raymondxyang · raymondxyang · Feb 13, 2019 · Feb 13, 2019 · Feb 13, 2019 · Feb 13, 2019
diff --git a/docs/Changelog.md b/docs/Changelog.md
@@ -134,45 +134,6 @@ This version of the operator has been available since version 1 of the default O
 <dd>Constrain input and output types to float tensors.</dd>
 </dl>
 
-### <a name="Affine-1"></a>**Affine-1**</a>
-
-  Affine takes one input data (Tensor<T>) and produces one output data
-  (Tensor<T>) where the affine function, y = alpha * x + beta,
-  is applied to the tensor elementwise.
-
-#### Version
-
-No versioning maintained for experimental ops.
-#### Attributes
-
-<dl>
-<dt><tt>alpha</tt> : float (default is 1.0)</dt>
-<dd>Value of alpha</dd>
-<dt><tt>beta</tt> : float (default is 0.0)</dt>
-<dd>Value of beta</dd>
-</dl>
-
-#### Inputs
-
-<dl>
-<dt><tt>X</tt> : T</dt>
-<dd>1D input tensor</dd>
-</dl>
-
-#### Outputs
-
-<dl>
-<dt><tt>Y</tt> : T</dt>
-<dd>1D output tensor</dd>
-</dl>
-
-#### Type Constraints
-
-<dl>
-<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
-<dd>Constrain input and output types to float tensors.</dd>
-</dl>
-
 ### <a name="And-1"></a>**And-1**</a>
 
   Returns the tensor resulted from performing the `and` logical operation
@@ -739,45 +700,6 @@ This version of the operator has been available since version 1 of the default O
 <dd>Constrain input and output types to float tensors.</dd>
 </dl>
 
-### <a name="Crop-1"></a>**Crop-1**</a>
-
-  Crop and image to the specified spatial dimensions. If scale is given,
-  then optionally start the crop offset by the left/top border amounts.
-  If scale is not provided, crop the borders as provided.
-
-#### Version
-
-No versioning maintained for experimental ops.
-#### Attributes
-
-<dl>
-<dt><tt>border</tt> : list of ints</dt>
-<dd>A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).</dd>
-<dt><tt>scale</tt> : list of ints</dