   "comment" : "A bool column vector which mask the input",
+   "name" : "Label",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor of the same type and shape as X. This input is a tensor of probabalistic labels for each logit",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "OutTrue",
-   "comment" : "True branch of input LoDTensor",
+   "name" : "Out",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D  of elementwise logistic losses.",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "OutFalse",
-   "comment" : "False branch of input LoDTensor",
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "fill",
+ "comment" : "Fill operator\n\nFill an tensor with `value` and `shape`. The type of the tensor is specify by\n`dtype`.\n",
+ "inputs" : [  ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(LoDTensor) The output tensor.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "level",
+   "name" : "value",
+   "type" : "float array",
+   "comment" : "The float values of tensor, which are flatten in row major",
+   "generated" : 0
+ }, { 
+   "name" : "shape",
+   "type" : "int array",
+   "comment" : "The shape of output tensor",
+   "generated" : 0
+ }, { 
+   "name" : "dtype",
    "type" : "int",
-   "comment" : "(int) the specific lod level to split.",
+   "comment" : "The data type of output tensor, Default is float",
+   "generated" : 0
+ }, { 
+   "name" : "force_cpu",
+   "type" : "bool",
+   "comment" : "Whether the output tensor must be at CPU memory or not. Default is false.",
    "generated" : 0
  } ] 
 },{
- "type" : "max_sequence_len",
- "comment" : "Calculate the max sequence length through lod_rank_table.",
+ "type" : "rnn_memory_helper",
+ "comment" : "",
  "inputs" : [ 
  { 
-   "name" : "RankTable",
-   "comment" : "The lod_rank_table.",
+   "name" : "X",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The max sequence length.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "multiplex",
- "comment" : "\nMultiplex Operator.\n\nMultiplex multiple tensors according to the index provided by the index tensor.\n\nIds: the index tensor.\nX[0 : N - 1]: the candidate tensors for output (N >= 2).\nFor each index i from 0 to batchSize - 1, the output is the i-th row of the\nthe (Ids[i])-th tensor.\n\nFor i-th row of the output tensor:\n\n$$y[i] = x_{k}[i]$$\n\nwhere `y` is the output tensor, `x_{k}` is the k-th input tensor,\nand `k = Ids[i]`.\n\n",
- "inputs" : [ 
- { 
-   "name" : "Ids",
-   "comment" : "The index tensor of multiplex operator.",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "X",
-   "comment" : "The candidate tensors of multiplex operator.",
-   "duplicable" : 1,
-   "intermediate" : 0
  } ], 
- "outputs" : [ 
+ "attrs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output tensor of multiplex operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
+   "name" : "dtype",
+   "type" : "int",
+   "comment" : "(int, default 5 (FP32)) Output data type",
+   "generated" : 0
+ } ] 
 },{
- "type" : "stanh",
- "comment" : "\nSTanh Activation Operator.\n\n$$y = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$\n\n",
+ "type" : "pad",
+ "comment" : "\nPad Operator.\n\nPad input into output, as specified by paddings and pad_value. \nThe input should be a k-D tensor(k > 0 and k < 7). As an example:\n\nGiven:\n\nX = [[1, 2],\n     [3, 4]],\n\npaddings = [0, 1, 1, 2],\n\nand\n\npad_value = 0,\n\nwe have:\n\nOut = [[0, 1, 2, 0, 0]\n       [0, 3, 4, 0, 0]\n       [0, 0, 0, 0, 0]]\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of STanh operator",
+   "comment" : "The input of pad op. The input should be a k-D tensor(k > 0 and k < 7)",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of STanh operator",
+   "name" : "Out",
+   "comment" : "The output of pad op. A tensor with the same shape as X.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "scale_a",
-   "type" : "float",
-   "comment" : "The scale parameter of a for the input",
+   "name" : "paddings",
+   "type" : "int array",
+   "comment" : "(vector<int>) A list<int> to describe the padding rules for each dimension. For 2-D image tensor, paddings=[0, 1, 2, 3] means padding 0 row to top, 1 row to bottom, 2 columns to left and 3 columns to right. Size of paddings should be equal to 2 * dimension size of the input tensor.",
    "generated" : 0
  }, { 
-   "name" : "scale_b",
+   "name" : "pad_value",
    "type" : "float",
-   "comment" : "The scale parameter of b for the input",
+   "comment" : "(float, default 0.0) The value to fill the padded areas.",
    "generated" : 0
  } ] 
 },{
- "type" : "adamax",
- "comment" : "\nAdamax Optimizer.\n\nWe implement the Adamax optimizer from Section 7 of the Adam\npaper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the\nAdam algorithm based on the infinity norm.\n\nAdamax updates:\n\n$$\nmoment\\_out = \\beta_1 * moment + (1 - \\beta_1) * grad \\\\\ninf\\_norm\\_out = max(\\beta_2 * inf\\_norm + \\epsilon, |grad|) \\\\\nlearning\\_rate = \\frac{learning\\_rate}{1 - \\beta_{1\\_pow}} \\\\\nparam\\_out = param - learning\\_rate * \\frac{moment\\_out}{inf\\_norm\\_out}\n$$\n\nThe original paper does not have an epsilon attribute.\nHowever, it is added here for numerical stability to prevent the\ndivision by 0 error.\n\n",
+ "type" : "lstm_unit",
+ "comment" : "\nLstm Unit Operator\n\nEquation:\n\n$$\ni, f, o, j = split(X) \\\\\nC = C_{prev} * sigm(f + forget\\_bias) + sigm(i) * tanh(j) \\\\\nH = C * sigm(o)\n$$\n\n",
  "inputs" : [ 
  { 
-   "name" : "Param",
-   "comment" : "(Tensor) Input parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Grad",
-   "comment" : "(Tensor) Input gradient",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "LearningRate",
-   "comment" : "(Tensor) Learning rate",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Moment",
-   "comment" : "(Tensor) First moment",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "InfNorm",
-   "comment" : "(Tensor) Input exponentially weighted infinity norm",
+   "name" : "X",
+   "comment" : "FC input before the non-linear activation.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Beta1Pow",
-   "comment" : "(Tensor) Input beta1 power accumulator",
+   "name" : "C_prev",
+   "comment" : "The cell state tensor of last time-step in the Lstm Unit operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "ParamOut",
-   "comment" : "(Tensor) Output parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "MomentOut",
-   "comment" : "(Tensor) Output first moment",
+   "name" : "C",
+   "comment" : "The cell tensor of Lstm Unit operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "InfNormOut",
-   "comment" : "(Tensor) Output exponentially weighted infinity norm",
+   "name" : "H",
+   "comment" : "The hidden state tensor of Lstm Unit operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "beta1",
-   "type" : "float",
-   "comment" : "(float, default 0.9) Exponential decay rate for the 1st moment estimates.",
-   "generated" : 0
- }, { 
-   "name" : "beta2",
-   "type" : "float",
-   "comment" : "(float, default 0.999) exponential decay rate for the weighted infinity norm estimates.",
-   "generated" : 0
- }, { 
-   "name" : "epsilon",
+   "name" : "forget_bias",
    "type" : "float",
-   "comment" : "(float, default 1.0e-8) Constant for numerical stability",
+   "comment" : "(float, default 0.0) The forget bias of Lstm Unit.",
    "generated" : 0
  } ] 
 },{
- "type" : "l1_norm",
- "comment" : "\nL1 Norm Operator.\n\nComputes the L1 norm of a tensor.\n\n$$Out = \\sum{|X|}$$\n\n",
+ "type" : "squared_l2_norm",
+ "comment" : "\nSquaredL2Norm Operator.\n\nComputes the squared L2 norm of a tensor.\n\n$$Out = \\sum_{i} X_{i}^2$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input of l1_norm op.",
+   "comment" : "(Tensor) The input of squared_l2_norm op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Scalar) The output of l1_norm op.",
+   "comment" : "(Scalar) The output of squared_l2_norm op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "dropout",
- "comment" : "\nDropout Operator.\n\nDropout refers to randomly dropping out units in a nerual network. It is a\nregularization technique for reducing overfitting by preventing neuron\nco-adaption during training. The dropout operator randomly set (according to\nthe given dropout probability) the outputs of some units to zero, while others\nare set equal to their corresponding inputs.\n\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "The input of dropout op.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
+ "type" : "uniform_random",
+ "comment" : "\nUniform random operator.\n\nThis operator initializes a tensor with random values sampled from a \nuniform distribution.\n\n",
+ "inputs" : [  ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of dropout op.",
+   "comment" : "(Tensor) The output tensor of uniform random op",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "Mask",
-   "comment" : "The random sampled dropout mask.",
-   "duplicable" : 0,
-   "intermediate" : 1
  } ], 
  "attrs" : [ 
  { 
-   "name" : "dropout_prob",
+   "name" : "shape",
+   "type" : "int array",
+   "comment" : "(vector<int>) The shape of the output tensor",
+   "generated" : 0
+ }, { 
+   "name" : "min",
    "type" : "float",
-   "comment" : "Probability of setting units to zero.",
+   "comment" : "(float, default -1.0) Minimum value of uniform random",
    "generated" : 0
  }, { 
-   "name" : "is_test",
-   "type" : "bool",
-   "comment" : "True if in test phase.",
+   "name" : "max",
+   "type" : "float",
+   "comment" : "(float, default 1.0) Maximun value of uniform random",
    "generated" : 0
  }, { 
    "name" : "seed",
    "type" : "int",
-   "comment" : "Dropout random seed.",
+   "comment" : "(int, default 0) Random seed used for generating samples. 0 means use a seed generated by the system.",
+   "generated" : 0
+ }, { 
+   "name" : "dtype",
+   "type" : "int",
+   "comment" : "(int, default 5(FP32)) Output tensor data type",
    "generated" : 0
  } ] 
 },{
- "type" : "lod_tensor_to_array",
- "comment" : "",
+ "type" : "sign",
+ "comment" : "\nSign operator\n\n$$Out = X.sign()$$\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "RankTable",
-   "comment" : "",
+   "comment" : "(Tensor) Input tensor of sign operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "",
+   "comment" : "(Tensor) Output tensor of sign operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "pool2d_cudnn",
- "comment" : "\nPool2d Operator.\n\nThe pooling2d operation calculates the output based on\nthe input, pooling_type and ksize, strides, paddings parameters.\nInput(X) and output(Out) are in NCHW format, where N is batch size, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nParameters(ksize, strides, paddings) are two elements.\nThese two elements represent height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:   \n  Input:\n       X shape: $(N, C, H_{in}, W_{in})$\n  Output:\n       Out shape: $(N, C, H_{out}, W_{out})$\n  Where\n       $$ \n       H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\\\\n       W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1\n       $$\n\n",
+ "type" : "roi_pool",
+ "comment" : "\nROIPool operator\n\nROI Pooling for Faster-RCNN. The link below is a further introduction: \nhttps://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn\n    ",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input tensor of pooling operator. The format of input tensor is NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.",
+   "comment" : "(Tensor), the input of ROIPoolOp. The format of input tensor is NCHW. Where N is batch size, C is the number of input channels, H is the height of the feature, and W is the width of the feature.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "ROIs",
+   "comment" : "(Tensor), ROIs (Regions of Interest) to pool over. should be a 2-D tensor of shape (num_rois, 5)given as [[batch_id, x1, y1, x2, y2], …]. Where batch_id is the id of the data, (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) The output tensor of pooling operator. The format of output tensor is also NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.",
+   "comment" : "(Tensor), The output of ROIPoolOp is a 4-D tensor with shape (num_rois, channels, pooled_h, pooled_w).",
    "duplicable" : 0,
    "intermediate" : 0
+ }, { 
+   "name" : "Argmax",
+   "comment" : "(Tensor), Argmaxes corresponding to indices in X used for gradient computation. Only output if arg “is_test” is false.",
+   "duplicable" : 0,
+   "intermediate" : 1
  } ], 
  "attrs" : [ 
  { 
-   "name" : "pooling_type",
-   "type" : "string",
-   "comment" : "(string), pooling type, can be \"max\" for max-pooling and \"avg\" for average-pooling.",
-   "generated" : 0
- }, { 
-   "name" : "ksize",
-   "type" : "int array",
-   "comment" : "(vector<int>) The pooling window size(height, width) of the pooling operator. If global_pooling = true, ksize and paddings will be ignored.",
-   "generated" : 0
- }, { 
-   "name" : "global_pooling",
-   "type" : "bool",
-   "comment" : "(bool, default false) Whether to use the global pooling. If global_pooling = true, ksize and paddings will be ignored.",
+   "name" : "spatial_scale",
+   "type" : "float",
+   "comment" : "(float, default 1.0), Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling.",
    "generated" : 0
  }, { 
-   "name" : "strides",
-   "type" : "int array",
-   "comment" : "(vector<int>, default {1, 1}), strides(height, width) of pooling operator.",
+   "name" : "pooled_height",
+   "type" : "int",
+   "comment" : "(int, default 1), The pooled output height.",
    "generated" : 0
  }, { 
-   "name" : "paddings",
-   "type" : "int array",
-   "comment" : "(vector<int>, default {0,0}), paddings(height, width) of pooling operator.If global_pooling = true, paddings and ksize will be ignored.",
+   "name" : "pooled_width",
+   "type" : "int",
+   "comment" : "(int, default 1), The pooled output width.",
    "generated" : 0
  } ] 
 },{
@@ -2109,871 +2037,1015 @@
    "generated" : 0
  } ] 
 },{
- "type" : "gaussian_random",
- "comment" : "\nGaussianRandom Operator.\n\nUsed to initialize tensors with gaussian random generator.\n\n",
- "inputs" : [  ], 
+ "type" : "reduce_min",
+ "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the min of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "Output matrix of gaussian random op",
+   "comment" : "(Tensor) The result tensor.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "shape",
-   "type" : "int array",
-   "comment" : "(vector<int>) The dimension of random tensor.",
-   "generated" : 0
- }, { 
-   "name" : "mean",
-   "type" : "float",
-   "comment" : "(float, default 0.0) mean of random tensor.",
-   "generated" : 0
- }, { 
-   "name" : "std",
-   "type" : "float",
-   "comment" : "(float, default 1.0) std of random tensor.",
-   "generated" : 0
- }, { 
-   "name" : "seed",
+   "name" : "dim",
    "type" : "int",
-   "comment" : "(int, default 0) Random seed of generator.0 means use system wide seed.",
+   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
    "generated" : 0
  }, { 
-   "name" : "dtype",
-   "type" : "int",
-   "comment" : "(int, default 5(FP32)) Output data type.",
+   "name" : "keep_dim",
+   "type" : "bool",
+   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
    "generated" : 0
  } ] 
 },{
- "type" : "lstm_unit",
- "comment" : "\nLstm Unit Operator\n\nEquation:\n\n$$\ni, f, o, j = split(X) \\\\\nC = C_{prev} * sigm(f + forget\\_bias) + sigm(i) * tanh(j) \\\\\nH = C * sigm(o)\n$$\n\n",
+ "type" : "smooth_l1_loss",
+ "comment" : "\nSmooth L1 Loss Operator.\n\nThis operator computes the smooth l1 loss for X and Y.\nThe operator takes the first dimension of X and Y as batch size.\nFor each instance, it computes the smooth l1 loss element by element first\nand then sums all the losses. So the shape of Out is [batch_size, 1].\n\nThe equation is:\n$$\nOut_{\\sigma}(X, Y)_i = \\begin{cases}\n0.5 * (\\sigma * (X_i - Y_i)) ^ 2\n\\quad |X_i - Y_i| \\lt \\frac{1} {{\\sigma} ^ 2} \\\\\n\\frac{|X_i - Y_i| - 0.5}{{\\sigma}^2},\n\\quad otherwise\n\\end{cases}\n$$\n\nIn the above equation, $Out_{\\sigma}(X, Y)_i$, $X_i$ and $Y_i$ represent the ith\nelement of Out, X and Y.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "FC input before the non-linear activation.",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. The input value of smooth l1 loss op with shape [batch_size, dim1, ..., dimN].",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "C_prev",
-   "comment" : "The cell state tensor of last time-step in the Lstm Unit operator.",
+   "name" : "Y",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. The target value of smooth l1 loss op with same shape as X.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "InsideWeight",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. This input is optional and should have same shape with X. If provided, the result of (X - Y) will be multiplied by this tensor element by element.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "OutsideWeight",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. This input is optional and should have same shape with X. If provided, the out smooth l1 loss will be multiplied by this tensor element by element.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "C",
-   "comment" : "The cell tensor of Lstm Unit operator.",
+   "name" : "Diff",
+   "comment" : "Intermediate variable to cache InsideWeight * (X - Y).",
    "duplicable" : 0,
-   "intermediate" : 0
+   "intermediate" : 1
  }, { 
-   "name" : "H",
-   "comment" : "The hidden state tensor of Lstm Unit operator.",
+   "name" : "Out",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank be 2. The output smooth l1 loss with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "forget_bias",
+   "name" : "sigma",
    "type" : "float",
-   "comment" : "(float, default 0.0) The forget bias of Lstm Unit.",
+   "comment" : "Hyper parameter of smooth l1 loss op.A float scalar with default value 3.0.",
    "generated" : 0
  } ] 
 },{
- "type" : "sign",
- "comment" : "\nSign operator\n\n$$Out = X.sign()$$\n",
+ "type" : "log_loss",
+ "comment" : "\nLogLoss Operator.\n\nLog loss is a loss function used for binary classification. Log Loss quantifies\nthe accuracy of a classifier by penalising false classifications. Minimising the\nLog Loss is equivalent to maximising the accuracy of the classifier. We define\nPredicted as the values predicted by our model and Labels as the target ground\ntruth value. Log loss can evaluate how close the predicted values are to the\ntarget. The shapes of Predicted and Labels are both [batch_size, 1].\nThe equation is:\n\n$$\nLoss = - Labels * log(Predicted + \\epsilon) -\n        (1 - Labels) * log(1 - Predicted + \\epsilon)\n$$\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(Tensor) Input tensor of sign operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "(Tensor) Output tensor of sign operator.",
+   "name" : "Predicted",
+   "comment" : "The input value (Predicted) of Log loss op.Predicted is a 2-D tensor with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "pow",
- "comment" : "\nPow Activation Operator.\n\n$y = x^{factor}$\n\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "Input of Pow operator",
+ }, { 
+   "name" : "Labels",
+   "comment" : "The target value (Labels) of Log loss op.Labels is a 2-D tensor with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Pow operator",
+   "name" : "Loss",
+   "comment" : "The output tensor with shape [batch_size, 1] which represents the log loss.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "factor",
+   "name" : "epsilon",
    "type" : "float",
-   "comment" : "The exponential factor of Pow",
+   "comment" : "Epsilon in log loss.",
    "generated" : 0
  } ] 
 },{
- "type" : "clip",
- "comment" : "\nClip Operator.\n\nThe clip operator limits the value of given input within an interval. The interval is\nspecified with arguments 'min' and 'max':\n\n$$\nOut = \\min(\\max(X, min), max)\n$$\n\n",
+ "type" : "unpool",
+ "comment" : "\n        \"Input shape: $(N, C_{in}, H_{in}, W_{in})$\n        Output shape: $(N, C_{out}, H_{out}, W_{out})$\n        Where\n          $$\n            H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\\\\n            W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1]\n          $$\n        Paper: http://www.matthewzeiler.com/wp-content/uploads/2017\n        /07/iccv2011.pdf\n        ",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor)The input of clip op.The number of dimensions must be between [1, 9].",
+   "comment" : "(Tensor) The input tensor of unpool operator. The format of input tensor is NCHW. Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Indices",
+   "comment" : "(Tensor) The input tensor of the indices given out by MaxPool2d. The format of input tensor is NCHW. Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor)The output of clip op with shape as input(X)",
+   "comment" : "(Tensor) The output tensor of unpool operator.The format of output tensor is also NCHW.Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "min",
-   "type" : "float",
-   "comment" : "(float)Minimum value, under which element is replaced by min.",
+   "name" : "ksize",
+   "type" : "int array",
+   "comment" : "(vector), the unpooling window size(height, width) of unpooling operator.",
    "generated" : 0
  }, { 
-   "name" : "max",
-   "type" : "float",
-   "comment" : "(float)Maximum value, above which element is replaced by max",
+   "name" : "strides",
+   "type" : "int array",
+   "comment" : "(vector, default:{1, 1}), strides (height, width) of unpooling operator.",
+   "generated" : 0
+ }, { 
+   "name" : "paddings",
+   "type" : "int array",
+   "comment" : "(vector defalut:{0,0}), paddings (height, width) of unpooling operator.",
+   "generated" : 0
+ }, { 
+   "name" : "unpooling_type",
+   "type" : "string",
+   "comment" : "(string), unpooling type, can be \"max\" for max-unpooling ",
    "generated" : 0
  } ] 
 },{
- "type" : "huber_loss",
- "comment" : "\nHuberLoss Operator.\n\nHuber loss is a loss function used in robust regression. We define X as the\ninput value and Y as the target value. Huber loss can evaluate the fitness of\nX to Y. Different from MSE loss, Huber loss is more robust for outliers. The\nshape of X and Y are [batch_size, 1]. The equation is:\n\n$$\nOut_{\\delta}(X, Y)_i =\n\\begin{cases}\n0.5 * (Y_i - X_i)^2,\n\\quad |Y_i - X_i| \\leq \\delta \\\\\n\\delta * (|Y_i - X_i| - 0.5 * \\delta),\n\\quad otherwise\n\\end{cases}\n$$\n\nIn the above equation, $Out_\\delta(X, Y)_i$, $X_i$ and $Y_i$ represent the ith\nelement of Out, X and Y.\n\n",
+ "type" : "transpose",
+ "comment" : "\nTranspose Operator.\n\nThe input tensor will be permuted according to the axis values given.\nThe op functions similar to how numpy.transpose works in python.\nFor example:\n >> input = numpy.arange(6).reshape((2,3))\n >> input\n array([[0, 1, 2],\n        [3, 4, 5]])\n >> axis = [1, 0]\n >> output = input.transpose(axis)\n >> output\n array([[0, 3],\n        [1, 4],\n\t\t[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",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input value of huber loss op.X is a 2-D tensor with shape [batch_size, 1].",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "The target value of huber loss op.Y is a 2-D tensor with shape [batch_size, 1].",
+   "comment" : "(Tensor)The input tensor, tensors with rank at most 6 are supported",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Residual",
-   "comment" : "Intermediate tensor to cache residual value between Y and X.The shape is same as Input(X) and will be reused in backward.",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
    "name" : "Out",
-   "comment" : "The output tensor with shape [batch_size, 1] which represents the huber loss.",
+   "comment" : "(Tensor)The output tensor",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "delta",
-   "type" : "float",
-   "comment" : "Hyper parameter in huber loss.",
+   "name" : "axis",
+   "type" : "int array",
+   "comment" : "(vector<int>)A list of values, and the size of the list should be the same with the input tensor rank, the tensor will permute the axes according the the values given",
    "generated" : 0
  } ] 
 },{
- "type" : "smooth_l1_loss",
- "comment" : "\nSmooth L1 Loss Operator.\n\nThis operator computes the smooth l1 loss for X and Y.\nThe operator takes the first dimension of X and Y as batch size.\nFor each instance, it computes the smooth l1 loss element by element first\nand then sums all the losses. So the shape of Out is [batch_size, 1].\n\nThe equation is:\n$$\nOut_{\\sigma}(X, Y)_i = \\begin{cases}\n0.5 * (\\sigma * (X_i - Y_i)) ^ 2\n\\quad |X_i - Y_i| \\lt \\frac{1} {{\\sigma} ^ 2} \\\\\n\\frac{|X_i - Y_i| - 0.5}{{\\sigma}^2},\n\\quad otherwise\n\\end{cases}\n$$\n\nIn the above equation, $Out_{\\sigma}(X, Y)_i$, $X_i$ and $Y_i$ represent the ith\nelement of Out, X and Y.\n\n",
+ "type" : "rnn_memory_helper_grad",
+ "comment" : "",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. The input value of smooth l1 loss op with shape [batch_size, dim1, ..., dimN].",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. The target value of smooth l1 loss op with same shape as X.",
+   "name" : "Out@GRAD",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "InsideWeight",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. This input is optional and should have same shape with X. If provided, the result of (X - Y) will be multiplied by this tensor element by element.",
+   "name" : "X",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "OutsideWeight",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank at least 2. This input is optional and should have same shape with X. If provided, the out smooth l1 loss will be multiplied by this tensor element by element.",
+   "name" : "Out",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Diff",
-   "comment" : "Intermediate variable to cache InsideWeight * (X - Y).",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
-   "name" : "Out",
-   "comment" : "(Tensor, default Tensor<float>) A tensor with rank be 2. The output smooth l1 loss with shape [batch_size, 1].",
+   "name" : "X@GRAD",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "sigma",
-   "type" : "float",
-   "comment" : "Hyper parameter of smooth l1 loss op.A float scalar with default value 3.0.",
+   "name" : "dtype",
+   "type" : "int",
+   "comment" : "(int, default 5 (FP32)) Output data type",
    "generated" : 0
  } ] 
 },{
- "type" : "beam_search",
- "comment" : "This is a beam search operator that help to generate sequences.",
+ "type" : "swish",
+ "comment" : "\nSwish Activation Operator.\n\n$$y = \\frac{x}{1 + e^{- \\beta x}}$$\n\n",
  "inputs" : [ 
  { 
-   "name" : "pre_ids",
-   "comment" : "ids in previous step",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "ids",
-   "comment" : "a LoDTensor of shape of [None,k]",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "scores",
-   "comment" : "a LoDTensor that has the same shape and LoD with `ids`",
+   "name" : "X",
+   "comment" : "Input of Swish operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "selected_ids",
-   "comment" : "a LoDTensor that stores the IDs selected by beam search",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "selected_scores",
-   "comment" : "a LoDTensor that has the same shape and LoD with `selected_ids`",
+   "name" : "Y",
+   "comment" : "Output of Swish operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "level",
-   "type" : "int",
-   "comment" : "the level of LoDTensor",
-   "generated" : 0
- }, { 
-   "name" : "beam_size",
-   "type" : "int",
-   "comment" : "beam size for beam search",
-   "generated" : 0
- }, { 
-   "name" : "end_id",
-   "type" : "int",
-   "comment" : "the token id which indicates the end of a sequence",
+   "name" : "beta",
+   "type" : "float",
+   "comment" : "Constant beta of swish operator",
    "generated" : 0
  } ] 
 },{
- "type" : "sum",
- "comment" : "\nSum operator.\n\nThis operators sums the input tensors. All the inputs can carry the \nLoD (Level of Details) information. However, the output only shares \nthe LoD information with the first input.\n",
+ "type" : "is_empty",
+ "comment" : "\nIsEmpty Operator which checks whether a tensor is empty.\n\nIt will just return product(tensor.ddims()) > 0;\n              ",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(vector<Tensor>) The input tensors of sum operator.",
-   "duplicable" : 1,
+   "comment" : "(Tensor) Tensor which is to be checked.",
+   "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) The output tensor of sum operator.",
+   "comment" : "(Tensor) a boolean Tensor that indicate empty or not.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "concat",
- "comment" : "\nConcat Operator.\n\nConcatenate the input tensors along dimension axis.\nExamples:\n  Input[0] = [[1,2],[3,4]]\n  Input[1] = [[5,6]]\n  axis = 0\n  Output = [[1,2],\n            [3,4],\n            [5,6]]\n\n",
+ "type" : "reduce_max",
+ "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the max of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input tensors of concat operator.",
-   "duplicable" : 1,
+   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
+   "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "Output tensor of concat operator.",
+   "comment" : "(Tensor) The result tensor.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "axis",
+   "name" : "dim",
    "type" : "int",
-   "comment" : "The axis along which the input tensors will be concatenated.",
+   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
+   "generated" : 0
+ }, { 
+   "name" : "keep_dim",
+   "type" : "bool",
+   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
    "generated" : 0
  } ] 
 },{
- "type" : "softmax_with_cross_entropy",
- "comment" : "\nSoftmax With Cross Entropy Operator.\n\nCross entropy loss with softmax is used as the output layer extensively. This\noperator computes the softmax normalized values for each row of the input\ntensor, after which cross-entropy loss is computed. This provides a more\nnumerically stable gradient.\n\nBecause this operator performs a softmax on logits internally, it expects\nunscaled logits. This operator should not be used with the output of\nsoftmax operator since that would produce incorrect results.\n\nWhen the attribute soft_label is set false, this operators expects mutually\nexclusive hard labels, each sample in a batch is in exactly one class with a\nprobability of 1.0. Each sample in the batch will have a single label.\n\nThe equation is as follows:\n\n1) Hard label (one-hot label, so every sample has exactly one class)\n\n$$Loss_j =  -\\text{Logit}_{Label_j} +\n\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{Logit}_i)\\right),\nj = 1,..., K$$\n\n2) Soft label (each sample can have a distribution over all classes)\n\n$$Loss_j =  -\\sum_{i=0}^{K}\\text{Label}_i \\left(\\text{Logit}_i -\n\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{Logit}_i)\\right)\\right),\nj = 1,...,K$$\n\n",
+ "type" : "rank_loss",
+ "comment" : "\nRankLoss Operator.\n\nRankLoss operator for RankNet\n(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf). \nRankNet is a pairwise ranking model with\none training sample consisting of a pair of doc A and B, and the label P\nindicating that A is ranked higher than B or not:\n\nP = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of\nthe input pair.\n\nThe RankLoss operator takes three inputs: Left (o_i), Right (o_j) and Label\n(P_{i,j}), which represent the output score of RankNet for the two docs and \nthe label respectively, and yields the rank loss C_{i,j} using the following \nequation:\n\n$$\n  C_{i,j} = -\\tilde{P_{ij}} * o_{i,j} + \\log(1 + e^{o_{i,j}}) \\\\\n  o_{i,j} =  o_i - o_j  \\\\\n  \\tilde{P_{i,j}} = \\left \\{0, 0.5, 1 \\right \\} \\ or \\ \\left \\{0, 1 \\right \\}\n$$\n\nThe operator can take batch inputs with size batch_size (batch_size >= 1).\n\n",
  "inputs" : [ 
  { 
-   "name" : "Logits",
-   "comment" : "(Tensor, default: Tensor<float>), The unscaled log probabilities which is a 2-D tensor with shape [N x K]. N is the batch_size, and K is the class number.",
+   "name" : "Label",
+   "comment" : "(2-D Tensor with shape [batch_size x 1]) The label indicating A ranked higher than B or not.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "(Tensor) The ground truth which is a 2-D tensor. If soft_label is set to false, Label is a Tensor<int64> with shape [N x 1]. If soft_label is set to true, Label is a Tensor<float/double> with shape [N x K].",
+   "name" : "Left",
+   "comment" : "(2-D Tensor with shape [batch_size x 1]) The output of RankNet for doc A.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Right",
+   "comment" : "(2-D Tensor with shape [batch_size x 1]) The output of RankNet for doc B.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Softmax",
-   "comment" : "(Tensor, default: Tensor<float>), A 2-D tensor with shape [N x K]. The outputs value of softmax activation by given the input batch, which will be used in backward calculation.",
+   "name" : "Out",
+   "comment" : "(2-D Tensor with shape [batch_size x 1]) The output loss of RankLoss operator.",
    "duplicable" : 0,
-   "intermediate" : 1
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "greater_than",
+ "comment" : "greater_than Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X > Y\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(LoDTensor) the left hand operand of greater_than operator",
+   "duplicable" : 0,
+   "intermediate" : 0
  }, { 
-   "name" : "Loss",
-   "comment" : "(Tensor, default: Tensor<float>), A 2-D tensor. The cross entropy loss with shape [N x 1].",
+   "name" : "Y",
+   "comment" : "(LoDTensor) the right hand operand of greater_than operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
+ "outputs" : [ 
  { 
-   "name" : "soft_label",
-   "type" : "bool",
-   "comment" : "(bool, default: false), A flag to indicate whether to interpretate the given labels as soft labels.",
-   "generated" : 0
- } ] 
+   "name" : "Out",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X > Y",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
 },{
- "type" : "fill_constant_batch_size_like",
- "comment" : "\nFillConstantBatchSizeLike Operator.\n\nFill up a variable with specified constant value.\n\n",
+ "type" : "sequence_softmax",
+ "comment" : "\nSequence Softmax Operator.\n\nSequenceSoftmaxOp computes the softmax activation among all time-steps for each\nsequence. The dimension of each time-step should be 1. Thus, the shape of\ninput Tensor can be either [N, 1] or [N], where N is the sum of the length\nof all sequences.\n\nThe algorithm works as follows:\n    for i-th sequence in a mini-batch:\n        $$Out(X[lod[i]:lod[i+1]], :) =\n            \\frac{\\exp(X[lod[i]:lod[i+1], :])}\n            {\\sum(\\exp(X[lod[i]:lod[i+1], :]))}$$\n\nFor example, for a mini-batch of 3 sequences with variable-length,\neach containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],\nthen softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :]\nand N turns out to be 7.\n\n",
  "inputs" : [ 
  { 
-   "name" : "Input",
-   "comment" : "(Tensor) Tensor whose dim_idx th dimension is used to specify the batch_size",
+   "name" : "X",
+   "comment" : "(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) Tensor of specified shape will be filled with the specified value",
+   "comment" : "(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "dtype",
-   "type" : "int",
-   "comment" : "(int, default 5 (FP32)) Output data type",
-   "generated" : 0
- }, { 
-   "name" : "shape",
-   "type" : "int array",
-   "comment" : "(vector<int>) The shape of the output",
-   "generated" : 0
- }, { 
-   "name" : "input_dim_idx",
-   "type" : "int",
-   "comment" : "(int, default 0) The index of input's batch size dimension",
-   "generated" : 0
- }, { 
-   "name" : "output_dim_idx",
-   "type" : "int",
-   "comment" : "(int, default 0) The index of output's batch size dimension",
-   "generated" : 0
- }, { 
-   "name" : "value",
-   "type" : "float",
-   "comment" : "(float, default 0) The value to be filled",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "adadelta",
- "comment" : "\nAdadelta Optimizer.\n\nAdadelta optimizer is implemented as explained in:\nhttps://arxiv.org/abs/1212.5701\nAdadelta is a per-dimension adaptive learning rate method used\nfor gradient descent.\n\nAdadelta updates are as follows:\n\n$$\navg\\_squared\\_grad\\_out = \\rho * avg\\_squared\\_grad + (1 - \\rho) * grad * grad \\\\\nparam\\_update =  - \\sqrt{\\frac{avg\\_squared\\_update + \\epsilon}{avg\\_squared\\_grad\\_out + \\epsilon}} * grad \\\\\navg\\_squared\\_update\\_out = \\rho * avg\\_squared\\_update + (1 - \\rho) * {param\\_update}^2 \\\\\nparam\\_out = param + param\\_update\n$$\n\n",
+ "type" : "momentum",
+ "comment" : "\nMomentum Optimizer.\n\nThis optimizer has a flag for Nestrov Momentum.\nThe update equations are as follows:\n\n$$\nvelocity = mu * velocity + gradient \\\\\nif (use\\_nesterov):   \\\\\n  param = param - gradient * learning\\_rate + mu * velocity * learning\\_rate \\\\\nelse:   \\\\\n  param = param - learning\\_rate * velocity. \\\\\n$$\n\n",
  "inputs" : [ 
  { 
    "name" : "Param",
-   "comment" : "(Tensor) Input parameter",
+   "comment" : "(Tensor, default Tensor<float>) Input parameter that has to be updated",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Grad",
-   "comment" : "(Tensor) Input gradient",
+   "comment" : "(Tensor, default Tensor<float>) Input gradient of the parameter",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "AvgSquaredGrad",
-   "comment" : "(Tensor) Input average of squared gradient",
+   "name" : "Velocity",
+   "comment" : "(Tensor, default Tensor<float>) Input velocity (corresponding to the parameter) that has to be updated",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "AvgSquaredUpdate",
-   "comment" : "(Tensor) Input average of squared parameter updates",
+   "name" : "LearningRate",
+   "comment" : "(Tensor, default Tensor<float>) Input learning rate",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "ParamOut",
-   "comment" : "(Tensor) Output parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "AvgSquaredGradOut",
-   "comment" : "(Tensor) Output average of squared gradient",
+   "comment" : "(Tensor) This output is updated parameter. It shared memory with Input(Param).",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "AvgSquaredUpdateOut",
-   "comment" : "(Tensor) Output average of squared parameter updates",
+   "name" : "VelocityOut",
+   "comment" : "(Tensor) This output is updated velocity. It shared memory with Input(Velocity).",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "rho",
+   "name" : "mu",
    "type" : "float",
-   "comment" : "(float, default 0.95) Exponential decay rate for squared gradients.",
+   "comment" : "(float) Momentum coefficient",
    "generated" : 0
  }, { 
-   "name" : "epsilon",
-   "type" : "float",
-   "comment" : "(float, default 1.0e-6) Constant for numerical stability",
+   "name" : "use_nesterov",
+   "type" : "bool",
+   "comment" : "(bool, default false) Use Nesterov Momentum",
    "generated" : 0
  } ] 
 },{
- "type" : "log",
- "comment" : "\nLog Activation Operator.\n\n$y = \\ln(x)$\n\nNatural logarithm of x.\n\n",
+ "type" : "scatter",
+ "comment" : "\nScatter Operator.\n\nThis operator obtains output by updating the input on selected indices on the first axis:\n\n$$\nOut = Ref \\\\\nOut[Index] = Ref[Index] + Updates\n$$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "Ref",
+   "comment" : "The source input of scatter op",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Index",
+   "comment" : "The index input of scatter op where Ref will be updated",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Updates",
+   "comment" : "The updated value of updates op",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "The output of add op",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "shrink_rnn_memory",
+ "comment" : "\n        In dynamic RNN, we are able to handle sequences of different lengths. \n        Because of the multiple lengths, the size of each step input can be \n        different, which may lead to a mismatching between the input of\n        the current step and the memory generated by the previous one. This \n        operator shrinks memory according to the size of the next step input, \n        to make sure that they can match each other.\n        ",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Log operator",
+   "comment" : "(LoDTensor) The RNN step memory to be shrinked.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "RankTable",
+   "comment" : "(LoDRankTable) The lod_rank_table of dynamic RNN.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "I",
+   "comment" : "(LoDTensor) The step index. The RNN step memory 'X' will be shrinked to match the size of the input of the index'th step.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Log operator",
+   "name" : "Out",
+   "comment" : "(LoDTensor) The shrinked RNN step memory.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "conv3d_cudnn",
- "comment" : "\nConvolution3D Operator.\n\nThe convolution operation calculates the output based on the input, filter\nand strides, paddings, dilations, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format, where N is batch\nsize, C is the number of channels,D is the depth of the feature, H is the height of\nthe feature, and W is the width of the feature.\nFilters(Input) is MCDHW format, where M is the number of output image channels,\nC is the number of input image channels, D is the depth of the filter,\nH is the height of the filter, and W is the width of the filter.\nParameters(strides, paddings, dilations) are three elements. These three elements\nrepresent depth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{out}, C_{in}, D_f, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out}= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\\\\n       H_{out}= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\\\\n       W_{out}= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1\n  $$\n",
+ "type" : "lod_reset",
+ "comment" : "LoDReset operator\n\nReset LoD of Input(X) into a new one specified by Input(TargetLoD) or\nAttr(target_lod), or set LoD for Input(X) if it doesn't have one.\nCurrently the lod_reset operator only supports the reset of level 0 LoD.\nAt least one of Input(TargetLoD) and Attr(target_lod) must be set,\nand if both of them are set, Input(TargetLoD) will be chosen as the\ntarget LoD.\n\nAn example:\nGiven a float LoDTensor X with shape (6, 1), its transpose form represents\n\n    [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],\n\nwith LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like\n\n    [1.0, 2.0], [3.0, 4.0, 5.0], [6.0].\n\nIf target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and\nthe sequences that the LoDTensor Output(Out) contains becomes:\n\n    [1.0, 2.0, 3.0, 4.0], [5.0, 6.0].\n\n",
  "inputs" : [ 
  { 
-   "name" : "Input",
-   "comment" : "(Tensor) The input tensor of convolution operator. The format of input tensor is NCDHW. Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature.",
+   "name" : "X",
+   "comment" : "(LoDTensor) The input tensor of lod_reset operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Filter",
-   "comment" : "(Tensor) The filter tensor of convolution operator. The format of the filter tensor is MCDHW, where M is the number of output image channels, C is the number of input image channels, D is the depth of the filter, H is the height of the filter, and W is the width of the filter.If the groups attribute is greater than 1, C equals the number of input image channels divided by the groups.",
+   "name" : "TargetLoD",
+   "comment" : "(Tensor, optional) The target level 0 LoD from Input().",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Output",
-   "comment" : "(Tensor) The output tensor of convolution operator.The format of output tensor is also NCDHW.",
+   "name" : "Out",
+   "comment" : "(LoDTensor) The output tensor of lod_reset operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "strides",
+   "name" : "target_lod",
    "type" : "int array",
-   "comment" : "(vector<int>, default:{1, 1, 1}), the strides(d_stride, h_stride, w_stride) of convolution operator.",
-   "generated" : 0
- }, { 
-   "name" : "paddings",
-   "type" : "int array",
-   "comment" : "(vector<int>, default:{0, 0, 0}), the paddings(d_pad, h_pad, w_pad) of convolution operator.",
-   "generated" : 0
- }, { 
-   "name" : "groups",
-   "type" : "int",
-   "comment" : "(int default:1), the groups number of the convolution operator. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels.",
-   "generated" : 0
- }, { 
-   "name" : "dilations",
-   "type" : "int array",
-   "comment" : "(vector<int> default:{1, 1, 1}), the dilations(d_dilation, h_dilation, w_dilation) of convolution operator.",
-   "generated" : 0
- }, { 
-   "name" : "workspace_size_MB",
-   "type" : "int",
-   "comment" : "workspace size for cudnn, in MB, workspace is a section of GPU memory which will be allocated/freed each time the operator runs, larger workspace size can increase performance but also requires better hardware. This size should be chosen carefully.",
+   "comment" : "The target level 0 LoD from Attr().",
    "generated" : 0
  } ] 
 },{
- "type" : "conv3d_transpose_cudnn",
- "comment" : "\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:   \n  Input:\n       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\\\\n       H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\\\\n       W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f\n  $$\n",
+ "type" : "write_to_array",
+ "comment" : "\nWriteToArray Operator.\n\nThis operator writes a LoDTensor to a LoDTensor array.\n\nAssume $T$ is LoDTensor, $i$ is the subscript of the array, and $A$ is the array. The\nequation is\n\n$$A[i] = T$$\n\n",
  "inputs" : [ 
  { 
-   "name" : "Input",
-   "comment" : "(Tensor) The input tensor of convolution transpose operator.The format of input tensor is NCDHW. Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature.",
+   "name" : "X",
+   "comment" : "(LoDTensor) the tensor will be written to tensor array",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Filter",
-   "comment" : "(Tensor) The filter tensor of convolution transpose operator.The format of the filter tensor is MCDHW, where M is the number of input feature channels, C is the number of output feature channels, D is the depth of the filter, H is the height of the filter, and W is the width of the filter.We enforce groups number == 1 and padding == 0 in the convolution3d transpose scenario.",
+   "name" : "I",
+   "comment" : "(Tensor) the subscript index in tensor array. The number of element should be 1",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Output",
-   "comment" : "(Tensor) The output tensor of convolution transpose operator.The format of output tensor is also NCDHW.Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature.",
+   "name" : "Out",
+   "comment" : "(TensorArray) the tensor array will be written",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
+ "attrs" : [  ] 
+},{
+ "type" : "logical_or",
+ "comment" : "logical_or Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = X || Y$$\n",
+ "inputs" : [ 
  { 
-   "name" : "strides",
-   "type" : "int array",
-   "comment" : "(vector<int> default:{1, 1, 1}), the strides{d_stride, h_stride, w_stride} of convolution transpose operator.",
-   "generated" : 0
- }, { 
-   "name" : "paddings",
-   "type" : "int array",
-   "comment" : "(vector<int> default:{0, 0, 0}), paddings(d_pad, h_pad, w_pad) of convolution transpose operator.",
-   "generated" : 0
- }, { 
-   "name" : "dilations",
-   "type" : "int array",
-   "comment" : "dilations of convolution operator.",
-   "generated" : 0
+   "name" : "X",
+   "comment" : "(LoDTensor) Left hand operand of logical_or operator",
+   "duplicable" : 0,
+   "intermediate" : 0
  }, { 
-   "name" : "workspace_size_MB",
-   "type" : "int",
-   "comment" : "workspace size for cudnn, in MB, workspace is a section of GPU memory which will be allocated/freed each time the operator runs, larger workspace size can increase performance but also requires better hardward. This size should be carefully setted.",
-   "generated" : 0
- } ] 
+   "name" : "Y",
+   "comment" : "(LoDTensor) Right hand operand of logical_or operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = X || Y$$",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
 },{
- "type" : "cross_entropy",
- "comment" : "\nCrossEntropy Operator.\n\nIt supports both standard cross-entropy and soft-label cross-entropy loss\ncomputation.\n1) One-hot cross-entropy:\n    soft_label = false, Label[i, 0] indicates the class index for sample i:\n\n                $Y[i] = -\\log(X[i, Label[i]])$\n\n2) Soft-label cross-entropy:\n    soft_label = true, Label[i, j] indicates the soft label of class j\n    for sample i:\n\n                $Y[i] = \\sum_j{-Label[i, j] * log(X[i, j])}$\n\n   Please make sure that in this case the summuation of each row of Label\n   equals one.\n\n3) One-hot cross-entropy with vecterized Input(Label):\n     As a special case of 2), when each row of Input(Label) has only one\n     non-zero element (equals 1), soft-label cross-entropy degenerates to a\n     one-hot cross-entropy with one-hot label representation.\n\nBoth the input X and Label can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n",
+ "type" : "seq_expand",
+ "comment" : "\nSeq Expand Operator.\n\nThis operator expands input(X) according to LOD of input(Y).\nFollowing are cases to better explain how this works:\nCase 1:\n\nGiven 2-level a LoDTensor input(X)\n    X.lod = [[0,       2, 3],\n             [0, 1,    3, 4]]\n    X.data = [a, b, c, d]\n    X.dims = [4, 1]\nand input(Y)\n    Y.lod = [[0,    2,    4],\n             [0, 3, 6, 7, 8]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 2-level LoDTensor\n    Out.lod = [[0,                2,    4],\n               [0,       3,       6, 7, 8]]\n    Out.data = [a, a, a, b, b, b, c, d]\n    Out.dims = [8, 1]\n\nCase 2:\n\nGiven a 0-level LoDTensor input(X)\n    X.data = [a, b, c]\n    X.lod = NULL\n    X.dims = [3, 1]\nand input(Y)\n    Y.lod = [[0, 2, 3, 6]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 1-level LoDTensor\n    Out.lod = [[0,    2, 3,      6]]\n    Out.data = [a, a, b, c, c, c]\n    Out.dims = [6, 1]\n\nCase 3:\n\nGiven a 0-level LoDTensor input(X)\n    X.data = [[a, b], [c, d], [e, f]]\n    X.lod = NULL\n    X.dims = [3, 2]\nand input(Y)\n    Y.lod = [[0, 2, 3, 6]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 1-level LoDTensor\n    Out.lod = [[0,           2,     3,                     6]]\n    Out.data = [[a,b], [a,b] [c,d], [e, f], [e, f], [e, f]]\n    Out.dims = [6, 2]\n\nCase 4:\n\nGiven 2-level a LoDTensor input(X)\n    X.lod = [[0,       2, 3],\n             [0, 1,    3, 4]]\n    X.data = [a, b, c, d]\n    X.dims = [4, 1]\nand input(Y)\n    Y.lod = [[0,    2,    4],\n             [0, 3, 6, 6, 8]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 2-level LoDTensor\n    Out.lod = [[0,                2,    4],\n               [0,       3,       6, 6, 8]]\n    Out.data = [a, a, a, b, b, b, d, d]\n    Out.dims = [8, 1]\n\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a probability computed by the previous operator, which is almost always the result of a softmax operator.",
+   "comment" : "(Tensor or LoDTensor) The input(X) of this operator can be a LoDTensor or a base Tensor.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "(Tensor), the ground truth which is a 2-D tensor. When soft_label is set to false, Label is a Tensor<int64> with shape [N x 1]. When soft_label is set to true, Label is a Tensor<float/double> with shape [N x K].",
+   "name" : "Y",
+   "comment" : "(LoDTensor)The reference input(Y) of seq_expand op.It must be a LoDTensor with k-level(k>0).The input(X) will be expanded according to LOD of input(Y).The element numbers of last level in input(Y) must be equal to dims[0] of input(X).",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape [N x 1]. The cross entropy loss.",
+   "name" : "Out",
+   "comment" : "(LodTensor)The output of seq_expand op.The lod of output will be as same as input(Y)'s lod.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "scale",
+ "comment" : "\nScale operator\n\n$$Out = scale*X$$\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(Tensor) Input tensor of scale operator.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(Tensor) Output tensor of scale operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "soft_label",
-   "type" : "bool",
-   "comment" : "(bool, default false), a flag indicating whether to interpretate the given labels as soft labels.",
+   "name" : "scale",
+   "type" : "float",
+   "comment" : "(float, default 0)The scaling factor of the scale operator.",
    "generated" : 0
  } ] 
 },{
- "type" : "matmul",
- "comment" : "\nMatMul Operator.\n\n\nThis operator is used to perform (batched) matrix multiplication\nover the last two dimensions of the input tensors `X` and `Y`.\n\nIf a transpose flag is specified, the last two dimensions of the\ntensor are transposed. If the tensor is rank-1 of shape [D], then\nfor `X` it is treated as [1, D] in nontransposed form and as [D, 1]\nin transposed form, whereas for `Y` it is the opposite: It is treated\nas [D, 1] in nontransposed form and as [1, D] in transposed form.\n\nExamples without transpose:\n- X: [K], Y: [K] => Out: [1]\n- X: [K], Y: [K, N] => Out: [N]\n- X: [B, M, K], Y: [K] => Out: [B, M]\n- X: [M, K], Y: [B, K, N] => Out: [B, M, N]\n- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]\n\nThe behavior is designed to be similar to the `numpy.matmul` function.\nThe differences are:\n- Currently only rank 1 to rank 3 input tensors are supported.\n- We add `transpose_X` and `transpose_Y` flags.\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",
+ "type" : "reduce_sum",
+ "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the sum of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The first input of MatMul op",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "The second input of MatMul op",
+   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of MatMul op",
+   "comment" : "(Tensor) The result tensor.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "transpose_X",
-   "type" : "bool",
-   "comment" : "If true, use the transpose of `X`.\n        ",
+   "name" : "dim",
+   "type" : "int",
+   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
    "generated" : 0
  }, { 
-   "name" : "transpose_Y",
+   "name" : "keep_dim",
    "type" : "bool",
-   "comment" : "If true, use the transpose of `Y`.\n        ",
+   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
    "generated" : 0
  } ] 
 },{
- "type" : "brelu",
- "comment" : "\nBRelu Activation Operator.\n\n$y = \\max(\\min(x, t_{min}), t_{max})$\n\n",
+ "type" : "stanh",
+ "comment" : "\nSTanh Activation Operator.\n\n$$y = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of BRelu operator",
+   "comment" : "Input of STanh operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Y",
-   "comment" : "Output of BRelu operator",
+   "comment" : "Output of STanh operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "t_min",
+   "name" : "scale_a",
    "type" : "float",
-   "comment" : "The min marginal value of BRelu",
+   "comment" : "The scale parameter of a for the input",
    "generated" : 0
  }, { 
-   "name" : "t_max",
+   "name" : "scale_b",
    "type" : "float",
-   "comment" : "The max marginal value of BRelu",
+   "comment" : "The scale parameter of b for the input",
    "generated" : 0
  } ] 
 },{
- "type" : "crf_decoding",
- "comment" : "\nThe crf_decoding operator reads the emission feature weights and the transition\nfeature weights learned by the linear_chain_crf operator. It implements the\nViterbi algorithm which is a dynamic programming algorithm for finding the most\nlikely sequence of hidden states, called the Viterbi path, that results in a\nsequence of observed tags.\n\nThe output of this operator changes according to whether Input(Label) is given:\n\n1. Input(Label) is given:\n\nThis happens in training. This operator is used to co-work with the chunk_eval\noperator.\n\nWhen Input(Label) is given, the crf_decoding operator returns a row vector\nwith shape [N x 1] whose values are fixed to be 0, indicating an incorrect\nprediction, or 1 indicating a tag is correctly predicted. Such an output is the\ninput to chunk_eval operator.\n\n2. Input(Label) is not given:\n\nThis is the standard decoding process.\n\nThe crf_decoding operator returns a row vector with shape [N x 1] whose values\nrange from 0 to maximum tag number - 1. Each element indicates an index of a\npredicted tag.\n",
+ "type" : "adamax",
+ "comment" : "\nAdamax Optimizer.\n\nWe implement the Adamax optimizer from Section 7 of the Adam\npaper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the\nAdam algorithm based on the infinity norm.\n\nAdamax updates:\n\n$$\nmoment\\_out = \\beta_1 * moment + (1 - \\beta_1) * grad \\\\\ninf\\_norm\\_out = max(\\beta_2 * inf\\_norm + \\epsilon, |grad|) \\\\\nlearning\\_rate = \\frac{learning\\_rate}{1 - \\beta_{1\\_pow}} \\\\\nparam\\_out = param - learning\\_rate * \\frac{moment\\_out}{inf\\_norm\\_out}\n$$\n\nThe original paper does not have an epsilon attribute.\nHowever, it is added here for numerical stability to prevent the\ndivision by 0 error.\n\n",
  "inputs" : [ 
  { 
-   "name" : "Emission",
-   "comment" : "(LoDTensor, default: LoDTensor<float>). A LoDTensor with shape [N x D] where N is the size of the mini-batch and D is the total tag number. This input is the unscaled emission weight matrix of the linear_chain_crf operator.",
+   "name" : "Param",
+   "comment" : "(Tensor) Input parameter",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Transition",
-   "comment" : "(Tensor, default: Tensor<float>). A Tensor with shape [(D + 2) x D]. This input is the transition weights learned by the linear_chain_crf operator, denoted as w. The 1st row of w are transition weights for the start mask. The 2nd row of w are transition weights for the end mask. Transition weights between other tags begin from the 3rd row of w. See more details in comments of the linear_chain_crf operator.",
+   "name" : "Grad",
+   "comment" : "(Tensor) Input gradient",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "(LoDTensor,  LoDTensor<int64_t>). The ground truth with shape [N x 1]. This input is optional. See more details in the operator's comments.",
+   "name" : "LearningRate",
+   "comment" : "(Tensor) Learning rate",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Moment",
+   "comment" : "(Tensor) First moment",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "InfNorm",
+   "comment" : "(Tensor) Input exponentially weighted infinity norm",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Beta1Pow",
+   "comment" : "(Tensor) Input beta1 power accumulator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "ViterbiPath",
-   "comment" : "(LoDTensor, LoDTensor<int64_t>). The decoding results. What to return changes depending on whether the Input(Label) (the ground truth) is given. See more details in the operator's comment.",
+   "name" : "ParamOut",
+   "comment" : "(Tensor) Output parameter",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "MomentOut",
+   "comment" : "(Tensor) Output first moment",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "InfNormOut",
+   "comment" : "(Tensor) Output exponentially weighted infinity norm",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "beta1",
+   "type" : "float",
+   "comment" : "(float, default 0.9) Exponential decay rate for the 1st moment estimates.",
+   "generated" : 0
+ }, { 
+   "name" : "beta2",
+   "type" : "float",
+   "comment" : "(float, default 0.999) exponential decay rate for the weighted infinity norm estimates.",
+   "generated" : 0
+ }, { 
+   "name" : "epsilon",
+   "type" : "float",
+   "comment" : "(float, default 1.0e-8) Constant for numerical stability",
+   "generated" : 0
+ } ] 
 },{
- "type" : "clip_by_norm",
- "comment" : "\nClipByNorm Operator.\n\nThis operator limits the L2 norm of the input $X$ within $max\\_norm$.\nIf the L2 norm of $X$ is less than or equal to $max\\_norm$, $Out$ will be\nthe same as $X$. If the L2 norm of $X$ is greater than $max\\_norm$, $X$ will\nbe linearly scaled to make the L2 norm of $Out$ equal to $max\\_norm$, as\nshown in the following formula:\n\n$$\nOut = \\frac{max\\_norm * X}{norm(X)},\n$$\n\nwhere $norm(X)$ represents the L2 norm of $X$.\n",
+ "type" : "tanh_shrink",
+ "comment" : "\nTanhShrink Activation Operator.\n\n$$y = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input of clip_by_norm op.The number of dimensions must be between [1, 9].",
+   "comment" : "Input of TanhShrink operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor) The output of clip_by_norm op with shape as input(X)",
+   "name" : "Y",
+   "comment" : "Output of TanhShrink operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "max_norm",
-   "type" : "float",
-   "comment" : "(float) The maximum norm value.",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "gather",
- "comment" : "\nGather Operator.\n\n$Out = X[Index]$\n\nOut is obtained by gathering entries of the outer-most dimension \nof X indexed by Index and concatenate them together.\n\nExample:\n\nX = [[1, 2],\n     [3, 4],\n     [5, 6]]\n\nIndex = [[1, 2]]\n\nThen:\n\nOut = [[3, 4],\n       [5, 6]]\n\n",
+ "type" : "mean",
+ "comment" : "\nMean Operator.\n\nOut is a scalar which is the mean of all elements in X. \n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The source input of gather op",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Index",
-   "comment" : "The index input of gather op",
+   "comment" : "The input of mean op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of gather op",
+   "comment" : "The output of mean op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "pool3d_cudnn",
- "comment" : "\nPool3d Operator.\n\nThe pooling3d operation calculates the output based on\nthe input, pooling_type, ksize, strides, and paddings parameters.\nInput(X) and output(Out) are in NCDHW format, where N is batch\nsize, C is the number of channels, and D, H and W are the depth, height and\nwidth of the feature, respectively. Parameters(ksize, strides, paddings) \nare three elements. These three elements represent depth, height and \nwidth, respectively. The input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       X shape: $(N, C, D_{in}, H_{in}, W_{in})$\n  Output:\n       Out shape: $(N, C, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out} = \\frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\\\\n       H_{out} = \\frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\\\\n       W_{out} = \\frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1\n  $$\n\n",
+ "type" : "reduce_mean",
+ "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the mean of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input tensor of pooling operator. The format of input tensor is NCDHW, where N is batch size, C is the number of channels, and D, H and W is the depth, height and width of the feature, respectively.",
+   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) The output tensor of pooling operator.The format of output tensor is also NCDHW, where N is batch size, C is the number of channels, and D, H and W is the depth, height and width of the feature, respectively.",
+   "comment" : "(Tensor) The result tensor.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "pooling_type",
-   "type" : "string",
-   "comment" : "(string) Pooling type, can be \"max\" for max-pooling and \"avg\" for average-pooling.",
+   "name" : "dim",
+   "type" : "int",
+   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
+   "generated" : 0
+ }, { 
+   "name" : "keep_dim",
+   "type" : "bool",
+   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
+   "generated" : 0
+ } ] 
+},{
+ "type" : "pool2d_cudnn",
+ "comment" : "\nPool2d Operator.\n\nThe pooling2d operation calculates the output based on\nthe input, pooling_type and ksize, strides, paddings parameters.\nInput(X) and output(Out) are in NCHW format, where N is batch size, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nParameters(ksize, strides, paddings) are two elements.\nThese two elements represent height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:   \n  Input:\n       X shape: $(N, C, H_{in}, W_{in})$\n  Output:\n       Out shape: $(N, C, H_{out}, W_{out})$\n  Where\n       $$ \n       H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\\\\n       W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1\n       $$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(Tensor) The input tensor of pooling operator. The format of input tensor is NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(Tensor) The output tensor of pooling operator. The format of output tensor is also NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [ 
+ { 
+   "name" : "pooling_type",
+   "type" : "string",
+   "comment" : "(string), pooling type, can be \"max\" for max-pooling and \"avg\" for average-pooling.",
    "generated" : 0
  }, { 
    "name" : "ksize",
    "type" : "int array",
-   "comment" : "(vector<int>) The pooling window size(depth, height, width) of pooling operator. If global_pooling = true, ksize and paddings will be ignored.",
+   "comment" : "(vector<int>) The pooling window size(height, width) of the pooling operator. If global_pooling = true, ksize and paddings will be ignored.",
    "generated" : 0
  }, { 
    "name" : "global_pooling",
    "type" : "bool",
-   "comment" : "(bool, default false) Whether to use the global pooling. If global_pooling = true, ksize and paddings wille be ignored.",
+   "comment" : "(bool, default false) Whether to use the global pooling. If global_pooling = true, ksize and paddings will be ignored.",
    "generated" : 0
  }, { 
    "name" : "strides",
    "type" : "int array",
-   "comment" : "(vector<int>, default {1,1,1}) Strides(depth, height, width) of the pooling operator.",
+   "comment" : "(vector<int>, default {1, 1}), strides(height, width) of pooling operator.",
    "generated" : 0
  }, { 
    "name" : "paddings",
    "type" : "int array",
-   "comment" : "(vector<int>, default {0,0,0}), paddings(depth, height, width) of pooling operator. If global_pooling = true, ksize and paddings will be ignored.",
+   "comment" : "(vector<int>, default {0,0}), paddings(height, width) of pooling operator.If global_pooling = true, paddings and ksize will be ignored.",
    "generated" : 0
  } ] 
 },{
- "type" : "crop",
- "comment" : "\nCrop Operator.\n\nCrop input into output, as specified by offsets and shape.\n\nThere are two ways to set shape:\n1. reference input: crop input X into the same shape as reference input.\n                    The dimension of reference input should\n                    be the same as the dimension of input X.\n2. shape list: crop input X into the shape described by a list<int>.\n               The size of shape list should be the same as\n               the dimension size of input X.\n\nThe input should be a k-D tensor(k > 0 and k < 7). As an example:\n\nGiven:\n\n    X = [[0, 1, 2, 0, 0]\n         [0, 3, 4, 0, 0]\n         [0, 0, 0, 0, 0]],\n\nand\n\n    offsets = [0, 1],\n\nand\n\n    shape = [2, 2],\n\nwe get:\n\n    Out = [[1, 2],\n           [3, 4]].\n\n",
+ "type" : "lod_tensor_to_array",
+ "comment" : "",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input of pad op. The input should be a k-D tensor(k > 0 and k < 7).",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "The input used as reference for cropping, which is of the same dimensions as X.",
+   "name" : "RankTable",
+   "comment" : "",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of crop op, which is of the same dimensions as X.",
+   "comment" : "",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "reshape",
+ "comment" : "\nReshape Operator.\n\nReshape Input(X) into the shape specified by Attr(shape).\n\nAn example:\nGiven a 2-D tensor X with 2 rows and 2 columns\n\n    [[1, 2], [3, 4]]\n\nand target shape = [1, 4], the reshape operator will transform\nthe tensor X into a 1-D tensor:\n\n    [1, 2, 3, 4]\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "The input tensor of reshape operator.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "The output tensor of reshape operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "offsets",
-   "type" : "int array",
-   "comment" : "A list<int> describing offsets to be cropped. The size of offsets list should be the same as the dimension size of input X.",
-   "generated" : 0
- }, { 
    "name" : "shape",
    "type" : "int array",
-   "comment" : "A list<int> describing the shape of output. The size of shape list should be the same as the dimension size of input X.",
+   "comment" : "(vector<int>) Target shape of reshape operator.",
    "generated" : 0
  } ] 
 },{
- "type" : "merge_lod_tensor",
- "comment" : "\n        Merge True and False branches of LoDTensor into a single Output,\n        with a mask at certain lod level. X is used to obtain complete\n        lod information. Please refer to SplitLoDTensorOp.",
+ "type" : "modified_huber_loss",
+ "comment" : "\nModified Huber Loss Operator.\n\nThis operator is used in binary classification problem. The shape of\ninput X and target Y are both [N, 1] and so is the shape of the output loss.\nSince target Y is not differentiable, calculating gradient for Y is illegal.\nThe formula of modified huber loss is:\n\n$$\nL(y, f(x)) = \n\\begin{cases}\n(\\max(0, 1 - yf(x)))^2,  \\text{if} \\  yf(x) >= -1    \\\\\n             -4yf(x),    \\quad \\text{otherwise}\n\\end{cases}\n$$\n\nMake sure the values of target label Y are in {0, 1} here. This operator will\nscale values of Y to {-1, +1} when computing losses and gradients.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input LoDTensor, contains complete lod information to construct the output",
+   "comment" : "The input tensor of modified huber loss op. X is 2-D tensor with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Mask",
-   "comment" : "A bool column vector which mask the input",
+   "name" : "Y",
+   "comment" : "The target labels of modified huber loss op. The shape of Y is the same as X. Values of Y must be 0 or 1.",
    "duplicable" : 0,
    "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "IntermediateVal",
+   "comment" : "Variable to save intermediate result which will be reused in backward processing.",
+   "duplicable" : 0,
+   "intermediate" : 1
  }, { 
-   "name" : "InTrue",
-   "comment" : "The True branch to be merged",
+   "name" : "Out",
+   "comment" : "Classification loss for X.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "elementwise_sub",
+ "comment" : "\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n$Out = X - Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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" : "(Tensor) The first input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "InFalse",
-   "comment" : "The False branch to be merged",
+   "name" : "Y",
+   "comment" : "(Tensor) The second input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The merged output LoDTensor",
+   "comment" : "The output of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "level",
+   "name" : "axis",
    "type" : "int",
-   "comment" : "(int) the specific lod level to rank.",
+   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
    "generated" : 0
  } ] 
 },{
- "type" : "elementwise_mul",
- "comment" : "\nLimited Elementwise Mul Operator.\n\nThe equation is:\n\n$Out = X \\odot\\ Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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",
+ "type" : "logical_and",
+ "comment" : "logical_and Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = X \\&\\& Y$$\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The first input tensor of elementwise op",
+   "comment" : "(LoDTensor) Left hand operand of logical_and operator",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "(Tensor) The second input tensor of elementwise op",
+   "comment" : "(LoDTensor) Right hand operand of logical_and operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of elementwise op",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = X \\&\\& Y$$",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "logical_not",
+ "comment" : "logical_not Operator\n\nIt operates element-wise on X, and returns the Out. X and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = !X$$\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(LoDTensor) Operand of logical_not operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = !X$$",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "abs",
+ "comment" : "\nAbs Activation Operator.\n\n$y = |x|$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "Input of Abs operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Y",
+   "comment" : "Output of Abs operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "square",
+ "comment" : "\nSquare Activation Operator.\n\n$y = x^2$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "Input of Square operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Y",
+   "comment" : "Output of Square operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "increment",
+ "comment" : "\nIncrement Operator.\n\nThe equation is: \n$$Out = X + step$$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "(Tensor) The input tensor of increment operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(Tensor) The output tensor of increment operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "axis",
-   "type" : "int",
-   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
+   "name" : "step",
+   "type" : "float",
+   "comment" : "(float, default 1.0) The step size by which the input tensor will be incremented.",
    "generated" : 0
  } ] 
 },{
@@ -3041,1295 +3113,1414 @@
    "generated" : 0
  } ] 
 },{
- "type" : "proximal_gd",
- "comment" : "\nProximalGD Operator.\n\nOptimizer that implements the proximal gradient descent algorithm:\n\n$$\nprox\\_param = param - learning\\_rate * grad \\\\\nparam = sign(prox\\_param) / (1 + learning\\_rate * l2) *\n        \\max(|prox\\_param| - learning\\_rate * l1, 0)\n$$        \n\nThe paper that proposed Proximal Gradient Descent:\n(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)\n\n",
+ "type" : "elementwise_mul",
+ "comment" : "\nLimited Elementwise Mul Operator.\n\nThe equation is:\n\n$Out = X \\odot\\ Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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" : "Param",
-   "comment" : "(Tensor, default Tensor<float>) Input parameter value that has to be updated.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Grad",
-   "comment" : "(Tensor, default Tensor<float>) Input gradient of the parameter.",
+   "name" : "X",
+   "comment" : "(Tensor) The first input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "LearningRate",
-   "comment" : "(Tensor, default Tensor<float>) The learning rate should be a tensor of size 1.",
+   "name" : "Y",
+   "comment" : "(Tensor) The second input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "ParamOut",
-   "comment" : "(Tensor) Output updated parameter value.",
+   "name" : "Out",
+   "comment" : "The output of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "l1",
-   "type" : "float",
-   "comment" : "(float, default 0.0) L1 regularization strength.",
-   "generated" : 0
- }, { 
-   "name" : "l2",
-   "type" : "float",
-   "comment" : "(float, default 0.0) L2 regularization strength.",
+   "name" : "axis",
+   "type" : "int",
+   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
    "generated" : 0
  } ] 
 },{
- "type" : "positive_negative_pair",
- "comment" : "\n        PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) \n        model performance. \n        Within some context, e.g. the \"query\", a LTR model generates scores\n        for a list of items, which gives a partial order of the items.\n        PositiveNegativePairOp takes a list of reference rank order \n        (Input(\"Label\")) and the model generated scores (Input(Score)) as \n        inputs and counts the pairs that ranked correctly and incorrectly.\n",
+ "type" : "huber_loss",
+ "comment" : "\nHuberLoss Operator.\n\nHuber loss is a loss function used in robust regression. We define X as the\ninput value and Y as the target value. Huber loss can evaluate the fitness of\nX to Y. Different from MSE loss, Huber loss is more robust for outliers. The\nshape of X and Y are [batch_size, 1]. The equation is:\n\n$$\nOut_{\\delta}(X, Y)_i =\n\\begin{cases}\n0.5 * (Y_i - X_i)^2,\n\\quad |Y_i - X_i| \\leq \\delta \\\\\n\\delta * (|Y_i - X_i| - 0.5 * \\delta),\n\\quad otherwise\n\\end{cases}\n$$\n\nIn the above equation, $Out_\\delta(X, Y)_i$, $X_i$ and $Y_i$ represent the ith\nelement of Out, X and Y.\n\n",
  "inputs" : [ 
  { 
-   "name" : "Score",
-   "comment" : "(Tensor, float) Model Score on an item (with respect to QueryID). It's a 2-D tensor with shape [batch_size, depth], where the column specified by the attribute \"column\" is used as item score.",
+   "name" : "X",
+   "comment" : "The input value of huber loss op.X is a 2-D tensor with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "(Tensor, float) Label of an item (with repsect to QueryId). It's a 2-D tensor with shape [batch_size, 1].",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "QueryID",
-   "comment" : "(Tensor, int64) Query ID that indicates the context. Its shape should be the same as Label.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "AccumulatePositivePair",
-   "comment" : "(float) Optional. The accumulated number of positive pairs over a stream of data. If provided, the output PositivePair will be initialized with this number rather than 0. it won't be modified in place.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "AccumulateNegativePair",
-   "comment" : "(float) Optional. The accumulated number of negative pairs over a stream of data. If provided, the output NegativePair will be initialized with this number rather than 0. it won't be modified in place.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "AccumulateNeutralPair",
-   "comment" : "(float) Optional. The accumulated number of neutral pairs over a stream of data. If provided, the output NeutralPair will be initialized with this number rather than 0. it won't be modified in place.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Weight",
-   "comment" : "(float) Optional. Weight of current item. If specified, its shape should be the same as Label, and the meaning of the output changes from numbers of pairs to the total sum of pairs' weights. Weight of a pair of items is the average of their weights.",
+   "name" : "Y",
+   "comment" : "The target value of huber loss op.Y is a 2-D tensor with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "PositivePair",
-   "comment" : "(float) Number of positive pairs, i.e. the pairs of items that are ranked correctly.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "NegativePair",
-   "comment" : "(float) Number of negative pairs, i.e. the pairs of items that are ranked incorrectly.",
+   "name" : "Residual",
+   "comment" : "Intermediate tensor to cache residual value between Y and X.The shape is same as Input(X) and will be reused in backward.",
    "duplicable" : 0,
-   "intermediate" : 0
+   "intermediate" : 1
  }, { 
-   "name" : "NeutralPair",
-   "comment" : "(float) Number of neutral pairs, i.e. the pairs of items that have the same score.",
+   "name" : "Out",
+   "comment" : "The output tensor with shape [batch_size, 1] which represents the huber loss.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "column",
-   "type" : "int",
-   "comment" : "(int, default -1) The column position of Score used to rank items in descending order. It must be in the range of [-rank(Score), rank(Score)). If `dim < 0`, the dim to reduce is `rank + dim`. Noting that reducing on the first dim will make the LoD info lost.",
+   "name" : "delta",
+   "type" : "float",
+   "comment" : "Hyper parameter in huber loss.",
    "generated" : 0
  } ] 
 },{
- "type" : "log_loss",
- "comment" : "\nLogLoss Operator.\n\nLog loss is a loss function used for binary classification. Log Loss quantifies\nthe accuracy of a classifier by penalising false classifications. Minimising the\nLog Loss is equivalent to maximising the accuracy of the classifier. We define\nPredicted as the values predicted by our model and Labels as the target ground\ntruth value. Log loss can evaluate how close the predicted values are to the\ntarget. The shapes of Predicted and Labels are both [batch_size, 1].\nThe equation is:\n\n$$\nLoss = - Labels * log(Predicted + \\epsilon) -\n        (1 - Labels) * log(1 - Predicted + \\epsilon)\n$$\n\n",
+ "type" : "sequence_slice",
+ "comment" : "\nSequence slice operator\n\nThe operator crops a subsequence from given sequence with given start offset and subsequence length.\nIt only supports sequence (LoD Tensor with level number is 1).\n- Case:\n    X = [[a1, a2;\n        b1, b2;\n        c1, c2]\n       [d1, d2;\n        e1, e2]]\n    LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)\n    Offset = [[0], [1]]; Length = [[2], [1]]\n\n    Out = [[a1, a2;\n            b1, b2]\n            [e1, e2]]\n    LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)\nNOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.\n    ",
  "inputs" : [ 
  { 
-   "name" : "Predicted",
-   "comment" : "The input value (Predicted) of Log loss op.Predicted is a 2-D tensor with shape [batch_size, 1].",
+   "name" : "X",
+   "comment" : "(LoDTensor), the input of SequenceSliceOp.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Labels",
-   "comment" : "The target value (Labels) of Log loss op.Labels is a 2-D tensor with shape [batch_size, 1].",
+   "name" : "Offset",
+   "comment" : "(Tensor), a vector<int> to describe the offset of every input sequence for sub sequence item.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Length",
+   "comment" : "(Tensor), a vector<int> to describe the length of every input sequence for sub sequence item.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Loss",
-   "comment" : "The output tensor with shape [batch_size, 1] which represents the log loss.",
+   "name" : "Out",
+   "comment" : "(LoDTensor), the output of SequenceSliceOp.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "epsilon",
-   "type" : "float",
-   "comment" : "Epsilon in log loss.",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "mean",
- "comment" : "\nMean Operator.\n\nOut is a scalar which is the mean of all elements in X. \n\n",
+ "type" : "hinge_loss",
+ "comment" : "\nHingeLoss Operator.\n\nLet x be a logit (prediction) and y be the actual label. The logit can\ntake any values from (-inf, inf), but the labels should be either -1 or 1.\nThen, the hinge loss is computed as follows:\n\n$$\nL_(x, y) = max(1 - y.x, 0) \n$$\n\nNote that the labels passed as input will have values as either 0 or 1.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input of mean op",
+   "name" : "Logits",
+   "comment" : "The input value (Logits) of Hinge loss op.Logits is a 2-D tensor with shape [batch_size, 1].",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Labels",
+   "comment" : "The target value (Labels) of Hinge loss op.Labels is a 2-D tensor with shape [batch_size, 1].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of mean op",
+   "name" : "Loss",
+   "comment" : "The output tensor with shape [batch_size, 1] which represents the hinge loss.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "elementwise_add",
- "comment" : "\nLimited Elementwise Add Operator.\n\nThe equation is:\n\n$Out = X + Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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",
+ "type" : "less_than",
+ "comment" : "less_than Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X < Y\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The first input tensor of elementwise op",
+   "comment" : "(LoDTensor) the left hand operand of less_than operator",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "(Tensor) The second input tensor of elementwise op",
+   "comment" : "(LoDTensor) the right hand operand of less_than operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output of elementwise op",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X < Y",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "axis",
-   "type" : "int",
-   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "fill_zeros_like",
- "comment" : "\nFillZerosLike Operator.\n\nFill up a variable with zeros.\nThe output will have the same size as the input.\n\n",
+ "type" : "gru_unit",
+ "comment" : "\nGRUUnit Operator implements partial calculations of the GRU unit as following:\n\n$$\nupdate \\ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\\\\nreset \\ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r)  \\\\\noutput \\ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\\\\noutput: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)\n$$\n\nwhich is same as one time step of GRU Operator.\n\n@note To implement the complete GRU unit, fully-connected operator must be \nused before to feed xu, xr and xc as the Input of GRUUnit operator.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input of fill-zeros-like op.",
+   "name" : "Input",
+   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the input.",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Y",
-   "comment" : "The variable will be filled up with zeros.",
+ }, { 
+   "name" : "HiddenPrev",
+   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size] for the states of previous time step.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Weight",
+   "comment" : "(Tensor) Weight matrix with shape [frame_size, frame_size * 3]. The elements continuous in memory can be divided into two parts. The first part are weights of the update gate and reset gate with shape [frame_size, frame_size * 2], and the second part are weights of output candidate with shape [frame_size, frame_size].",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Bias",
+   "comment" : "(Tensor) Bias vector with shape [1, frame_size * 3] concatenating bias of the update gate, reset gate and output candidate.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
-},{
- "type" : "prelu",
- "comment" : "\nPRelu Operator.\n\nThe equation is:\n\n$$\nf(x) =\n\\begin{cases}\n\\alpha * x, \\quad  \\text{if} \\ x < 0 \\\\\nx,         \\qquad  \\text{if} \\ x >= 0\n\\end{cases}\n$$\n\nThe input `X` can carry the LoD (Level of Details) information,\nor not. And the output shares the LoD information with input `X`.\n\n",
- "inputs" : [ 
+ "outputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input tensor of prelu operator.",
+   "name" : "Gate",
+   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the output of update gate, reset gate and output candidate.",
    "duplicable" : 0,
-   "intermediate" : 0
+   "intermediate" : 1
  }, { 
-   "name" : "Alpha",
-   "comment" : "The alpha weight of prelu operator.",
+   "name" : "ResetHiddenPrev",
+   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size] for the reseted hidden state of previous time step.",
+   "duplicable" : 0,
+   "intermediate" : 1
+ }, { 
+   "name" : "Hidden",
+   "comment" : "(Tensor) The GRU hidden state of the current time step with shape [batch_size, frame_size].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
+ "attrs" : [ 
+ { 
+   "name" : "activation",
+   "type" : "int",
+   "comment" : "(enum int, default tanh) The activation type used for output candidate {h}_t.",
+   "generated" : 0
+ }, { 
+   "name" : "gate_activation",
+   "type" : "int",
+   "comment" : "(enum int, default sigmoid) The activation type used in update gate and reset gate.",
+   "generated" : 0
+ } ] 
+},{
+ "type" : "gaussian_random",
+ "comment" : "\nGaussianRandom Operator.\n\nUsed to initialize tensors with gaussian random generator.\n\n",
+ "inputs" : [  ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "The output tensor of prelu operator.",
+   "comment" : "Output matrix of gaussian random op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "shape",
+   "type" : "int array",
+   "comment" : "(vector<int>) The dimension of random tensor.",
+   "generated" : 0
+ }, { 
+   "name" : "mean",
+   "type" : "float",
+   "comment" : "(float, default 0.0) mean of random tensor.",
+   "generated" : 0
+ }, { 
+   "name" : "std",
+   "type" : "float",
+   "comment" : "(float, default 1.0) std of random tensor.",
+   "generated" : 0
+ }, { 
+   "name" : "seed",
+   "type" : "int",
+   "comment" : "(int, default 0) Random seed of generator.0 means use system wide seed.",
+   "generated" : 0
+ }, { 
+   "name" : "dtype",
+   "type" : "int",
+   "comment" : "(int, default 5(FP32)) Output data type.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "fill",
- "comment" : "Fill operator\n\nFill an tensor with `value` and `shape`. The type of the tensor is specify by\n`dtype`.\n",
+ "type" : "fill_constant",
+ "comment" : "\nFillConstantBatchSizeLike Operator.\n\nFill up a variable with specified constant value.\n\n",
  "inputs" : [  ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) The output tensor.",
+   "comment" : "(Tensor) Tensor of specified shape will be filled with the specified value",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "value",
-   "type" : "float array",
-   "comment" : "The float values of tensor, which are flatten in row major",
+   "name" : "dtype",
+   "type" : "int",
+   "comment" : "(int, default 5 (FP32)) Output data type",
    "generated" : 0
  }, { 
    "name" : "shape",
    "type" : "int array",
-   "comment" : "The shape of output tensor",
+   "comment" : "(vector<int>) The shape of the output",
    "generated" : 0
  }, { 
-   "name" : "dtype",
-   "type" : "int",
-   "comment" : "The data type of output tensor, Default is float",
+   "name" : "value",
+   "type" : "float",
+   "comment" : "(float, default 0) The value to be filled",
    "generated" : 0
  }, { 
    "name" : "force_cpu",
    "type" : "bool",
-   "comment" : "Whether the output tensor must be at CPU memory or not. Default is false.",
+   "comment" : "(bool, default false) Force fill output variable to cpu memory. Otherwise, fill output variable to the running device",
    "generated" : 0
  } ] 
 },{
- "type" : "sigmoid_cross_entropy_with_logits",
- "comment" : "\nSigmoidCrossEntropyWithLogits Operator.\n\nThis measures the element-wise probability error in classification tasks\nin which each class is independent. This can be thought of as predicting labels\nfor a data-point, where labels are not mutually exclusive.\nFor example, a news article can be about politics, technology or sports\nat the same time or none of these.\n\nThe logistic loss is given as follows:\n\n       $$loss = -Labels * \\log(\\sigma(X)) - (1 - Labels) * \\log(1 - \\sigma(X))$$\n\nWe know that $$\\sigma(X) = (1 / (1 + \\exp(-X)))$$. By substituting this we get:\n\n       $$loss = X - X * Labels + \\log(1 + \\exp(-X))$$\n\nFor stability and to prevent overflow of $$\\exp(-X)$$ when X < 0,\nwe reformulate the loss as follows:\n\n       $$loss = \\max(X, 0) - X * Labels + \\log(1 + \\exp(-|X|))$$\n\nBoth the input `X` and `Labels` can carry the LoD (Level of Details) information.\nHowever the output only shares the LoD with input `X`.\n\n",
+ "type" : "fill_zeros_like",
+ "comment" : "\nFillZerosLike Operator.\n\nFill up a variable with zeros.\nThe output will have the same size as the input.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a tensor of logits computed by the previous  operator. Logits are unscaled log probabilities given as log(p/(1-p)).",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Label",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor of the same type and shape as X. This input is a tensor of probabalistic labels for each logit",
+   "comment" : "The input of fill-zeros-like op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D  of elementwise logistic losses.",
+   "name" : "Y",
+   "comment" : "The variable will be filled up with zeros.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "modified_huber_loss",
- "comment" : "\nModified Huber Loss Operator.\n\nThis operator is used in binary classification problem. The shape of\ninput X and target Y are both [N, 1] and so is the shape of the output loss.\nSince target Y is not differentiable, calculating gradient for Y is illegal.\nThe formula of modified huber loss is:\n\n$$\nL(y, f(x)) = \n\\begin{cases}\n(\\max(0, 1 - yf(x)))^2,  \\text{if} \\  yf(x) >= -1    \\\\\n             -4yf(x),    \\quad \\text{otherwise}\n\\end{cases}\n$$\n\nMake sure the values of target label Y are in {0, 1} here. This operator will\nscale values of Y to {-1, +1} when computing losses and gradients.\n\n",
+ "type" : "softmax_with_cross_entropy",
+ "comment" : "\nSoftmax With Cross Entropy Operator.\n\nCross entropy loss with softmax is used as the output layer extensively. This\noperator computes the softmax normalized values for each row of the input\ntensor, after which cross-entropy loss is computed. This provides a more\nnumerically stable gradient.\n\nBecause this operator performs a softmax on logits internally, it expects\nunscaled logits. This operator should not be used with the output of\nsoftmax operator since that would produce incorrect results.\n\nWhen the attribute soft_label is set false, this operators expects mutually\nexclusive hard labels, each sample in a batch is in exactly one class with a\nprobability of 1.0. Each sample in the batch will have a single label.\n\nThe equation is as follows:\n\n1) Hard label (one-hot label, so every sample has exactly one class)\n\n$$Loss_j =  -\\text{Logit}_{Label_j} +\n\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{Logit}_i)\\right),\nj = 1,..., K$$\n\n2) Soft label (each sample can have a distribution over all classes)\n\n$$Loss_j =  -\\sum_{i=0}^{K}\\text{Label}_i \\left(\\text{Logit}_i -\n\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{Logit}_i)\\right)\\right),\nj = 1,...,K$$\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input tensor of modified huber loss op. X is 2-D tensor with shape [batch_size, 1].",
+   "name" : "Logits",
+   "comment" : "(Tensor, default: Tensor<float>), The unscaled log probabilities which is a 2-D tensor with shape [N x K]. N is the batch_size, and K is the class number.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "The target labels of modified huber loss op. The shape of Y is the same as X. Values of Y must be 0 or 1.",
+   "name" : "Label",
+   "comment" : "(Tensor) The ground truth which is a 2-D tensor. If soft_label is set to false, Label is a Tensor<int64> with shape [N x 1]. If soft_label is set to true, Label is a Tensor<float/double> with shape [N x K].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "IntermediateVal",
-   "comment" : "Variable to save intermediate result which will be reused in backward processing.",
+   "name" : "Softmax",
+   "comment" : "(Tensor, default: Tensor<float>), A 2-D tensor with shape [N x K]. The outputs value of softmax activation by given the input batch, which will be used in backward calculation.",
    "duplicable" : 0,
    "intermediate" : 1
  }, { 
-   "name" : "Out",
-   "comment" : "Classification loss for X.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "elementwise_sub",
- "comment" : "\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n$Out = X - Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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" : "(Tensor) The first input tensor of elementwise op",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "(Tensor) The second input tensor of elementwise op",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "The output of elementwise op",
+   "name" : "Loss",
+   "comment" : "(Tensor, default: Tensor<float>), A 2-D tensor. The cross entropy loss with shape [N x 1].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "axis",
-   "type" : "int",
-   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
+   "name" : "soft_label",
+   "type" : "bool",
+   "comment" : "(bool, default: false), A flag to indicate whether to interpretate the given labels as soft labels.",
    "generated" : 0
  } ] 
 },{
- "type" : "reduce_mean",
- "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the mean of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
+ "type" : "fill_constant_batch_size_like",
+ "comment" : "\nFillConstantBatchSizeLike Operator.\n\nFill up a variable with specified constant value.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
+   "name" : "Input",
+   "comment" : "(Tensor) Tensor whose dim_idx th dimension is used to specify the batch_size",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) The result tensor.",
+   "comment" : "(Tensor) Tensor of specified shape will be filled with the specified value",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "dim",
+   "name" : "dtype",
    "type" : "int",
-   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
+   "comment" : "(int, default 5 (FP32)) Output data type",
    "generated" : 0
  }, { 
-   "name" : "keep_dim",
-   "type" : "bool",
-   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
+   "name" : "shape",
+   "type" : "int array",
+   "comment" : "(vector<int>) The shape of the output",
+   "generated" : 0
+ }, { 
+   "name" : "input_dim_idx",
+   "type" : "int",
+   "comment" : "(int, default 0) The index of input's batch size dimension",
+   "generated" : 0
+ }, { 
+   "name" : "output_dim_idx",
+   "type" : "int",
+   "comment" : "(int, default 0) The index of output's batch size dimension",
+   "generated" : 0
+ }, { 
+   "name" : "value",
+   "type" : "float",
+   "comment" : "(float, default 0) The value to be filled",
    "generated" : 0
  } ] 
 },{
- "type" : "square",
- "comment" : "\nSquare Activation Operator.\n\n$y = x^2$\n\n",
+ "type" : "tanh",
+ "comment" : "\nTanh Activation Operator.\n\n$$y = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Square operator",
+   "comment" : "Input of Tanh operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Y",
-   "comment" : "Output of Square operator",
+   "comment" : "Output of Tanh operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "reduce_max",
- "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the max of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n",
+ "type" : "feed",
+ "comment" : "\nFeed Operator.\n\nIt should not be configured by users directly.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input tensor. Tensors with rank at most 6 are supported.",
+   "comment" : "The input of feed op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) The result tensor.",
+   "comment" : "The output of feed op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "dim",
+   "name" : "col",
    "type" : "int",
-   "comment" : "(int, default 0) The dimension to reduce. Must be in the range [-rank(input), rank(input)). If `dim < 0`, the dim to reduce is `rank + dim`. Note that reducing on the first dim will make the LoD info lost.",
-   "generated" : 0
- }, { 
-   "name" : "keep_dim",
-   "type" : "bool",
-   "comment" : "(bool, default false) If true, retain the reduced dimension with length 1.",
+   "comment" : "(int) The column of feed",
    "generated" : 0
  } ] 
 },{
- "type" : "logical_or",
- "comment" : "logical_or Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = X || Y$$\n",
+ "type" : "expand",
+ "comment" : "\nExpand operator tiles the input by given times number. You should set times\nnumber for each dimension by providing attribute 'expand_times'. The rank of X\nshould be in [1, 6]. Please notice that size of 'expand_times' must be same with\nX's rank. Following is a using case:\n\nInput(X) is a 3-D tensor with shape [2, 3, 1]:\n\n        [\n           [[1], [2], [3]],\n           [[4], [5], [6]]\n        ]\n\nAttr(expand_times):  [1, 2, 2]\n\nOutput(Out) is a 3-D tensor with shape [2, 6, 2]:\n\n        [\n            [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],\n            [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]\n        ]\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) Left hand operand of logical_or operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "(LoDTensor) Right hand operand of logical_or operator",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank in [1, 6].X is the input tensor to be expanded.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = X || Y$$",
+   "comment" : "(Tensor, default Tensor<float>) A tensor with rank in [1, 6].The rank of Output(Out) is same as Input(X) except that each dimension size of Output(Out) is equal to corresponding dimension size of Input(X) multiplying corresponding value of Attr(expand_times).",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "expand_times",
+   "type" : "int array",
+   "comment" : "Expand times number for each dimension.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "less_than",
- "comment" : "less_than Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X < Y\n",
+ "type" : "elementwise_div",
+ "comment" : "\nLimited Elementwise Div Operator.\n\nThe equation is:\n\n$Out = X / Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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" : "(LoDTensor) the left hand operand of less_than operator",
+   "comment" : "(Tensor) The first input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "(LoDTensor) the right hand operand of less_than operator",
+   "comment" : "(Tensor) The second input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X < Y",
+   "comment" : "The output of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "axis",
+   "type" : "int",
+   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
+   "generated" : 0
+ } ] 
 },{
- "type" : "gru_unit",
- "comment" : "\nGRUUnit Operator implements partial calculations of the GRU unit as following:\n\n$$\nupdate \\ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\\\\nreset \\ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r)  \\\\\noutput \\ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\\\\noutput: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)\n$$\n\nwhich is same as one time step of GRU Operator.\n\n@note To implement the complete GRU unit, fully-connected operator must be \nused before to feed xu, xr and xc as the Input of GRUUnit operator.\n\n",
+ "type" : "elementwise_add",
+ "comment" : "\nLimited Elementwise Add Operator.\n\nThe equation is:\n\n$Out = X + Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n  shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\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" : "Input",
-   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the input.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "HiddenPrev",
-   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size] for the states of previous time step.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Weight",
-   "comment" : "(Tensor) Weight matrix with shape [frame_size, frame_size * 3]. The elements continuous in memory can be divided into two parts. The first part are weights of the update gate and reset gate with shape [frame_size, frame_size * 2], and the second part are weights of output candidate with shape [frame_size, frame_size].",
+   "name" : "X",
+   "comment" : "(Tensor) The first input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Bias",
-   "comment" : "(Tensor) Bias vector with shape [1, frame_size * 3] concatenating bias of the update gate, reset gate and output candidate.",
+   "name" : "Y",
+   "comment" : "(Tensor) The second input tensor of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Gate",
-   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the output of update gate, reset gate and output candidate.",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
-   "name" : "ResetHiddenPrev",
-   "comment" : "(Tensor) Matrix with shape [batch_size, frame_size] for the reseted hidden state of previous time step.",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
-   "name" : "Hidden",
-   "comment" : "(Tensor) The GRU hidden state of the current time step with shape [batch_size, frame_size].",
+   "name" : "Out",
+   "comment" : "The output of elementwise op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "activation",
-   "type" : "int",
-   "comment" : "(enum int, default tanh) The activation type used for output candidate {h}_t.",
-   "generated" : 0
- }, { 
-   "name" : "gate_activation",
+   "name" : "axis",
    "type" : "int",
-   "comment" : "(enum int, default sigmoid) The activation type used in update gate and reset gate.",
+   "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X",
    "generated" : 0
  } ] 
 },{
- "type" : "swish",
- "comment" : "\nSwish Activation Operator.\n\n$$y = \\frac{x}{1 + e^{- \\beta x}}$$\n\n",
+ "type" : "cross_entropy",
+ "comment" : "\nCrossEntropy Operator.\n\nIt supports both standard cross-entropy and soft-label cross-entropy loss\ncomputation.\n1) One-hot cross-entropy:\n    soft_label = false, Label[i, 0] indicates the class index for sample i:\n\n                $Y[i] = -\\log(X[i, Label[i]])$\n\n2) Soft-label cross-entropy:\n    soft_label = true, Label[i, j] indicates the soft label of class j\n    for sample i:\n\n                $Y[i] = \\sum_j{-Label[i, j] * log(X[i, j])}$\n\n   Please make sure that in this case the summuation of each row of Label\n   equals one.\n\n3) One-hot cross-entropy with vecterized Input(Label):\n     As a special case of 2), when each row of Input(Label) has only one\n     non-zero element (equals 1), soft-label cross-entropy degenerates to a\n     one-hot cross-entropy with one-hot label representation.\n\nBoth the input X and Label 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" : "Input of Swish operator",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a probability computed by the previous operator, which is almost always the result of a softmax operator.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Label",
+   "comment" : "(Tensor), the ground truth which is a 2-D tensor. When soft_label is set to false, Label is a Tensor<int64> with shape [N x 1]. When soft_label is set to true, Label is a Tensor<float/double> with shape [N x K].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Y",
-   "comment" : "Output of Swish operator",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape [N x 1]. The cross entropy loss.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "beta",
-   "type" : "float",
-   "comment" : "Constant beta of swish operator",
+   "name" : "soft_label",
+   "type" : "bool",
+   "comment" : "(bool, default false), a flag indicating whether to interpretate the given labels as soft labels.",
    "generated" : 0
  } ] 
 },{
- "type" : "is_empty",
- "comment" : "\nIsEmpty Operator which checks whether a tensor is empty.\n\nIt will just return product(tensor.ddims()) > 0;\n              ",
+ "type" : "matmul",
+ "comment" : "\nMatMul Operator.\n\n\nThis operator is used to perform (batched) matrix multiplication\nover the last two dimensions of the input tensors `X` and `Y`.\n\nIf a transpose flag is specified, the last two dimensions of the\ntensor are transposed. If the tensor is rank-1 of shape [D], then\nfor `X` it is treated as [1, D] in nontransposed form and as [D, 1]\nin transposed form, whereas for `Y` it is the opposite: It is treated\nas [D, 1] in nontransposed form and as [1, D] in transposed form.\n\nExamples without transpose:\n- X: [K], Y: [K] => Out: [1]\n- X: [K], Y: [K, N] => Out: [N]\n- X: [B, M, K], Y: [K] => Out: [B, M]\n- X: [M, K], Y: [B, K, N] => Out: [B, M, N]\n- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]\n\nThe behavior is designed to be similar to the `numpy.matmul` function.\nThe differences are:\n- Currently only rank 1 to rank 3 input tensors are supported.\n- We add `transpose_X` and `transpose_Y` flags.\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" : "(Tensor) Tensor which is to be checked.",
+   "comment" : "The first input of MatMul op",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Y",
+   "comment" : "The second input of MatMul op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor) a boolean Tensor that indicate empty or not.",
+   "comment" : "The output of MatMul op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "transpose_X",
+   "type" : "bool",
+   "comment" : "If true, use the transpose of `X`.\n        ",
+   "generated" : 0
+ }, { 
+   "name" : "transpose_Y",
+   "type" : "bool",
+   "comment" : "If true, use the transpose of `Y`.\n        ",
+   "generated" : 0
+ } ] 
 },{
- "type" : "sequence_concat",
- "comment" : "\nThe sequence_concat operator concatenates multiple LoDTensors. \nIt only supports sequence (LoD Tensor with level number is 1) \nor a nested sequence (LoD tensor with level number is 2) as its input.\n- Case1:\n  If the axis is other than 0(here, axis is 1 and level is 1),\n  each input should have the same LoD information and the LoD \n  information of the output keeps the same as the input.\n\n  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)\n  LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)\n\n- Case2:\n  If the axis is 0(here, leve is 0), the inputs are concatenated along \n  time steps, the LoD information of the output need to re-compute.\n  The LoD information of level-1 should be same.\n\n  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n  LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\n- Case3:\n  If the axis is 0(here, level is 1).\n\n  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n  LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)\n\n- Case4:\n  If the LoD number is 1, axis is 0, level is 0\n\n  LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)\n  LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\nNOTE: The levels of all the inputs should be the same.\n    ",
+ "type" : "dropout",
+ "comment" : "\nDropout Operator.\n\nDropout refers to randomly dropping out units in a nerual network. It is a\nregularization technique for reducing overfitting by preventing neuron\nco-adaption during training. The dropout operator randomly set (according to\nthe given dropout probability) the outputs of some units to zero, while others\nare set equal to their corresponding inputs.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LodTensorArray) Input is a vector of LoDTensor, each of which is a variable-length sequence or nested sequence.",
-   "duplicable" : 1,
+   "comment" : "The input of dropout op.",
+   "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor), Variable-length output of sequence_concat Op.",
+   "comment" : "The output of dropout op.",
    "duplicable" : 0,
    "intermediate" : 0
+ }, { 
+   "name" : "Mask",
+   "comment" : "The random sampled dropout mask.",
+   "duplicable" : 0,
+   "intermediate" : 1
  } ], 
  "attrs" : [ 
  { 
-   "name" : "axis",
-   "type" : "int",
-   "comment" : "(int, default 0) The axis along which the inputs will be joined. If axis is 0, the inputs will be joined with LoD index.",
+   "name" : "dropout_prob",
+   "type" : "float",
+   "comment" : "Probability of setting units to zero.",
    "generated" : 0
  }, { 
-   "name" : "level",
+   "name" : "is_test",
+   "type" : "bool",
+   "comment" : "True if in test phase.",
+   "generated" : 0
+ }, { 
+   "name" : "seed",
    "type" : "int",
-   "comment" : "(int, default 0) The level at which the inputs will be joined. If the level is 0, the inputs will be joined at the nested sequence level. If the level is 1, the inputs will be joined at the sequence level. The level should be less than the level number of inputs.",
+   "comment" : "Dropout random seed.",
    "generated" : 0
  } ] 
 },{
- "type" : "floor",
- "comment" : "\nFloor Activation Operator.\n\n$y = floor(x)$\n\n",
+ "type" : "fetch",
+ "comment" : "\nFetch Operator.\n\nIt should not be configured by users directly.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Floor operator",
+   "comment" : "The input of fetch op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Floor operator",
+   "name" : "Out",
+   "comment" : "The output of fetch op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "col",
+   "type" : "int",
+   "comment" : "(int) The column of fetch",
+   "generated" : 0
+ } ] 
 },{
- "type" : "cast",
- "comment" : "\nCast Operator.\n\nThis Operator casts the input tensor to another data type and\nreturns tha Output Tensor.\n\n",
+ "type" : "squared_l2_distance",
+ "comment" : "\nSquaredL2Distance operator\n\nThis operator will cacluate the squared L2 distance for the input and \nthe target. Number of distance value will be equal to the first dimension \nof input. First dimension of the target could be equal to the input or to 1. \nIf the first dimension of target is 1, the operator will broadcast target's \nfirst dimension to input's first dimension. During backward propagation, \nthe user can decide whether to calculate the gradient of the input or \nthe target or both.\n\nBoth the input X and Y can carry the LoD (Level of Details) information. \nHowever, the output only shares the LoD information with input X.\n    ",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input tensor of cast op",
+   "comment" : "(Tensor) Input of SquaredL2DistanceOp.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Y",
+   "comment" : "(Tensor) Target of SquaredL2DistanceOp.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
+   "name" : "sub_result",
+   "comment" : "(Tensor) Buffering subtraction result which will be reused in backward.",
+   "duplicable" : 0,
+   "intermediate" : 1
+ }, { 
    "name" : "Out",
-   "comment" : "The output tensor of cast op",
+   "comment" : "(Tensor) Squared l2 distance between input and target.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "out_dtype",
-   "type" : "int",
-   "comment" : "output data type",
-   "generated" : 0
- }, { 
-   "name" : "in_dtype",
-   "type" : "int",
-   "comment" : "input data type",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "ceil",
- "comment" : "\nCeil Activation Operator.\n\n$y = ceil(x)$\n\n",
+ "type" : "while",
+ "comment" : "\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Ceil operator",
-   "duplicable" : 0,
+   "comment" : "A set of variables, which are required by operators inside the block of While Op.",
+   "duplicable" : 1,
+   "intermediate" : 0
+ }, { 
+   "name" : "Condition",
+   "comment" : "(Bool) An scalar. When it's False, the While Op will be terminated.",
+   "duplicable" : 1,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Ceil operator",
+   "name" : "Out",
+   "comment" : "A set of variables, which will be assigned with values generated by the operators inside the block of While Op.",
+   "duplicable" : 1,
+   "intermediate" : 0
+ }, { 
+   "name" : "StepScopes",
+   "comment" : "(StepScopeVar) A vector of local scope, which size equals the step number of While Op. The i'th scope storages temporary variables generated in the i'th step.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "step_block",
+   "type" : "block id",
+   "comment" : "The step block inside WhileOp",
+   "generated" : 0
+ } ] 
 },{
- "type" : "tanh",
- "comment" : "\nTanh Activation Operator.\n\n$$y = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\n\n",
+ "type" : "relu",
+ "comment" : "\nRelu Activation Operator.\n\n$y = \\max(x, 0)$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Tanh operator",
+   "comment" : "Input of Relu operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Y",
-   "comment" : "Output of Tanh operator",
+   "comment" : "Output of Relu operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "feed",
- "comment" : "\nFeed Operator.\n\nIt should not be configured by users directly.\n\n",
+ "type" : "decayed_adagrad",
+ "comment" : "\nDecayed Adagrad Optimizer.\n\nThe update is done as follows:\n\n$$\nmoment\\_out = decay * moment + (1 - decay) * grad * grad \\\\\nparam\\_out = param - \\frac{learning\\_rate * grad}{\\sqrt{moment\\_out} + epsilon}\n$$\n\nThe original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\ndoes not have an epsilon attribute. It is added here for numerical\nstability to avoid the division by zero error.\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The input of feed op",
+   "name" : "Param",
+   "comment" : "(Tensor) Input parameter",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Grad",
+   "comment" : "(Tensor) Input gradient",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Moment",
+   "comment" : "(Tensor) Second moment",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "LearningRate",
+   "comment" : "(Tensor) Learning rate",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of feed op",
+   "name" : "ParamOut",
+   "comment" : "(Tensor) Output parameter",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "MomentOut",
+   "comment" : "(Tensor) Output second moment",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "col",
-   "type" : "int",
-   "comment" : "(int) The column of feed",
+   "name" : "decay",
+   "type" : "float",
+   "comment" : "(float, default 0.95) Discounting factor for coming gradient",
+   "generated" : 0
+ }, { 
+   "name" : "epsilon",
+   "type" : "float",
+   "comment" : "(float, default 1.0e-6) Constant for numerical stability",
    "generated" : 0
  } ] 
 },{
- "type" : "rnn_memory_helper",
- "comment" : "",
+ "type" : "beam_search",
+ "comment" : "This is a beam search operator that help to generate sequences.",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "",
+   "name" : "pre_ids",
+   "comment" : "ids in previous step",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "ids",
+   "comment" : "a LoDTensor of shape of [None,k]",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "scores",
+   "comment" : "a LoDTensor that has the same shape and LoD with `ids`",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "",
+   "name" : "selected_ids",
+   "comment" : "a LoDTensor that stores the IDs selected by beam search",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "selected_scores",
+   "comment" : "a LoDTensor that has the same shape and LoD with `selected_ids`",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "dtype",
+   "name" : "level",
    "type" : "int",
-   "comment" : "(int, default 5 (FP32)) Output data type",
+   "comment" : "the level of LoDTensor",
+   "generated" : 0
+ }, { 
+   "name" : "beam_size",
+   "type" : "int",
+   "comment" : "beam size for beam search",
+   "generated" : 0
+ }, { 
+   "name" : "end_id",
+   "type" : "int",
+   "comment" : "the token id which indicates the end of a sequence",
    "generated" : 0
  } ] 
 },{
- "type" : "unpool",
- "comment" : "\n        \"Input shape: $(N, C_{in}, H_{in}, W_{in})$\n        Output shape: $(N, C_{out}, H_{out}, W_{out})$\n        Where\n          $$\n            H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\\\\n            W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1]\n          $$\n        Paper: http://www.matthewzeiler.com/wp-content/uploads/2017\n        /07/iccv2011.pdf\n        ",
+ "type" : "split_lod_tensor",
+ "comment" : "\n        Split a LoDTensor with a Mask at certain level. The input LoDTensor\n        has 3 sequence at certain lod level. The Mask is a bool column vector,\n        such as [0, 1, 0] at the same level. The first and third sequence will\n        be send to False Output LoDTensor; whereas the second sequence will\n        be send to True Output LoDTensor. Please refer to MergeLoDTensorOp.",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) The input tensor of unpool operator. The format of input tensor is NCHW. Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
+   "comment" : "The input LoDTensor",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Indices",
-   "comment" : "(Tensor) The input tensor of the indices given out by MaxPool2d. The format of input tensor is NCHW. Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
+   "name" : "Mask",
+   "comment" : "A bool column vector which mask the input",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor) The output tensor of unpool operator.The format of output tensor is also NCHW.Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
+   "name" : "OutTrue",
+   "comment" : "True branch of input LoDTensor",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "OutFalse",
+   "comment" : "False branch of input LoDTensor",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "ksize",
-   "type" : "int array",
-   "comment" : "(vector), the unpooling window size(height, width) of unpooling operator.",
-   "generated" : 0
- }, { 
-   "name" : "strides",
-   "type" : "int array",
-   "comment" : "(vector, default:{1, 1}), strides (height, width) of unpooling operator.",
-   "generated" : 0
- }, { 
-   "name" : "paddings",
-   "type" : "int array",
-   "comment" : "(vector defalut:{0,0}), paddings (height, width) of unpooling operator.",
-   "generated" : 0
- }, { 
-   "name" : "unpooling_type",
-   "type" : "string",
-   "comment" : "(string), unpooling type, can be \"max\" for max-unpooling ",
+   "name" : "level",
+   "type" : "int",
+   "comment" : "(int) the specific lod level to split.",
    "generated" : 0
  } ] 
 },{
- "type" : "transpose",
- "comment" : "\nTranspose Operator.\n\nThe input tensor will be permuted according to the axis values given.\nThe op functions similar to how numpy.transpose works in python.\nFor example:\n >> input = numpy.arange(6).reshape((2,3))\n >> input\n array([[0, 1, 2],\n        [3, 4, 5]])\n >> axis = [1, 0]\n >> output = input.transpose(axis)\n >> output\n array([[0, 3],\n        [1, 4],\n\t\t[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",
+ "type" : "greater_equal",
+ "comment" : "greater_equal Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X >= Y\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor)The input tensor, tensors with rank at most 6 are supported",
+   "comment" : "(LoDTensor) the left hand operand of greater_equal operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Y",
+   "comment" : "(LoDTensor) the right hand operand of greater_equal operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor)The output tensor",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X >= Y",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "axis",
-   "type" : "int array",
-   "comment" : "(vector<int>)A list of values, and the size of the list should be the same with the input tensor rank, the tensor will permute the axes according the the values given",
-   "generated" : 0
- } ] 
+ "attrs" : [  ] 
 },{
- "type" : "rnn_memory_helper_grad",
- "comment" : "",
+ "type" : "crop",
+ "comment" : "\nCrop Operator.\n\nCrop input into output, as specified by offsets and shape.\n\nThere are two ways to set shape:\n1. reference input: crop input X into the same shape as reference input.\n                    The dimension of reference input should\n                    be the same as the dimension of input X.\n2. shape list: crop input X into the shape described by a list<int>.\n               The size of shape list should be the same as\n               the dimension size of input X.\n\nThe input should be a k-D tensor(k > 0 and k < 7). As an example:\n\nGiven:\n\n    X = [[0, 1, 2, 0, 0]\n         [0, 3, 4, 0, 0]\n         [0, 0, 0, 0, 0]],\n\nand\n\n    offsets = [0, 1],\n\nand\n\n    shape = [2, 2],\n\nwe get:\n\n    Out = [[1, 2],\n           [3, 4]].\n\n",
  "inputs" : [ 
  { 
-   "name" : "Out@GRAD",
-   "comment" : "",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
    "name" : "X",
-   "comment" : "",
+   "comment" : "The input of pad op. The input should be a k-D tensor(k > 0 and k < 7).",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Out",
-   "comment" : "",
+   "name" : "Y",
+   "comment" : "The input used as reference for cropping, which is of the same dimensions as X.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "X@GRAD",
-   "comment" : "",
+   "name" : "Out",
+   "comment" : "The output of crop op, which is of the same dimensions as X.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "dtype",
-   "type" : "int",
-   "comment" : "(int, default 5 (FP32)) Output data type",
+   "name" : "offsets",
+   "type" : "int array",
+   "comment" : "A list<int> describing offsets to be cropped. The size of offsets list should be the same as the dimension size of input X.",
+   "generated" : 0
+ }, { 
+   "name" : "shape",
+   "type" : "int array",
+   "comment" : "A list<int> describing the shape of output. The size of shape list should be the same as the dimension size of input X.",
    "generated" : 0
  } ] 
 },{
- "type" : "momentum",
- "comment" : "\nMomentum Optimizer.\n\nThis optimizer has a flag for Nestrov Momentum.\nThe update equations are as follows:\n\n$$\nvelocity = mu * velocity + gradient \\\\\nif (use\\_nesterov):   \\\\\n  param = param - gradient * learning\\_rate + mu * velocity * learning\\_rate \\\\\nelse:   \\\\\n  param = param - learning\\_rate * velocity. \\\\\n$$\n\n",
+ "type" : "brelu",
+ "comment" : "\nBRelu Activation Operator.\n\n$y = \\max(\\min(x, t_{min}), t_{max})$\n\n",
  "inputs" : [ 
  { 
-   "name" : "Param",
-   "comment" : "(Tensor, default Tensor<float>) Input parameter that has to be updated",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Grad",
-   "comment" : "(Tensor, default Tensor<float>) Input gradient of the parameter",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Velocity",
-   "comment" : "(Tensor, default Tensor<float>) Input velocity (corresponding to the parameter) that has to be updated",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "LearningRate",
-   "comment" : "(Tensor, default Tensor<float>) Input learning rate",
+   "name" : "X",
+   "comment" : "Input of BRelu operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "ParamOut",
-   "comment" : "(Tensor) This output is updated parameter. It shared memory with Input(Param).",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "VelocityOut",
-   "comment" : "(Tensor) This output is updated velocity. It shared memory with Input(Velocity).",
+   "name" : "Y",
+   "comment" : "Output of BRelu operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "mu",
+   "name" : "t_min",
    "type" : "float",
-   "comment" : "(float) Momentum coefficient",
+   "comment" : "The min marginal value of BRelu",
    "generated" : 0
  }, { 
-   "name" : "use_nesterov",
-   "type" : "bool",
-   "comment" : "(bool, default false) Use Nesterov Momentum",
+   "name" : "t_max",
+   "type" : "float",
+   "comment" : "The max marginal value of BRelu",
    "generated" : 0
  } ] 
 },{
- "type" : "scatter",
- "comment" : "\nScatter Operator.\n\nThis operator obtains output by updating the input on selected indices on the first axis:\n\n$$\nOut = Ref \\\\\nOut[Index] = Ref[Index] + Updates\n$$\n\n",
+ "type" : "crf_decoding",
+ "comment" : "\nThe crf_decoding operator reads the emission feature weights and the transition\nfeature weights learned by the linear_chain_crf operator. It implements the\nViterbi algorithm which is a dynamic programming algorithm for finding the most\nlikely sequence of hidden states, called the Viterbi path, that results in a\nsequence of observed tags.\n\nThe output of this operator changes according to whether Input(Label) is given:\n\n1. Input(Label) is given:\n\nThis happens in training. This operator is used to co-work with the chunk_eval\noperator.\n\nWhen Input(Label) is given, the crf_decoding operator returns a row vector\nwith shape [N x 1] whose values are fixed to be 0, indicating an incorrect\nprediction, or 1 indicating a tag is correctly predicted. Such an output is the\ninput to chunk_eval operator.\n\n2. Input(Label) is not given:\n\nThis is the standard decoding process.\n\nThe crf_decoding operator returns a row vector with shape [N x 1] whose values\nrange from 0 to maximum tag number - 1. Each element indicates an index of a\npredicted tag.\n",
  "inputs" : [ 
  { 
-   "name" : "Ref",
-   "comment" : "The source input of scatter op",
+   "name" : "Emission",
+   "comment" : "(LoDTensor, default: LoDTensor<float>). A LoDTensor with shape [N x D] where N is the size of the mini-batch and D is the total tag number. This input is the unscaled emission weight matrix of the linear_chain_crf operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Index",
-   "comment" : "The index input of scatter op where Ref will be updated",
+   "name" : "Transition",
+   "comment" : "(Tensor, default: Tensor<float>). A Tensor with shape [(D + 2) x D]. This input is the transition weights learned by the linear_chain_crf operator, denoted as w. The 1st row of w are transition weights for the start mask. The 2nd row of w are transition weights for the end mask. Transition weights between other tags begin from the 3rd row of w. See more details in comments of the linear_chain_crf operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Updates",
-   "comment" : "The updated value of updates op",
+   "name" : "Label",
+   "comment" : "(LoDTensor,  LoDTensor<int64_t>). The ground truth with shape [N x 1]. This input is optional. See more details in the operator's comments.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of add op",
+   "name" : "ViterbiPath",
+   "comment" : "(LoDTensor, LoDTensor<int64_t>). The decoding results. What to return changes depending on whether the Input(Label) (the ground truth) is given. See more details in the operator's comment.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "less_equal",
- "comment" : "less_equal Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X <= Y\n",
+ "type" : "cos_sim",
+ "comment" : "\nCosine Similarity Operator.\n\n$Out = X^T * Y / (\\sqrt{X^T * X} * \\sqrt{Y^T * Y})$\n\nThe input X and Y must have the same shape, except that the 1st dimension\nof input Y could be just 1 (different from input X), which will be\nbroadcasted to match the shape of input X before computing their cosine\nsimilarity.\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" : "(LoDTensor) the left hand operand of less_equal operator",
+   "comment" : "The 1st input of cos_sim op.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "(LoDTensor) the right hand operand of less_equal operator",
+   "comment" : "The 2nd input of cos_sim op.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X <= Y",
+   "comment" : "The output of cos_sim op.",
    "duplicable" : 0,
    "intermediate" : 0
+ }, { 
+   "name" : "XNorm",
+   "comment" : "Norm of the first input, reduced along the 1st dimension.",
+   "duplicable" : 0,
+   "intermediate" : 1
+ }, { 
+   "name" : "YNorm",
+   "comment" : "Norm of the second input, reduced along the 1st dimension.",
+   "duplicable" : 0,
+   "intermediate" : 1
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "rank_loss",
- "comment" : "\nRankLoss Operator.\n\nRankLoss operator for RankNet\n(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf). \nRankNet is a pairwise ranking model with\none training sample consisting of a pair of doc A and B, and the label P\nindicating that A is ranked higher than B or not:\n\nP = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of\nthe input pair.\n\nThe RankLoss operator takes three inputs: Left (o_i), Right (o_j) and Label\n(P_{i,j}), which represent the output score of RankNet for the two docs and \nthe label respectively, and yields the rank loss C_{i,j} using the following \nequation:\n\n$$\n  C_{i,j} = -\\tilde{P_{ij}} * o_{i,j} + \\log(1 + e^{o_{i,j}}) \\\\\n  o_{i,j} =  o_i - o_j  \\\\\n  \\tilde{P_{i,j}} = \\left \\{0, 0.5, 1 \\right \\} \\ or \\ \\left \\{0, 1 \\right \\}\n$$\n\nThe operator can take batch inputs with size batch_size (batch_size >= 1).\n\n",
+ "type" : "conv3d_transpose_cudnn",
+ "comment" : "\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:   \n  Input:\n       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\\\\n       H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\\\\n       W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f\n  $$\n",
  "inputs" : [ 
  { 
-   "name" : "Label",
-   "comment" : "(2-D Tensor with shape [batch_size x 1]) The label indicating A ranked higher than B or not.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Left",
-   "comment" : "(2-D Tensor with shape [batch_size x 1]) The output of RankNet for doc A.",
+   "name" : "Input",
+   "comment" : "(Tensor) The input tensor of convolution transpose operator.The format of input tensor is NCDHW. Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Right",
-   "comment" : "(2-D Tensor with shape [batch_size x 1]) The output of RankNet for doc B.",
+   "name" : "Filter",
+   "comment" : "(Tensor) The filter tensor of convolution transpose operator.The format of the filter tensor is MCDHW, where M is the number of input feature channels, C is the number of output feature channels, D is the depth of the filter, H is the height of the filter, and W is the width of the filter.We enforce groups number == 1 and padding == 0 in the convolution3d transpose scenario.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(2-D Tensor with shape [batch_size x 1]) The output loss of RankLoss operator.",
+   "name" : "Output",
+   "comment" : "(Tensor) The output tensor of convolution transpose operator.The format of output tensor is also NCDHW.Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "strides",
+   "type" : "int array",
+   "comment" : "(vector<int> default:{1, 1, 1}), the strides{d_stride, h_stride, w_stride} of convolution transpose operator.",
+   "generated" : 0
+ }, { 
+   "name" : "paddings",
+   "type" : "int array",
+   "comment" : "(vector<int> default:{0, 0, 0}), paddings(d_pad, h_pad, w_pad) of convolution transpose operator.",
+   "generated" : 0
+ }, { 
+   "name" : "dilations",
+   "type" : "int array",
+   "comment" : "dilations of convolution operator.",
+   "generated" : 0
+ }, { 
+   "name" : "workspace_size_MB",
+   "type" : "int",
+   "comment" : "workspace size for cudnn, in MB, workspace is a section of GPU memory which will be allocated/freed each time the operator runs, larger workspace size can increase performance but also requires better hardward. This size should be carefully setted.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "greater_than",
- "comment" : "greater_than Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X > Y\n",
+ "type" : "conv_shift",
+ "comment" : "\nConvShift Operator.\n\nA layer for circular convolution of two vectors,\nas used in the Neural Turing Machine: https://arxiv.org/abs/1410.5401\n\nThe equation is:\n\n$$Out[i] = \\sum_{j=-(N-1)/2}^{(N-1)/2} X_{i+j} * Y_{j}$$\n\nwhere X's index is computed modulo M, and Y's index is computed modulo N.\n\nBoth inputs X and Y can carry LoD (Level of Details) information.\nHowever, the output only shares the LoD information with input X.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) the left hand operand of greater_than operator",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape B x M, where B is the batch size and M is the data dimension.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "(LoDTensor) the right hand operand of greater_than operator",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape B x N, where B is the batch size and N is the data dimension. N must be odd.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X > Y",
+   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape B x M, i.e., the same shape as X.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "equal",
- "comment" : "equal Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X == Y\n",
+ "type" : "conv2d_cudnn",
+ "comment" : "\nConvolution Operator.\n\nThe convolution operation calculates the output based on the input, filter\nand strides, paddings, dilations, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and Output(Output) are in NCHW format. Where N is batch\nsize, C is the number of channels, H is the height of the feature, and W is\nthe width of the feature.\nFilters(Input) is MCHW format. Where M is the number of output image channels, C is\nthe number of input image channels, H is the height of the filter, and W\nis the width of the filter.\nParameters(strides, paddings, dilations) are two elements. These two elements represent\nheight and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       Input shape: $(N, C_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{out}, C_{in}, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, H_{out}, W_{out})$\n  Where\n$$\n       H_{out}= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\\\\n       W_{out}= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1\n$$\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(LoDTensor) the left hand operand of equal operator",
+   "name" : "Input",
+   "comment" : "(Tensor) The input tensor of convolution operator. The format of input tensor is NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "(LoDTensor) the right hand operand of equal operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X == Y",
+   "name" : "Filter",
+   "comment" : "(Tensor) The filter tensor of convolution operator. The format of the filter tensor is MCHW, where M is the number of output image channels, C is the number of input image channels, H is the height of the filter, and W is the width of the filter. If the groups attribute is greater than 1, C equals the number of input image channels divided by the groups.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
-},{
- "type" : "uniform_random",
- "comment" : "\nUniform random operator.\n\nThis operator initializes a tensor with random values sampled from a \nuniform distribution.\n\n",
- "inputs" : [  ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor) The output tensor of uniform random op",
+   "name" : "Output",
+   "comment" : "(Tensor) The output tensor of convolution operator. The format of output tensor is also NCHW.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "shape",
+   "name" : "strides",
    "type" : "int array",
-   "comment" : "(vector<int>) The shape of the output tensor",
+   "comment" : "(vector<int> default:{1, 1}), the strides(h_stride, w_stride) of convolution operator.",
    "generated" : 0
  }, { 
-   "name" : "min",
-   "type" : "float",
-   "comment" : "(float, default -1.0) Minimum value of uniform random",
+   "name" : "paddings",
+   "type" : "int array",
+   "comment" : "(vector<int> default:{0, 0}), the paddings(h_pad, w_pad) of convolution operator.",
    "generated" : 0
  }, { 
-   "name" : "max",
-   "type" : "float",
-   "comment" : "(float, default 1.0) Maximun value of uniform random",
+   "name" : "groups",
+   "type" : "int",
+   "comment" : "(int default:1), the groups number of the convolution operator. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels.",
    "generated" : 0
  }, { 
-   "name" : "seed",
-   "type" : "int",
-   "comment" : "(int, default 0) Random seed used for generating samples. 0 means use a seed generated by the system.",
+   "name" : "dilations",
+   "type" : "int array",
+   "comment" : "(vector<int> default:{1, 1}), the dilations(h_dilation, w_dilation) of convolution operator.",
    "generated" : 0
  }, { 
-   "name" : "dtype",
+   "name" : "workspace_size_MB",
    "type" : "int",
-   "comment" : "(int, default 5(FP32)) Output tensor data type",
+   "comment" : "workspace size for cudnn, in MB, workspace is a section of GPU memory which will be allocated/freed each time the operator runs, larger workspace size can increase performance but also requires better hardware. This size should be chosen carefully.",
    "generated" : 0
  } ] 
 },{
- "type" : "roi_pool",
- "comment" : "\nROIPool operator\n\nROI Pooling for Faster-RCNN. The link below is a further introduction: \nhttps://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn\n    ",
+ "type" : "conditional_block",
+ "comment" : "Conditional block operator\n\nRun the sub-block if X is not empty. Params is the other inputs and Out is the\noutputs of the sub-block.\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor), the input of ROIPoolOp. The format of input tensor is NCHW. Where N is batch size, C is the number of input channels, H is the height of the feature, and W is the width of the feature.",
-   "duplicable" : 0,
+   "comment" : "The conditional variable of this operator. If X is empty, the whole sub-block will not be executed.",
+   "duplicable" : 1,
    "intermediate" : 0
  }, { 
-   "name" : "ROIs",
-   "comment" : "(Tensor), ROIs (Regions of Interest) to pool over. should be a 2-D tensor of shape (num_rois, 5)given as [[batch_id, x1, y1, x2, y2], …]. Where batch_id is the id of the data, (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates.",
-   "duplicable" : 0,
+   "name" : "Params",
+   "comment" : "The input variables of the sub-block.",
+   "duplicable" : 1,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(Tensor), The output of ROIPoolOp is a 4-D tensor with shape (num_rois, channels, pooled_h, pooled_w).",
-   "duplicable" : 0,
+   "comment" : "The output variables of the sub-block.",
+   "duplicable" : 1,
    "intermediate" : 0
  }, { 
-   "name" : "Argmax",
-   "comment" : "(Tensor), Argmaxes corresponding to indices in X used for gradient computation. Only output if arg “is_test” is false.",
+   "name" : "Scope",
+   "comment" : "(std::vector<Scope*>) The step scope of conditional block. To unify the conditional block, rnn and while op, the type of scope is std::vector<Scope*>",
    "duplicable" : 0,
-   "intermediate" : 1
+   "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "spatial_scale",
-   "type" : "float",
-   "comment" : "(float, default 1.0), Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling.",
-   "generated" : 0
- }, { 
-   "name" : "pooled_height",
-   "type" : "int",
-   "comment" : "(int, default 1), The pooled output height.",
-   "generated" : 0
- }, { 
-   "name" : "pooled_width",
-   "type" : "int",
-   "comment" : "(int, default 1), The pooled output width.",
+   "name" : "block",
+   "type" : "block id",
+   "comment" : "The step block of conditional block operator",
    "generated" : 0
  } ] 
 },{
- "type" : "softmax",
- "comment" : "\nSoftmax Operator.\n\nThe input of the softmax operator is a 2-D tensor with shape N x K (N is the\nbatch_size, K is the dimension of input feature). The output tensor has the\nsame shape as the input tensor.\n\nFor each row of the input tensor, the softmax operator squashes the\nK-dimensional vector of arbitrary real values to a K-dimensional vector of real\nvalues in the range [0, 1] that add up to 1.\nIt computes the exponential of the given dimension and the sum of exponential\nvalues of all the other dimensions in the K-dimensional vector input.\nThen the ratio of the exponential of the given dimension and the sum of\nexponential values of all the other dimensions is the output of the softmax\noperator.\n\nFor each row $i$ and each column $j$ in Input(X), we have:\n    $$Y[i, j] = \\frac{\\exp(X[i, j])}{\\sum_j(exp(X[i, j])}$$\n\n",
+ "type" : "sum",
+ "comment" : "\nSum operator.\n\nThis operators sums the input tensors. All the inputs can carry the \nLoD (Level of Details) information. However, the output only shares \nthe LoD information with the first input.\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input tensor of softmax. 2-D with shape [batch_size, input_feature_dimensions].",
-   "duplicable" : 0,
+   "comment" : "(vector<Tensor>) The input tensors of sum operator.",
+   "duplicable" : 1,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "The normalized values with the same shape as X.",
+   "name" : "Out",
+   "comment" : "(Tensor) The output tensor of sum operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "seq_expand",
- "comment" : "\nSeq Expand Operator.\n\nThis operator expands input(X) according to LOD of input(Y).\nFollowing are cases to better explain how this works:\nCase 1:\n\nGiven 2-level a LoDTensor input(X)\n    X.lod = [[0,       2, 3],\n             [0, 1,    3, 4]]\n    X.data = [a, b, c, d]\n    X.dims = [4, 1]\nand input(Y)\n    Y.lod = [[0,    2,    4],\n             [0, 3, 6, 7, 8]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 2-level LoDTensor\n    Out.lod = [[0,                2,    4],\n               [0,       3,       6, 7, 8]]\n    Out.data = [a, a, a, b, b, b, c, d]\n    Out.dims = [8, 1]\n\nCase 2:\n\nGiven a 0-level LoDTensor input(X)\n    X.data = [a, b, c]\n    X.lod = NULL\n    X.dims = [3, 1]\nand input(Y)\n    Y.lod = [[0, 2, 3, 6]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 1-level LoDTensor\n    Out.lod = [[0,    2, 3,      6]]\n    Out.data = [a, a, b, c, c, c]\n    Out.dims = [6, 1]\n\nCase 3:\n\nGiven a 0-level LoDTensor input(X)\n    X.data = [[a, b], [c, d], [e, f]]\n    X.lod = NULL\n    X.dims = [3, 2]\nand input(Y)\n    Y.lod = [[0, 2, 3, 6]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 1-level LoDTensor\n    Out.lod = [[0,           2,     3,                     6]]\n    Out.data = [[a,b], [a,b] [c,d], [e, f], [e, f], [e, f]]\n    Out.dims = [6, 2]\n\nCase 4:\n\nGiven 2-level a LoDTensor input(X)\n    X.lod = [[0,       2, 3],\n             [0, 1,    3, 4]]\n    X.data = [a, b, c, d]\n    X.dims = [4, 1]\nand input(Y)\n    Y.lod = [[0,    2,    4],\n             [0, 3, 6, 6, 8]]\nwith condition len(Y.lod[-1]) -1 == X.dims[0]\nthen we get 2-level LoDTensor\n    Out.lod = [[0,                2,    4],\n               [0,       3,       6, 6, 8]]\n    Out.data = [a, a, a, b, b, b, d, d]\n    Out.dims = [8, 1]\n\n\n",
+ "type" : "concat",
+ "comment" : "\nConcat Operator.\n\nConcatenate the input tensors along dimension axis.\nExamples:\n  Input[0] = [[1,2],[3,4]]\n  Input[1] = [[5,6]]\n  axis = 0\n  Output = [[1,2],\n            [3,4],\n            [5,6]]\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor or LoDTensor) The input(X) of this operator can be a LoDTensor or a base Tensor.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "(LoDTensor)The reference input(Y) of seq_expand op.It must be a LoDTensor with k-level(k>0).The input(X) will be expanded according to LOD of input(Y).The element numbers of last level in input(Y) must be equal to dims[0] of input(X).",
-   "duplicable" : 0,
+   "comment" : "Input tensors of concat operator.",
+   "duplicable" : 1,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LodTensor)The output of seq_expand op.The lod of output will be as same as input(Y)'s lod.",
+   "comment" : "Output tensor of concat operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "axis",
+   "type" : "int",
+   "comment" : "The axis along which the input tensors will be concatenated.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "sqrt",
- "comment" : "\nSqrt Activation Operator.\n\n$y = \\sqrt{x}$\n\n",
+ "type" : "less_equal",
+ "comment" : "less_equal Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X <= Y\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Sqrt operator",
+   "comment" : "(LoDTensor) the left hand operand of less_equal operator",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Y",
+   "comment" : "(LoDTensor) the right hand operand of less_equal operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Sqrt operator",
+   "name" : "Out",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X <= Y",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "logical_and",
- "comment" : "logical_and Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = X \\&\\& Y$$\n",
+ "type" : "equal",
+ "comment" : "equal Operator\n\nIt operates element-wise on X and Y, and returns the Out. Each of them is a\nN-dim tensor. X and Y could be any type.  The each element of the Out tensor is\ncalculated by Out = X == Y\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) Left hand operand of logical_and operator",
+   "comment" : "(LoDTensor) the left hand operand of equal operator",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
    "name" : "Y",
-   "comment" : "(LoDTensor) Right hand operand of logical_and operator",
+   "comment" : "(LoDTensor) the right hand operand of equal operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = X \\&\\& Y$$",
+   "comment" : "(LoDTensor) n-dim bool tensor. Each element is Out = X == Y",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "logical_not",
- "comment" : "logical_not Operator\n\nIt operates element-wise on X, and returns the Out. X and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = !X$$\n",
+ "type" : "gather",
+ "comment" : "\nGather Operator.\n\n$Out = X[Index]$\n\nOut is obtained by gathering entries of the outer-most dimension \nof X indexed by Index and concatenate them together.\n\nExample:\n\nX = [[1, 2],\n     [3, 4],\n     [5, 6]]\n\nIndex = [[1, 2]]\n\nThen:\n\nOut = [[3, 4],\n       [5, 6]]\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(LoDTensor) Operand of logical_not operator",
+   "comment" : "The source input of gather op",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Index",
+   "comment" : "The index input of gather op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = !X$$",
+   "comment" : "The output of gather op",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "abs",
- "comment" : "\nAbs Activation Operator.\n\n$y = |x|$\n\n",
+ "type" : "clip_by_norm",
+ "comment" : "\nClipByNorm Operator.\n\nThis operator limits the L2 norm of the input $X$ within $max\\_norm$.\nIf the L2 norm of $X$ is less than or equal to $max\\_norm$, $Out$ will be\nthe same as $X$. If the L2 norm of $X$ is greater than $max\\_norm$, $X$ will\nbe linearly scaled to make the L2 norm of $Out$ equal to $max\\_norm$, as\nshown in the following formula:\n\n$$\nOut = \\frac{max\\_norm * X}{norm(X)},\n$$\n\nwhere $norm(X)$ represents the L2 norm of $X$.\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Abs operator",
+   "comment" : "(Tensor) The input of clip_by_norm op.The number of dimensions must be between [1, 9].",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Abs operator",
+   "name" : "Out",
+   "comment" : "(Tensor) The output of clip_by_norm op with shape as input(X)",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "max_norm",
+   "type" : "float",
+   "comment" : "(float) The maximum norm value.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "logical_xor",
- "comment" : "logical_xor Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$\n",
+ "type" : "chunk_eval",
+ "comment" : "\nFor some basics of chunking, please refer to\n‘Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>’.\n\n\nCheckEvalOp computes the precision, recall, and F1-score of chunk detection,\nand supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.\nHere is a NER example of labeling for these tagging schemes:\n\n \t     Li     Ming    works  at  Agricultural   Bank   of    China  in  Beijing.\n  IO:    I-PER  I-PER   O      O   I-ORG          I-ORG  I-ORG I-ORG  O   I-LOC\n  IOB:   B-PER  I-PER   O      O   B-ORG          I-ORG  I-ORG I-ORG  O   B-LOC\n  IOE:   I-PER  E-PER   O      O   I-ORG          I-ORG  I-ORG E-ORG  O   E-LOC\n  IOBES: B-PER  E-PER   O      O   I-ORG          I-ORG  I-ORG E-ORG  O   S-LOC\n\nThere are three chunk types(named entity types) including PER(person), ORG(organization)\nand LOC(LOCATION), and we can see that the labels have the form <tag type>-<chunk type>.\n\nSince the calculations actually use label ids rather than labels, extra attention\nshould be paid when mapping labels to ids to make CheckEvalOp work. The key point\nis that the listed equations are satisfied by ids.\n\n    tag_type = label % num_tag_type\n    chunk_type = label / num_tag_type\n\nwhere `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`\nis the num of chunk types, and `tag_type` get its value from the following table.\n\n    Scheme Begin Inside End   Single\n     plain   0     -      -     -\n     IOB     0     1      -     -\n     IOE     -     0      1     -\n     IOBES   0     1      2     3\n\nStill use NER as example, assuming the tagging scheme is IOB while chunk types are ORG,\nPER and LOC. To satisfy the above equations, the label map can be like this:\n\n    B-ORG  0\n    I-ORG  1\n    B-PER  2\n    I-PER  3\n    B-LOC  4\n    I-LOC  5\n    O      6\n\nIt’s not hard to verify the equations noting that the num of chunk types\nis 3 and the num of tag types in IOB scheme is 2. For example, the label\nid of I-LOC is 5, the tag type id of I-LOC is 1, and the chunk type id of\nI-LOC is 2, which consistent with the results from the equations.\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(LoDTensor) Left hand operand of logical_xor operator",
+   "name" : "Inference",
+   "comment" : "(Tensor, default: Tensor<int64_t>). Predictions from the network.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "(LoDTensor) Right hand operand of logical_xor operator",
+   "name" : "Label",
+   "comment" : "(Tensor, default: Tensor<int64_t>). The true tag sequences.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(LoDTensor) n-dim bool tensor. Each element is $$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$",
+   "name" : "Precision",
+   "comment" : "(float). The evaluated precision (called positive predictive value) of chunks on the given mini-batch.",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "sequence_slice",
- "comment" : "\nSequence slice operator\n\nThe operator crops a subsequence from given sequence with given start offset and subsequence length.\nIt only supports sequence (LoD Tensor with level number is 1).\n- Case:\n    X = [[a1, a2;\n        b1, b2;\n        c1, c2]\n       [d1, d2;\n        e1, e2]]\n    LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)\n    Offset = [[0], [1]]; Length = [[2], [1]]\n\n    Out = [[a1, a2;\n            b1, b2]\n            [e1, e2]]\n    LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)\nNOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.\n    ",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "(LoDTensor), the input of SequenceSliceOp.",
+ }, { 
+   "name" : "Recall",
+   "comment" : "(float). The evaluated recall (true positive rate or sensitivity) of chunks on the given mini-batch.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Offset",
-   "comment" : "(Tensor), a vector<int> to describe the offset of every input sequence for sub sequence item.",
+   "name" : "F1-Score",
+   "comment" : "(float). The evaluated F1-Score on the given mini-batch.",
    "duplicable" : 0,
    "intermediate" : 0
+ } ], 
+ "attrs" : [ 
+ { 
+   "name" : "num_chunk_types",
+   "type" : "int",
+   "comment" : "(int). The number of chunk type. See below for details.",
+   "generated" : 0
  }, { 
-   "name" : "Length",
-   "comment" : "(Tensor), a vector<int> to describe the length of every input sequence for sub sequence item.",
+   "name" : "chunk_scheme",
+   "type" : "string",
+   "comment" : "(string, default IOB). The labeling scheme indicating how to encode the chunks. Must be IOB, IOE, IOBES or plain. See below for details.",
+   "generated" : 0
+ }, { 
+   "name" : "excluded_chunk_types",
+   "type" : "int array",
+   "comment" : "(list<int>) A list including chunk type ids indicating chunk types that are not counted. See below for details.",
+   "generated" : 0
+ } ] 
+},{
+ "type" : "sigmoid",
+ "comment" : "\nSigmoid Activation Operator\n\n$$y = \\frac{1}{1 + e^{-x}}$$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "Input of Sigmoid operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(LoDTensor), the output of SequenceSliceOp.",
+   "name" : "Y",
+   "comment" : "Output of Sigmoid operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "hinge_loss",
- "comment" : "\nHingeLoss Operator.\n\nLet x be a logit (prediction) and y be the actual label. The logit can\ntake any values from (-inf, inf), but the labels should be either -1 or 1.\nThen, the hinge loss is computed as follows:\n\n$$\nL_(x, y) = max(1 - y.x, 0) \n$$\n\nNote that the labels passed as input will have values as either 0 or 1.\n\n",
+ "type" : "sequence_concat",
+ "comment" : "\nThe sequence_concat operator concatenates multiple LoDTensors. \nIt only supports sequence (LoD Tensor with level number is 1) \nor a nested sequence (LoD tensor with level number is 2) as its input.\n- Case1:\n  If the axis is other than 0(here, axis is 1 and level is 1),\n  each input should have the same LoD information and the LoD \n  information of the output keeps the same as the input.\n\n  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)\n  LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)\n\n- Case2:\n  If the axis is 0(here, leve is 0), the inputs are concatenated along \n  time steps, the LoD information of the output need to re-compute.\n  The LoD information of level-1 should be same.\n\n  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n  LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\n- Case3:\n  If the axis is 0(here, level is 1).\n\n  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n  LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)\n\n- Case4:\n  If the LoD number is 1, axis is 0, level is 0\n\n  LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)\n  LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)\n  LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\nNOTE: The levels of all the inputs should be the same.\n    ",
  "inputs" : [ 
  { 
-   "name" : "Logits",
-   "comment" : "The input value (Logits) of Hinge loss op.Logits is a 2-D tensor with shape [batch_size, 1].",
+   "name" : "X",
+   "comment" : "(LodTensorArray) Input is a vector of LoDTensor, each of which is a variable-length sequence or nested sequence.",
+   "duplicable" : 1,
+   "intermediate" : 0
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "(LoDTensor), Variable-length output of sequence_concat Op.",
    "duplicable" : 0,
    "intermediate" : 0
+ } ], 
+ "attrs" : [ 
+ { 
+   "name" : "axis",
+   "type" : "int",
+   "comment" : "(int, default 0) The axis along which the inputs will be joined. If axis is 0, the inputs will be joined with LoD index.",
+   "generated" : 0
  }, { 
-   "name" : "Labels",
-   "comment" : "The target value (Labels) of Hinge loss op.Labels is a 2-D tensor with shape [batch_size, 1].",
+   "name" : "level",
+   "type" : "int",
+   "comment" : "(int, default 0) The level at which the inputs will be joined. If the level is 0, the inputs will be joined at the nested sequence level. If the level is 1, the inputs will be joined at the sequence level. The level should be less than the level number of inputs.",
+   "generated" : 0
+ } ] 
+},{
+ "type" : "floor",
+ "comment" : "\nFloor Activation Operator.\n\n$y = floor(x)$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "Input of Floor operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Loss",
-   "comment" : "The output tensor with shape [batch_size, 1] which represents the hinge loss.",
+   "name" : "Y",
+   "comment" : "Output of Floor operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "bilinear_tensor_product",
- "comment" : "\nBilinear Tensor Product operator.\nGiven input X and Y, a 3D tensor Weight and a Bias. Each column of the\nOutput is computed by one slice $i = 1, . . . , k$ of the tensor:\n\n$$\nM =  (X W_i) * Y \\\\\nOut_i = \\sum_j {M_j} + Bias_i\n$$\n\nWhere $W_i$ is the $i$-th slice of Input(Weight);\n      $M_j$ is the $j$-th column of $M$;\n      $Out_i$ is the $i$-th column of Output(Out);\n      $Bias_i$ is a column vector, each element of it is equal to\n        the $i$-th element of $Bias$;\n\n",
+ "type" : "cast",
+ "comment" : "\nCast Operator.\n\nThis Operator casts the input tensor to another data type and\nreturns tha Output Tensor.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The first input of bilinear_tensor_product operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "The second input of bilinear_tensor_product operator.",
+   "comment" : "The input tensor of cast op",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "Weight",
-   "comment" : "The learnable parameters of bilinear_tensor_product operator.",
+ } ], 
+ "outputs" : [ 
+ { 
+   "name" : "Out",
+   "comment" : "The output tensor of cast op",
    "duplicable" : 0,
    "intermediate" : 0
+ } ], 
+ "attrs" : [ 
+ { 
+   "name" : "out_dtype",
+   "type" : "int",
+   "comment" : "output data type",
+   "generated" : 0
  }, { 
-   "name" : "Bias",
-   "comment" : "The learnable bias of bilinear_tensor_product operator.",
+   "name" : "in_dtype",
+   "type" : "int",
+   "comment" : "input data type",
+   "generated" : 0
+ } ] 
+},{
+ "type" : "ceil",
+ "comment" : "\nCeil Activation Operator.\n\n$y = ceil(x)$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "X",
+   "comment" : "Input of Ceil operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of bilinear_tensor_product operator.",
+   "name" : "Y",
+   "comment" : "Output of Ceil operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
@@ -4379,592 +4570,419 @@
    "generated" : 0
  } ] 
 },{
- "type" : "beam_search_decode",
- "comment" : "\nPack the result of Beam search op into SentenceIds and SentenceScores.\n",
+ "type" : "bilinear_tensor_product",
+ "comment" : "\nBilinear Tensor Product operator.\nGiven input X and Y, a 3D tensor Weight and a Bias. Each column of the\nOutput is computed by one slice $i = 1, . . . , k$ of the tensor:\n\n$$\nM =  (X W_i) * Y \\\\\nOut_i = \\sum_j {M_j} + Bias_i\n$$\n\nWhere $W_i$ is the $i$-th slice of Input(Weight);\n      $M_j$ is the $j$-th column of $M$;\n      $Out_i$ is the $i$-th column of Output(Out);\n      $Bias_i$ is a column vector, each element of it is equal to\n        the $i$-th element of $Bias$;\n\n",
  "inputs" : [ 
  { 
-   "name" : "Ids",
-   "comment" : "(LodTensorArray)score of the candidate words in each step",
+   "name" : "X",
+   "comment" : "The first input of bilinear_tensor_product operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Scores",
-   "comment" : "(LodTensorArray)score of the candidate words in each step",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "SentenceIds",
-   "comment" : "(LodTensor)All possible result sentences of word ids",
+   "name" : "Y",
+   "comment" : "The second input of bilinear_tensor_product operator.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "SentenceScores",
-   "comment" : "(LodTensor)All possible result sentences of word scores",
+   "name" : "Weight",
+   "comment" : "The learnable parameters of bilinear_tensor_product operator.",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "assign",
- "comment" : "Assign Operator\n\nOut = X,  when type in [LoDTensor/SelectedRows/LoDTensorArray]\nraise error if the type is not listed above.\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "(LoDTensor, SelectedRows or LoDTensorArray) The input variable could be LoDTensor, SelectedRows or LoDTensorArray.",
+ }, { 
+   "name" : "Bias",
+   "comment" : "The learnable bias of bilinear_tensor_product operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "(LoDTensor, SelectedRows or LoDTensorArray) The type of output is the same as input X.",
+   "comment" : "The output of bilinear_tensor_product operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "split",
- "comment" : "\nSplit operator\n\nThis operator splits the input tensor into multiple sub-tensors.\n\nExample:\n  Input = [[1,2],\n           [3,4],\n           [5,6]]\n  sections = [2,1]\n  axis = 0\n  Output[0] = [[1,2],\n               [3,4]]\n  Output[1] = [[5,6]]\n\n    ",
+ "type" : "batch_norm",
+ "comment" : "\nBatch Normalization.\n\nBatch Norm has been implemented as discussed in the paper:\nhttps://arxiv.org/pdf/1502.03167.pdf\nCan be used as a normalizer function for conv2d and fully_connected operations.\nThe required data format for this layer is one of the following:\n1. NHWC `[batch, in_height, in_width, in_channels]`\n2. NCHW `[batch, in_channels, in_height, in_width]`\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) Input tensor of the split operator.",
+   "comment" : "The input tensor",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "(Tensor) Output tensors of the split operator.",
-   "duplicable" : 1,
+ }, { 
+   "name" : "Scale",
+   "comment" : "Scale is a 1-dimensional tensor of size C that is applied to the output",
+   "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [ 
- { 
-   "name" : "sections",
-   "type" : "int array",
-   "comment" : "(vector<int>) the length of each output along the specified axis.",
-   "generated" : 0
  }, { 
-   "name" : "num",
-   "type" : "int",
-   "comment" : "(int, default 0)Number of sub-tensors. This must evenly divide Input.dims()[axis]",
-   "generated" : 0
+   "name" : "Bias",
+   "comment" : "Bias is a 1-dimensional tensor of size C that is applied to the output",
+   "duplicable" : 0,
+   "intermediate" : 0
  }, { 
-   "name" : "axis",
-   "type" : "int",
-   "comment" : "(int, default 0) The axis which the input will be splited on.",
-   "generated" : 0
- } ] 
-},{
- "type" : "chunk_eval",
- "comment" : "\nFor some basics of chunking, please refer to\n‘Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>’.\n\n\nCheckEvalOp computes the precision, recall, and F1-score of chunk detection,\nand supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.\nHere is a NER example of labeling for these tagging schemes:\n\n \t     Li     Ming    works  at  Agricultural   Bank   of    China  in  Beijing.\n  IO:    I-PER  I-PER   O      O   I-ORG          I-ORG  I-ORG I-ORG  O   I-LOC\n  IOB:   B-PER  I-PER   O      O   B-ORG          I-ORG  I-ORG I-ORG  O   B-LOC\n  IOE:   I-PER  E-PER   O      O   I-ORG          I-ORG  I-ORG E-ORG  O   E-LOC\n  IOBES: B-PER  E-PER   O      O   I-ORG          I-ORG  I-ORG E-ORG  O   S-LOC\n\nThere are three chunk types(named entity types) including PER(person), ORG(organization)\nand LOC(LOCATION), and we can see that the labels have the form <tag type>-<chunk type>.\n\nSince the calculations actually use label ids rather than labels, extra attention\nshould be paid when mapping labels to ids to make CheckEvalOp work. The key point\nis that the listed equations are satisfied by ids.\n\n    tag_type = label % num_tag_type\n    chunk_type = label / num_tag_type\n\nwhere `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`\nis the num of chunk types, and `tag_type` get its value from the following table.\n\n    Scheme Begin Inside End   Single\n     plain   0     -      -     -\n     IOB     0     1      -     -\n     IOE     -     0      1     -\n     IOBES   0     1      2     3\n\nStill use NER as example, assuming the tagging scheme is IOB while chunk types are ORG,\nPER and LOC. To satisfy the above equations, the label map can be like this:\n\n    B-ORG  0\n    I-ORG  1\n    B-PER  2\n    I-PER  3\n    B-LOC  4\n    I-LOC  5\n    O      6\n\nIt’s not hard to verify the equations noting that the num of chunk types\nis 3 and the num of tag types in IOB scheme is 2. For example, the label\nid of I-LOC is 5, the tag type id of I-LOC is 1, and the chunk type id of\nI-LOC is 2, which consistent with the results from the equations.\n",
- "inputs" : [ 
- { 
-   "name" : "Inference",
-   "comment" : "(Tensor, default: Tensor<int64_t>). Predictions from the network.",
+   "name" : "Mean",
+   "comment" : "The global mean (for training) or estimated mean (for testing)",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "(Tensor, default: Tensor<int64_t>). The true tag sequences.",
+   "name" : "Variance",
+   "comment" : "The global variance (for training) or estimated Variance (for testing)",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Precision",
-   "comment" : "(float). The evaluated precision (called positive predictive value) of chunks on the given mini-batch.",
+   "name" : "Y",
+   "comment" : "result after normalization",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Recall",
-   "comment" : "(float). The evaluated recall (true positive rate or sensitivity) of chunks on the given mini-batch.",
+   "name" : "MeanOut",
+   "comment" : "Share memory with Mean. Store the global mean when training",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "F1-Score",
-   "comment" : "(float). The evaluated F1-Score on the given mini-batch.",
+   "name" : "VarianceOut",
+   "comment" : "Share memory with Variance. Store the global Variance when training",
    "duplicable" : 0,
    "intermediate" : 0
+ }, { 
+   "name" : "SavedMean",
+   "comment" : "Mean of the current mini batch, will apply to output when training",
+   "duplicable" : 0,
+   "intermediate" : 1
+ }, { 
+   "name" : "SavedVariance",
+   "comment" : "Variance of the current mini batch, will apply to output when training",
+   "duplicable" : 0,
+   "intermediate" : 1
  } ], 
  "attrs" : [ 
  { 
-   "name" : "num_chunk_types",
-   "type" : "int",
-   "comment" : "(int). The number of chunk type. See below for details.",
+   "name" : "is_test",
+   "type" : "bool",
+   "comment" : "",
    "generated" : 0
  }, { 
-   "name" : "chunk_scheme",
-   "type" : "string",
-   "comment" : "(string, default IOB). The labeling scheme indicating how to encode the chunks. Must be IOB, IOE, IOBES or plain. See below for details.",
+   "name" : "momentum",
+   "type" : "float",
+   "comment" : "",
    "generated" : 0
  }, { 
-   "name" : "excluded_chunk_types",
-   "type" : "int array",
-   "comment" : "(list<int>) A list including chunk type ids indicating chunk types that are not counted. See below for details.",
+   "name" : "epsilon",
+   "type" : "float",
+   "comment" : "",
+   "generated" : 0
+ }, { 
+   "name" : "tensor_format",
+   "type" : "string",
+   "comment" : "",
    "generated" : 0
  } ] 
 },{
- "type" : "sigmoid",
- "comment" : "\nSigmoid Activation Operator\n\n$$y = \\frac{1}{1 + e^{-x}}$$\n\n",
+ "type" : "auc",
+ "comment" : "\nArea Under The Curve (AUC) Operator.\n\nThis implementation computes the AUC according to forward output and label.\nIt is used very widely in binary classification evaluation. As a note:\nIf input label contains values other than 0 and 1, it will be cast\nto bool. You can find the relevant definitions here:\nhttps://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve\n\nThere are two types of possible curves:\n1. ROC: Receiver operating characteristic\n2. PR: Precision Recall\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "Input of Sigmoid operator",
+   "name" : "Out",
+   "comment" : "A floating point 2D tensor, values are in the range [0, 1].Each row is sorted in descending order. This input should be theoutput of topk.Typically, this tensor indicates the probability of each label",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Indices",
+   "comment" : "An int 2D tensor, indicating the indices of originaltensor before sorting. Typically, this tensor indicates which label the probability stands for.",
+   "duplicable" : 0,
+   "intermediate" : 0
+ }, { 
+   "name" : "Label",
+   "comment" : "A 2D int tensor indicating the label of the training data.The height is batch size and width is always 1.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "Output of Sigmoid operator",
+   "name" : "AUC",
+   "comment" : "A scalar representing the current area-under-the-curve.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "curve",
+   "type" : "string",
+   "comment" : "Curve type, can be 'ROC' or 'PR'.",
+   "generated" : 0
+ }, { 
+   "name" : "num_thresholds",
+   "type" : "int",
+   "comment" : "The number of thresholds to use when discretizing the roc curve.",
+   "generated" : 0
+ } ] 
 },{
- "type" : "squared_l2_distance",
- "comment" : "\nSquaredL2Distance operator\n\nThis operator will cacluate the squared L2 distance for the input and \nthe target. Number of distance value will be equal to the first dimension \nof input. First dimension of the target could be equal to the input or to 1. \nIf the first dimension of target is 1, the operator will broadcast target's \nfirst dimension to input's first dimension. During backward propagation, \nthe user can decide whether to calculate the gradient of the input or \nthe target or both.\n\nBoth the input X and Y can carry the LoD (Level of Details) information. \nHowever, the output only shares the LoD information with input X.\n    ",
+ "type" : "read_from_array",
+ "comment" : "\nReadFromArray Operator.\n\nRead a LoDTensor from a LoDTensor Array.\n\nAssume $T$ is LoDTensor, $i$ is the subscript of the array, and $A$ is the array. The\nequation is\n\n$$T = A[i]$$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor) Input of SquaredL2DistanceOp.",
+   "comment" : "(TensorArray) the array will be read from.",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "(Tensor) Target of SquaredL2DistanceOp.",
+   "name" : "I",
+   "comment" : "(Tensor) the subscript index in tensor array. The number of element should be 1",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "sub_result",
-   "comment" : "(Tensor) Buffering subtraction result which will be reused in backward.",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
    "name" : "Out",
-   "comment" : "(Tensor) Squared l2 distance between input and target.",
+   "comment" : "(LoDTensor) the tensor will be read from.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "relu",
- "comment" : "\nRelu Activation Operator.\n\n$y = \\max(x, 0)$\n\n",
+ "type" : "softplus",
+ "comment" : "\nSoftplus Activation Operator.\n\n$y = \\ln(1 + e^{x})$\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "Input of Relu operator",
+   "comment" : "Input of Softplus operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
    "name" : "Y",
-   "comment" : "Output of Relu operator",
+   "comment" : "Output of Softplus operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "fetch",
- "comment" : "\nFetch Operator.\n\nIt should not be configured by users directly.\n\n",
+ "type" : "split",
+ "comment" : "\nSplit operator\n\nThis operator splits the input tensor into multiple sub-tensors.\n\nExample:\n  Input = [[1,2],\n           [3,4],\n           [5,6]]\n  sections = [2,1]\n  axis = 0\n  Output[0] = [[1,2],\n               [3,4]]\n  Output[1] = [[5,6]]\n\n    ",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "The input of fetch op",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "The output of fetch op",
+   "comment" : "(Tensor) Input tensor of the split operator.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [ 
- { 
-   "name" : "col",
-   "type" : "int",
-   "comment" : "(int) The column of fetch",
-   "generated" : 0
- } ] 
-},{
- "type" : "while",
- "comment" : "\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "A set of variables, which are required by operators inside the block of While Op.",
-   "duplicable" : 1,
-   "intermediate" : 0
- }, { 
-   "name" : "Condition",
-   "comment" : "(Bool) An scalar. When it's False, the While Op will be terminated.",
-   "duplicable" : 1,
-   "intermediate" : 0
- } ], 
  "outputs" : [ 
  { 
    "name" : "Out",
-   "comment" : "A set of variables, which will be assigned with values generated by the operators inside the block of While Op.",
+   "comment" : "(Tensor) Output tensors of the split operator.",
    "duplicable" : 1,
    "intermediate" : 0
- }, { 
-   "name" : "StepScopes",
-   "comment" : "(StepScopeVar) A vector of local scope, which size equals the step number of While Op. The i'th scope storages temporary variables generated in the i'th step.",
-   "duplicable" : 0,
-   "intermediate" : 0
  } ], 
  "attrs" : [ 
  { 
-   "name" : "step_block",
-   "type" : "block id",
-   "comment" : "The step block inside WhileOp",
+   "name" : "sections",
+   "type" : "int array",
+   "comment" : "(vector<int>) the length of each output along the specified axis.",
    "generated" : 0
- } ] 
-},{
- "type" : "proximal_adagrad",
- "comment" : "\nProximal Adagrad Optimizer.\n\nOptimizer that implements the proximal adagrad algorithm:\n\n$$\nmoment = moment + grad * grad \\\\\nprox\\_param = param - learning\\_rate * grad * (1 / \\sqrt{moment}) \\\\\nparam = sign(prox\\_param) / (1 + learning\\_rate * l2) *\n        \\max(|prox\\_param| - learning\\_rate * l1 , 0)\n$$\n\nThe paper that proposed Proximal GD: \n(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)\nHere, we use the adagrad learning rate as specified here: \n(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n\n",
- "inputs" : [ 
- { 
-   "name" : "Param",
-   "comment" : "(Tensor, default Tensor<float>) Input parameter that has to be updated.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Moment",
-   "comment" : "(Tensor, default Tensor<float>) Moment parameter that has to be updated.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Grad",
-   "comment" : "(Tensor, default Tensor<float>) Input gradient of the parameter.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "LearningRate",
-   "comment" : "(Tensor, default Tensor<float>) The learning rate should be a tensor of size 1.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "ParamOut",
-   "comment" : "(Tensor) Output updated parameter value.",
-   "duplicable" : 0,
-   "intermediate" : 0
  }, { 
-   "name" : "MomentOut",
-   "comment" : "(Tensor) Output updated moment value.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [ 
- { 
-   "name" : "l1",
-   "type" : "float",
-   "comment" : "(float, default 0.0) L1 regularization strength.",
+   "name" : "num",
+   "type" : "int",
+   "comment" : "(int, default 0)Number of sub-tensors. This must evenly divide Input.dims()[axis]",
    "generated" : 0
  }, { 
-   "name" : "l2",
-   "type" : "float",
-   "comment" : "(float, default 0.0) L2 regularization strength.",
+   "name" : "axis",
+   "type" : "int",
+   "comment" : "(int, default 0) The axis which the input will be splited on.",
    "generated" : 0
  } ] 
 },{
- "type" : "minus",
- "comment" : "\nMinus Operator.\n\nEquation:\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 left tensor of minus operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "The right tensor of minus operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "The output tensor of minus operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "cos_sim",
- "comment" : "\nCosine Similarity Operator.\n\n$Out = X^T * Y / (\\sqrt{X^T * X} * \\sqrt{Y^T * Y})$\n\nThe input X and Y must have the same shape, except that the 1st dimension\nof input Y could be just 1 (different from input X), which will be\nbroadcasted to match the shape of input X before computing their cosine\nsimilarity.\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",
+ "type" : "beam_search_decode",
+ "comment" : "\nPack the result of Beam search op into SentenceIds and SentenceScores.\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "The 1st input of cos_sim op.",
+   "name" : "Ids",
+   "comment" : "(LodTensorArray)score of the candidate words in each step",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Y",
-   "comment" : "The 2nd input of cos_sim op.",
+   "name" : "Scores",
+   "comment" : "(LodTensorArray)score of the candidate words in each step",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "The output of cos_sim op.",
+   "name" : "SentenceIds",
+   "comment" : "(LodTensor)All possible result sentences of word ids",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "XNorm",
-   "comment" : "Norm of the first input, reduced along the 1st dimension.",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
-   "name" : "YNorm",
-   "comment" : "Norm of the second input, reduced along the 1st dimension.",
+   "name" : "SentenceScores",
+   "comment" : "(LodTensor)All possible result sentences of word scores",
    "duplicable" : 0,
-   "intermediate" : 1
+   "intermediate" : 0
  } ], 
  "attrs" : [  ] 
 },{
- "type" : "precision_recall",
- "comment" : "\nPrecision Recall Operator.\n\nWhen given Input(Indices) and Input(Labels), this operator can be used\nto compute various metrics including:\n1. macro average precision\n2. macro average recall\n3. macro f1 score\n4. micro average precision\n5. micro average recall\n6. micro f1 score\n\nTo compute the above metrics, we need to do statistics for true positives,\nfalse positives and false negatives. Here the count of true negatives is not\nnecessary, but counting it may provide potential usage and the cost is\ntrivial, so the operator also provides the count of true negatives.\n\nWe define state as a 2-D tensor with shape [class_number, 4]. Each row of a\nstate contains statistic variables for corresponding class. Layout of each row\nis: TP(true positives), FP(false positives), TN(true negatives),\nFN(false negatives). If Input(Weights) is provided, TP, FP, TN, FN will be\ncalculated by given weight instead of the instance count.\n\nThis operator also supports metrics computing for cross-batch situation. To\nachieve this, Input(StatesInfo) should be provided. State of current batch\ndata will be accumulated to Input(StatesInfo) and Output(AccumStatesInfo)\nis the accumulation state.\n\nOutput(BatchMetrics) is metrics of current batch data while\nOutput(AccumStatesInfo) is metrics of accumulation data.\n\n",
+ "type" : "assign",
+ "comment" : "Assign Operator\n\nOut = X,  when type in [LoDTensor/SelectedRows/LoDTensorArray]\nraise error if the type is not listed above.\n",
  "inputs" : [ 
  { 
-   "name" : "MaxProbs",
-   "comment" : "(Tensor, default Tensor<float>) A 2-D tensor with shape N x 1, where N is the batch size. Each row contains the max probability of an instance which computed by the previous top_k (k=1) operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Indices",
-   "comment" : "(Tensor, default Tensor<int>) A 2-D tensor with shape N x 1, where N is the batch size. Each row contains the corresponding index which computed by the previous top_k (k=1) operator.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Labels",
-   "comment" : "(Tensor, default Tensor<int>) A 2-D tensor with shape N x 1, where N is the batch size. Each element is a label and the value should be in [0, class_number - 1].",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Weights",
-   "comment" : "(Tensor, default Tensor<float>) A 2-D tensor with shape N x 1, where N is the batch size. This input is optional. If provided, weight of instance would be considered when computing metrics.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "StatesInfo",
-   "comment" : "(Tensor, default Tensor<int>) A 2-D tensor with shape D x 4, where D is the number of classes. This input is optional. If provided, current state will be accumulated to this state and the accumulation state will be the output state.",
+   "name" : "X",
+   "comment" : "(LoDTensor, SelectedRows or LoDTensorArray) The input variable could be LoDTensor, SelectedRows or LoDTensorArray.",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "BatchMetrics",
-   "comment" : "(Tensor, default Tensor<float>) A 1-D tensor with shape {6}. This output tensor contains metrics for current batch data. The layout is [macro average precision, macro average recall, macro f1 score, micro average precision, micro average recall, micro f1 score].",
+   "name" : "Out",
+   "comment" : "(LoDTensor, SelectedRows or LoDTensorArray) The type of output is the same as input X.",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "AccumMetrics",
-   "comment" : "(Tensor, default Tensor<float>) A 1-D tensor with shape {6}. This output tensor contains metrics for accumulated data. The layout is [macro average precision, macro average recall, macro f1 score, micro average precision, micro average recall, micro f1 score].",
+ } ], 
+ "attrs" : [  ] 
+},{
+ "type" : "adam",
+ "comment" : "\nAdam Optimizer.\n\nThis implements the Adam optimizer from Section 2 of the Adam\npaper : https://arxiv.org/abs/1412.6980.\nAdam is a first-order gradient-based optimization method based on\nadaptive estimates of lower-order moments.\n\nAdam updates:\n\n$$\nmoment\\_1\\_out = \\beta_1 * moment\\_1 + (1 - \\beta_1) * grad \\\\\nmoment\\_2_\\out = \\beta_2 * moment\\_2 + (1 - \\beta_2) * grad * grad \\\\\nlearning\\_rate = learning\\_rate *\n                  \\frac{\\sqrt{1 - \\beta_{2\\_pow}}}{1 - \\beta_{1\\_pow}} \\\\\nparam\\_out = param - learning\\_rate * \\frac{moment\\_1}{\\sqrt{moment\\_2} + \\epsilon}\n$$\n\n",
+ "inputs" : [ 
+ { 
+   "name" : "Param",
+   "comment" : "(Tensor) Input parameter",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "AccumStatesInfo",
-   "comment" : "(Tensor, default Tensor<float>) A 2-D tensor with shape D x 4, where D is equal to class number. This output tensor contains accumulated state variables used to compute metrics. The layout for each class is [true positives, false positives, true negatives, false negatives].",
+   "name" : "Grad",
+   "comment" : "(Tensor) Input gradient",
    "duplicable" : 0,
    "intermediate" : 0
- } ], 
- "attrs" : [ 
- { 
-   "name" : "class_number",
-   "type" : "int",
-   "comment" : "(int) Number of classes to be evaluated.",
-   "generated" : 0
- } ] 
-},{
- "type" : "batch_norm",
- "comment" : "\nBatch Normalization.\n\nBatch Norm has been implemented as discussed in the paper:\nhttps://arxiv.org/pdf/1502.03167.pdf\nCan be used as a normalizer function for conv2d and fully_connected operations.\nThe required data format for this layer is one of the following:\n1. NHWC `[batch, in_height, in_width, in_channels]`\n2. NCHW `[batch, in_channels, in_height, in_width]`\n\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "The input tensor",
+ }, { 
+   "name" : "LearningRate",
+   "comment" : "(Tensor) Learning rate",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Scale",
-   "comment" : "Scale is a 1-dimensional tensor of size C that is applied to the output",
+   "name" : "Moment1",
+   "comment" : "(Tensor) Input first moment",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Bias",
-   "comment" : "Bias is a 1-dimensional tensor of size C that is applied to the output",
+   "name" : "Moment2",
+   "comment" : "(Tensor) Input second moment",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Mean",
-   "comment" : "The global mean (for training) or estimated mean (for testing)",
+   "name" : "Beta1Pow",
+   "comment" : "(Tensor) Input beta1 power accumulator",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Variance",
-   "comment" : "The global variance (for training) or estimated Variance (for testing)",
+   "name" : "Beta2Pow",
+   "comment" : "(Tensor) Input beta2 power accumulator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Y",
-   "comment" : "result after normalization",
+   "name" : "ParamOut",
+   "comment" : "(Tensor) Output parameter",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "MeanOut",
-   "comment" : "Share memory with Mean. Store the global mean when training",
+   "name" : "Moment1Out",
+   "comment" : "(Tensor) Output first moment",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "VarianceOut",
-   "comment" : "Share memory with Variance. Store the global Variance when training",
+   "name" : "Moment2Out",
+   "comment" : "(Tensor) Output second moment",
    "duplicable" : 0,
    "intermediate" : 0
- }, { 
-   "name" : "SavedMean",
-   "comment" : "Mean of the current mini batch, will apply to output when training",
-   "duplicable" : 0,
-   "intermediate" : 1
- }, { 
-   "name" : "SavedVariance",
-   "comment" : "Variance of the current mini batch, will apply to output when training",
-   "duplicable" : 0,
-   "intermediate" : 1
  } ], 
  "attrs" : [ 
  { 
-   "name" : "is_test",
-   "type" : "bool",
-   "comment" : "",
+   "name" : "beta1",
+   "type" : "float",
+   "comment" : "(float, default 0.9) Exponential decay rate for the first moment estimates.",
    "generated" : 0
  }, { 
-   "name" : "momentum",
+   "name" : "beta2",
    "type" : "float",
-   "comment" : "",
+   "comment" : "(float, default 0.999) exponential decay rate for the second moment estimates.",
    "generated" : 0
  }, { 
    "name" : "epsilon",
    "type" : "float",
-   "comment" : "",
-   "generated" : 0
- }, { 
-   "name" : "tensor_format",
-   "type" : "string",
-   "comment" : "",
+   "comment" : "(float, default 1.0e-8) Constant for numerical stability",
    "generated" : 0
  } ] 
 },{
- "type" : "read_from_array",
- "comment" : "\nReadFromArray Operator.\n\nRead a LoDTensor from a LoDTensor Array.\n\nAssume $T$ is LoDTensor, $i$ is the subscript of the array, and $A$ is the array. The\nequation is\n\n$$T = A[i]$$\n\n",
+ "type" : "adadelta",
+ "comment" : "\nAdadelta Optimizer.\n\nAdadelta optimizer is implemented as explained in:\nhttps://arxiv.org/abs/1212.5701\nAdadelta is a per-dimension adaptive learning rate method used\nfor gradient descent.\n\nAdadelta updates are as follows:\n\n$$\navg\\_squared\\_grad\\_out = \\rho * avg\\_squared\\_grad + (1 - \\rho) * grad * grad \\\\\nparam\\_update =  - \\sqrt{\\frac{avg\\_squared\\_update + \\epsilon}{avg\\_squared\\_grad\\_out + \\epsilon}} * grad \\\\\navg\\_squared\\_update\\_out = \\rho * avg\\_squared\\_update + (1 - \\rho) * {param\\_update}^2 \\\\\nparam\\_out = param + param\\_update\n$$\n\n",
  "inputs" : [ 
  { 
-   "name" : "X",
-   "comment" : "(TensorArray) the array will be read from.",
+   "name" : "Param",
+   "comment" : "(Tensor) Input parameter",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "I",
-   "comment" : "(Tensor) the subscript index in tensor array. The number of element should be 1",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "(LoDTensor) the tensor will be read from.",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "softplus",
- "comment" : "\nSoftplus Activation Operator.\n\n$y = \\ln(1 + e^{x})$\n\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "Input of Softplus operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Y",
-   "comment" : "Output of Softplus operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
-},{
- "type" : "accuracy",
- "comment" : "\nAccuracy Operator. \n\nIt will print accuracy rate for classification.\nThe accuracy is calculated as follows:\n\n$$accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples}$$\n\nBoth the input Out and Label can carry the LoD (Level of Details)\ninformation, or not. But the output only shares the LoD information \nwith the input Out(Inference).\n\n",
- "inputs" : [ 
- { 
-   "name" : "Out",
-   "comment" : "The network output of topk (inferences)",
+   "name" : "Grad",
+   "comment" : "(Tensor) Input gradient",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Indices",
-   "comment" : "The the network output of topk (indices)",
+   "name" : "AvgSquaredGrad",
+   "comment" : "(Tensor) Input average of squared gradient",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Label",
-   "comment" : "Label of the training data",
+   "name" : "AvgSquaredUpdate",
+   "comment" : "(Tensor) Input average of squared parameter updates",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Accuracy",
-   "comment" : "The accuracy of current batch",
+   "name" : "ParamOut",
+   "comment" : "(Tensor) Output parameter",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Correct",
-   "comment" : "The correct samples count of current batch",
+   "name" : "AvgSquaredGradOut",
+   "comment" : "(Tensor) Output average of squared gradient",
    "duplicable" : 0,
    "intermediate" : 0
  }, { 
-   "name" : "Total",
-   "comment" : "The samples count of current batch",
+   "name" : "AvgSquaredUpdateOut",
+   "comment" : "(Tensor) Output average of squared parameter updates",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
- "attrs" : [  ] 
+ "attrs" : [ 
+ { 
+   "name" : "rho",
+   "type" : "float",
+   "comment" : "(float, default 0.95) Exponential decay rate for squared gradients.",
+   "generated" : 0
+ }, { 
+   "name" : "epsilon",
+   "type" : "float",
+   "comment" : "(float, default 1.0e-6) Constant for numerical stability",
+   "generated" : 0
+ } ] 
 },{
- "type" : "conv_shift",
- "comment" : "\nConvShift Operator.\n\nA layer for circular convolution of two vectors,\nas used in the Neural Turing Machine: https://arxiv.org/abs/1410.5401\n\nThe equation is:\n\n$$Out[i] = \\sum_{j=-(N-1)/2}^{(N-1)/2} X_{i+j} * Y_{j}$$\n\nwhere X's index is computed modulo M, and Y's index is computed modulo N.\n\nBoth inputs X and Y can carry LoD (Level of Details) information.\nHowever, the output only shares the LoD information with input X.\n\n",
+ "type" : "log",
+ "comment" : "\nLog Activation Operator.\n\n$y = \\ln(x)$\n\nNatural logarithm of x.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape B x M, where B is the batch size and M is the data dimension.",
-   "duplicable" : 0,
-   "intermediate" : 0
- }, { 
-   "name" : "Y",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape B x N, where B is the batch size and N is the data dimension. N must be odd.",
+   "comment" : "Input of Log operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
  "outputs" : [ 
  { 
-   "name" : "Out",
-   "comment" : "(Tensor, default Tensor<float>), a 2-D tensor with shape B x M, i.e., the same shape as X.",
+   "name" : "Y",
+   "comment" : "Output of Log operator",
    "duplicable" : 0,
    "intermediate" : 0
  } ], 
@@ -5289,22 +5307,4 @@
    "intermediate" : 0
  } ], 
  "attrs" : [  ] 
-},{
- "type" : "softsign",
- "comment" : "\nSoftsign Activation Operator.\n\n$$y = \\frac{x}{1 + |x|}$$\n\n",
- "inputs" : [ 
- { 
-   "name" : "X",
-   "comment" : "Input of Softsign operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "outputs" : [ 
- { 
-   "name" : "Y",
-   "comment" : "Output of Softsign operator",
-   "duplicable" : 0,
-   "intermediate" : 0
- } ], 
- "attrs" : [  ] 
 }]
diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js
index 26211c8008455c0202372075c1ba99e77cbb7d90..6d29e58889af86a2d4069fee496b04fbe3f7da70 100644
--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
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\ No newline at end of file
diff --git a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt
index 44dbeecbbdf39f5d8f529c63b9a52d71da26aede..757a5840bca4c8028e362789ec95bb03d261d2c1 100644
--- a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt
+++ b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt
@@ -1,17 +1,18 @@
 # 如何写新的Operator
 
  - [概念简介](#概念简介)
- - [实现C++类](#实现C++类)
-   - [定义ProtoMaker类](#定义ProtoMaker类)
-   - [定义Operator类](#定义Operator类)
-   - [定义OpKernel类](#定义OpKernel类)
-   - [注册Operator](#注册Operator)
+ - [实现C++类](#实现c类)
+   - [定义ProtoMaker类](#定义protomaker类)
+   - [定义Operator类](#定义operator类)
+   - [定义OpKernel类](#定义opkernel类)
+   - [注册Operator](#注册operator)
    - [编译](#编译)
- - [绑定Python](#绑定Python)
+ - [绑定Python](#绑定python)
  - [实现单元测试](#实现单元测试)
-   - [前向Operator单测](#前向Operator单测)
-   - [反向Operator单测](#反向Operator单测)
+   - [前向Operator单测](#前向operator单测)
+   - [反向Operator单测](#反向operator单测)
    - [编译和执行](#编译和执行)
+ - [注意事项](#注意事项)
 
 
 ## 概念简介
@@ -43,7 +44,7 @@ Kernel实现       | CPU、CUDA共享Kernel实现在`.h`文件中，否则，CPU
 ## 实现C++类
 
 
-### 1. 定义ProtoMaker类
+### 定义ProtoMaker类
 
 矩阵乘法的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。
 
@@ -100,7 +101,7 @@ The equation is: Out = scale*X
 - `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
 
 
-### 2. 定义Operator类
+### 定义Operator类
 
 下面的点实现了MulOp的定义：
 
@@ -149,7 +150,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
 
 通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和下面将要介绍的注册函数一起放在`.cc`中
 
-### 3. 定义OpKernel类
+### 定义OpKernel类
 
 `MulKernel`继承自`framework::OpKernel`，带有下面两个模板参数:
 
@@ -177,6 +178,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
     math::matmul<DeviceContext, T>(*X, false, *Y, false, 1, Z, 0, device_context);
   }
   };
+  ```
 
 需要注意：**不同设备(CPU、CUDA)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。**
 
@@ -188,7 +190,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
 到此，前向Op实现完成。接下来，需要在`.cc`文件中注册该op和kernel。
 反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。**但需注意反向Op没有`ProtoMaker`**。
 
-### 4. 注册Operator
+### 注册Operator
 
 - 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
 
@@ -220,7 +222,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
                            ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
     ```
 
-### 5. 编译
+### 编译
 
 运行下面命令可以进行编译：
 
@@ -236,6 +238,7 @@ make mul_op
 
 单测包括对比前向Op不同设备(CPU、CUDA)的实现、对比反向OP不同设备(CPU、CUDA)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
 
+### 前向Operator单测
 
 Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp`里完成。测试Operator，需要：
 
@@ -273,8 +276,7 @@ Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp
       def test_check_grad_ingore_y(self):
           self.check_grad(
               ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
-
-    ```
+  ```
 
 上面的代码首先导入依赖的包，下面是对`setUp`函数中操作的重要变量的详细解释：
 
@@ -282,6 +284,8 @@ Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp
 - `self.inputs` : 定义输入，类型为`numpy.array`，并初始化。
 - `self.outputs` : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。
 
+### 反向operator单测
+
 而反向测试中：
 - `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
   - 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
@@ -290,7 +294,7 @@ Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp
 - `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。
 
 
-### 编译和执行单元测试
+### 编译和执行
 
 `python/paddle/v2/framework/tests` 目录下新增的 `test_*.py` 单元测试会被自动加入工程进行编译。
 
diff --git a/develop/doc_cn/howto/dev/new_op_cn.html b/develop/doc_cn/howto/dev/new_op_cn.html
index 8eab287637c4808fcbc4fa6012561e8a085f48de..89581bebe55111691678967be7006255b7766d21 100644
--- a/develop/doc_cn/howto/dev/new_op_cn.html
+++ b/develop/doc_cn/howto/dev/new_op_cn.html
@@ -208,21 +208,22 @@
 <span id="operator"></span><h1>如何写新的Operator<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h1>
 <ul class="simple">
 <li><a class="reference external" href="#概念简介">概念简介</a></li>
-<li><a class="reference external" href="#实现C++类">实现C++类</a><ul>
-<li><a class="reference external" href="#定义ProtoMaker类">定义ProtoMaker类</a></li>
-<li><a class="reference external" href="#定义Operator类">定义Operator类</a></li>
-<li><a class="reference external" href="#定义OpKernel类">定义OpKernel类</a></li>
-<li><a class="reference external" href="#注册Operator">注册Operator</a></li>
+<li><a class="reference external" href="#实现c类">实现C++类</a><ul>
+<li><a class="reference external" href="#定义protomaker类">定义ProtoMaker类</a></li>
+<li><a class="reference external" href="#定义operator类">定义Operator类</a></li>
+<li><a class="reference external" href="#定义opkernel类">定义OpKernel类</a></li>
+<li><a class="reference external" href="#注册operator">注册Operator</a></li>
 <li><a class="reference external" href="#编译">编译</a></li>
 </ul>
 </li>
-<li><a class="reference external" href="#绑定Python">绑定Python</a></li>
+<li><a class="reference external" href="#绑定python">绑定Python</a></li>
 <li><a class="reference external" href="#实现单元测试">实现单元测试</a><ul>
-<li><a class="reference external" href="#前向Operator单测">前向Operator单测</a></li>
-<li><a class="reference external" href="#反向Operator单测">反向Operator单测</a></li>
+<li><a class="reference external" href="#前向operator单测">前向Operator单测</a></li>
+<li><a class="reference external" href="#反向operator单测">反向Operator单测</a></li>
 <li><a class="reference external" href="#编译和执行">编译和执行</a></li>
 </ul>
 </li>
+<li><a class="reference external" href="#注意事项">注意事项</a></li>
 </ul>
 <div class="section" id="">
 <span id="id1"></span><h2>概念简介<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
@@ -246,7 +247,7 @@ Kernel实现       | CPU、CUDA共享Kernel实现在<code class="docutils litera
 <div class="section" id="c">
 <span id="c"></span><h2>实现C++类<a class="headerlink" href="#c" title="永久链接至标题">¶</a></h2>
 <div class="section" id="protomaker">
-<span id="protomaker"></span><h3>1. 定义ProtoMaker类<a class="headerlink" href="#protomaker" title="永久链接至标题">¶</a></h3>
+<span id="protomaker"></span><h3>定义ProtoMaker类<a class="headerlink" href="#protomaker" title="永久链接至标题">¶</a></h3>
 <p>矩阵乘法的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。</p>
 <p>首先定义<code class="docutils literal"><span class="pre">ProtoMaker</span></code>来描述该Op的输入、输出，并添加注释：</p>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOpMaker</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
@@ -294,7 +295,7 @@ Kernel实现       | CPU、CUDA共享Kernel实现在<code class="docutils litera
 </ul>
 </div>
 <div class="section" id="operator">
-<span id="id2"></span><h3>2. 定义Operator类<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h3>
+<span id="id2"></span><h3>定义Operator类<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h3>
 <p>下面的点实现了MulOp的定义：</p>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOp</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span> <span class="p">{</span>
  <span class="k">public</span><span class="o">:</span>
@@ -337,7 +338,7 @@ Kernel实现       | CPU、CUDA共享Kernel实现在<code class="docutils litera
 <p>通常<code class="docutils literal"><span class="pre">OpProtoMaker</span></code>和<code class="docutils literal"><span class="pre">Op</span></code>类的定义写在<code class="docutils literal"><span class="pre">.cc</span></code>文件中，和下面将要介绍的注册函数一起放在<code class="docutils literal"><span class="pre">.cc</span></code>中</p>
 </div>
 <div class="section" id="opkernel">
-<span id="opkernel"></span><h3>3. 定义OpKernel类<a class="headerlink" href="#opkernel" title="永久链接至标题">¶</a></h3>
+<span id="opkernel"></span><h3>定义OpKernel类<a class="headerlink" href="#opkernel" title="永久链接至标题">¶</a></h3>
 <p><code class="docutils literal"><span class="pre">MulKernel</span></code>继承自<code class="docutils literal"><span class="pre">framework::OpKernel</span></code>，带有下面两个模板参数:</p>
 <ul class="simple">
 <li><code class="docutils literal"><span class="pre">typename</span> <span class="pre">DeviceContext</span></code>: 表示设备类型，不同设备(CPU、CUDA)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43"><code class="docutils literal"><span class="pre">OnehotCrossEntropyOpKernel</span></code></a>。</li>
@@ -350,47 +351,45 @@ Kernel实现       | CPU、CUDA共享Kernel实现在<code class="docutils litera
 <li><code class="docutils literal"><span class="pre">Compute</span></code>函数里实现<code class="docutils literal"><span class="pre">OpKernel</span></code>的具体计算逻辑。</li>
 </ul>
 <p>下面是 <code class="docutils literal"><span class="pre">MulKernel</span></code> <code class="docutils literal"><span class="pre">Compute</span></code>的实现：</p>
-<div class="highlight-cpp"><div class="highlight"><pre><span></span>template &lt;typename DeviceContext, typename T&gt;
-class MulKernel : public framework::OpKernel {
-public:
-void Compute(const framework::ExecutionContext&amp; context) const override {
-  auto* X = context.Input&lt;Tensor&gt;(&quot;X&quot;);
-  auto* Y = context.Input&lt;Tensor&gt;(&quot;Y&quot;);
-  auto* Z = context.Output&lt;Tensor&gt;(&quot;Out&quot;);
-  Z-&gt;mutable_data&lt;T&gt;(context.GetPlace());
-  auto&amp; device_context = context.template device_context&lt;DeviceContext&gt;();
-  math::matmul&lt;DeviceContext, T&gt;(*X, false, *Y, false, 1, Z, 0, device_context);
-}
-};
-
-需要注意：**不同设备(CPU、CUDA)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。**
-
-`MulOp`的CPU、CUDA实现共享同一个`Kernel`。`OpKernel`不共享的例子可以参考：[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
-
-为了使`OpKernel`的计算过程书写更加简单，并且CPU、CUDA的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库，请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)。
-
-
-到此，前向Op实现完成。接下来，需要在`.cc`文件中注册该op和kernel。
-反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。**但需注意反向Op没有`ProtoMaker`**。
-
-### 4. 注册Operator
-
-- 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
-
-  ```cpp
-  namespace ops = paddle::operators;
-  REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
-  REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel&lt;paddle::platform::CPUDeviceContext, float&gt;);
-  REGISTER_OP_CPU_KERNEL(mul_grad,
-                ops::MulGradKernel&lt;paddle::platform::CPUDeviceContext, float&gt;);
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">DeviceContext</span><span class="p">,</span> <span class="k">typename</span> <span class="n">T</span><span class="o">&gt;</span>
+<span class="k">class</span> <span class="nc">MulKernel</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpKernel</span> <span class="p">{</span>
+<span class="k">public</span><span class="o">:</span>
+<span class="kt">void</span> <span class="n">Compute</span><span class="p">(</span><span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">ExecutionContext</span><span class="o">&amp;</span> <span class="n">context</span><span class="p">)</span> <span class="k">const</span> <span class="k">override</span> <span class="p">{</span>
+  <span class="k">auto</span><span class="o">*</span> <span class="n">X</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="n">Input</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">);</span>
+  <span class="k">auto</span><span class="o">*</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="n">Input</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;Y&quot;</span><span class="p">);</span>
+  <span class="k">auto</span><span class="o">*</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="n">Output</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">);</span>
+  <span class="n">Z</span><span class="o">-&gt;</span><span class="n">mutable_data</span><span class="o">&lt;</span><span class="n">T</span><span class="o">&gt;</span><span class="p">(</span><span class="n">context</span><span class="p">.</span><span class="n">GetPlace</span><span class="p">());</span>
+  <span class="k">auto</span><span class="o">&amp;</span> <span class="n">device_context</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="k">template</span> <span class="n">device_context</span><span class="o">&lt;</span><span class="n">DeviceContext</span><span class="o">&gt;</span><span class="p">();</span>
+  <span class="n">math</span><span class="o">::</span><span class="n">matmul</span><span class="o">&lt;</span><span class="n">DeviceContext</span><span class="p">,</span> <span class="n">T</span><span class="o">&gt;</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="p">,</span> <span class="nb">false</span><span class="p">,</span> <span class="o">*</span><span class="n">Y</span><span class="p">,</span> <span class="nb">false</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">device_context</span><span class="p">);</span>
+<span class="p">}</span>
+<span class="p">};</span>
 </pre></div>
 </div>
-<p>在上面的代码中：</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>- `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`。
-- `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
-- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulGradKernel`类。
+<p>需要注意：<strong>不同设备(CPU、CUDA)共享一个Op定义，是否则共享同一个<code class="docutils literal"><span class="pre">OpKernel</span></code>，取决于<code class="docutils literal"><span class="pre">Compute</span></code>调用的函数是否支持不同设备。</strong></p>
+<p><code class="docutils literal"><span class="pre">MulOp</span></code>的CPU、CUDA实现共享同一个<code class="docutils literal"><span class="pre">Kernel</span></code>。<code class="docutils literal"><span class="pre">OpKernel</span></code>不共享的例子可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43"><code class="docutils literal"><span class="pre">OnehotCrossEntropyOpKernel</span></code></a>。</p>
+<p>为了使<code class="docutils literal"><span class="pre">OpKernel</span></code>的计算过程书写更加简单，并且CPU、CUDA的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现<code class="docutils literal"><span class="pre">Compute</span></code>接口。关于在PaddlePaddle中如何使用Eigen库，请参考<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md">使用文档</a>。</p>
+<p>到此，前向Op实现完成。接下来，需要在<code class="docutils literal"><span class="pre">.cc</span></code>文件中注册该op和kernel。
+反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。<strong>但需注意反向Op没有<code class="docutils literal"><span class="pre">ProtoMaker</span></code></strong>。</p>
+</div>
+<div class="section" id="operator">
+<span id="id3"></span><h3>注册Operator<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h3>
+<ul>
+<li><p class="first">在<code class="docutils literal"><span class="pre">.cc</span></code>文件中注册前向、反向Op类，注册CPU Kernel。</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
+<span class="n">REGISTER_OP</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOp</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOpMaker</span><span class="p">,</span> <span class="n">mul_grad</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOpGrad</span><span class="p">);</span>
+<span class="n">REGISTER_OP_CPU_KERNEL</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">CPUDeviceContext</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
+<span class="n">REGISTER_OP_CPU_KERNEL</span><span class="p">(</span><span class="n">mul_grad</span><span class="p">,</span>
+              <span class="n">ops</span><span class="o">::</span><span class="n">MulGradKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">CPUDeviceContext</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
 </pre></div>
 </div>
+<p>在上面的代码中：</p>
+<ul class="simple">
+<li><code class="docutils literal"><span class="pre">REGISTER_OP</span></code> ： 注册<code class="docutils literal"><span class="pre">ops::MulOp</span></code>类，类型名为<code class="docutils literal"><span class="pre">mul</span></code>，该类的<code class="docutils literal"><span class="pre">ProtoMaker</span></code>为<code class="docutils literal"><span class="pre">ops::MulOpMaker</span></code>，注册<code class="docutils literal"><span class="pre">ops::MulOpGrad</span></code>，类型名为<code class="docutils literal"><span class="pre">mul_grad</span></code>。</li>
+<li><code class="docutils literal"><span class="pre">REGISTER_OP_WITHOUT_GRADIENT</span></code> ： 用于注册没有反向的Op。</li>
+<li><code class="docutils literal"><span class="pre">REGISTER_OP_CPU_KERNEL</span></code> ：注册<code class="docutils literal"><span class="pre">ops::MulKernel</span></code>类，并特化模板参数为<code class="docutils literal"><span class="pre">paddle::platform::CPUPlace</span></code>和<code class="docutils literal"><span class="pre">float</span></code>类型，同理，注册<code class="docutils literal"><span class="pre">ops::MulGradKernel</span></code>类。</li>
+</ul>
+</li>
+</ul>
 <ul>
 <li><p class="first">在 <code class="docutils literal"><span class="pre">.cu</span></code>文件中注册CUDA Kernel。</p>
 <ul class="simple">
@@ -409,7 +408,7 @@ void Compute(const framework::ExecutionContext&amp; context) const override {
 </ul>
 </div>
 <div class="section" id="">
-<span id="id3"></span><h3>5. 编译<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
+<span id="id4"></span><h3>编译<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
 <p>运行下面命令可以进行编译：</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">mul_op</span>
 </pre></div>
@@ -421,8 +420,10 @@ void Compute(const framework::ExecutionContext&amp; context) const override {
 <p>系统会对新增的op自动绑定Python，并链接到生成的lib库中。</p>
 </div>
 <div class="section" id="">
-<span id="id4"></span><h2>实现单元测试<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<span id="id5"></span><h2>实现单元测试<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
 <p>单测包括对比前向Op不同设备(CPU、CUDA)的实现、对比反向OP不同设备(CPU、CUDA)的实现、反向Op的梯度测试。下面介绍介绍<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py"><code class="docutils literal"><span class="pre">MulOp</span></code>的单元测试</a>。</p>
+<div class="section" id="operator">
+<span id="id6"></span><h3>前向Operator单测<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h3>
 <p>Op单元测试继承自<code class="docutils literal"><span class="pre">OpTest</span></code>。各项更加具体的单元测试在<code class="docutils literal"><span class="pre">TestMulOp</span></code>里完成。测试Operator，需要：</p>
 <ol class="simple">
 <li>在<code class="docutils literal"><span class="pre">setUp</span></code>函数定义输入、输出，以及相关的属性参数。</li>
@@ -457,7 +458,6 @@ void Compute(const framework::ExecutionContext&amp; context) const override {
     <span class="k">def</span> <span class="nf">test_check_grad_ingore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
             <span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s1">&#39;Y&#39;</span><span class="p">))</span>
-
 </pre></div>
 </div>
 <p>上面的代码首先导入依赖的包，下面是对<code class="docutils literal"><span class="pre">setUp</span></code>函数中操作的重要变量的详细解释：</p>
@@ -466,6 +466,9 @@ void Compute(const framework::ExecutionContext&amp; context) const override {
 <li><code class="docutils literal"><span class="pre">self.inputs</span></code> : 定义输入，类型为<code class="docutils literal"><span class="pre">numpy.array</span></code>，并初始化。</li>
 <li><code class="docutils literal"><span class="pre">self.outputs</span></code> : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。</li>
 </ul>
+</div>
+<div class="section" id="operator">
+<span id="id7"></span><h3>反向operator单测<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h3>
 <p>而反向测试中：</p>
 <ul class="simple">
 <li><code class="docutils literal"><span class="pre">test_check_grad_normal</span></code>中调用<code class="docutils literal"><span class="pre">check_grad</span></code>使用数值法检测梯度正确性和稳定性。<ul>
@@ -476,8 +479,9 @@ void Compute(const framework::ExecutionContext&amp; context) const override {
 </li>
 <li><code class="docutils literal"><span class="pre">test_check_grad_ingore_x</span></code>和<code class="docutils literal"><span class="pre">test_check_grad_ingore_y</span></code>分支用来测试只需要计算一个输入梯度的情况。</li>
 </ul>
+</div>
 <div class="section" id="">
-<span id="id5"></span><h3>编译和执行单元测试<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
+<span id="id8"></span><h3>编译和执行<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
 <p><code class="docutils literal"><span class="pre">python/paddle/v2/framework/tests</span></code> 目录下新增的 <code class="docutils literal"><span class="pre">test_*.py</span></code> 单元测试会被自动加入工程进行编译。</p>
 <p>请注意，<strong>不同于Op的编译测试，运行单元测试测时需要编译整个工程</strong>，并且编译时需要打开<code class="docutils literal"><span class="pre">WITH_TESTING</span></code>, 即<code class="docutils literal"><span class="pre">cmake</span> <span class="pre">paddle_dir</span> <span class="pre">-DWITH_TESTING=ON</span></code>。编译成功后，执行下面的命令来运行单元测试：</p>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>make <span class="nb">test</span> <span class="nv">ARGS</span><span class="o">=</span><span class="s2">&quot;-R test_mul_op -V&quot;</span>
@@ -490,7 +494,7 @@ void Compute(const framework::ExecutionContext&amp; context) const override {
 </div>
 </div>
 <div class="section" id="">
-<span id="id6"></span><h2>注意事项<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<span id="id9"></span><h2>注意事项<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
 <ul class="simple">
 <li>为每个Op创建单独的<code class="docutils literal"><span class="pre">*_op.h</span></code>（如有）、<code class="docutils literal"><span class="pre">*_op.cc</span></code>和<code class="docutils literal"><span class="pre">*_op.cu</span></code>（如有）。不允许一个文件中包含多个Op，这将会导致编译出错。</li>
 <li>注册Op时的类型名，需要和该Op的名字一样。即不允许在<code class="docutils literal"><span class="pre">A_op.cc</span></code>里面，注册<code class="docutils literal"><span class="pre">REGISTER_OP(B,</span> <span class="pre">...)</span></code>等，这将会导致单元测试出错。</li>
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index d374cd129d26bba45835640821c0e52397175ee7..b7a97179d7e7f65377df334263e48b4f93792e05 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
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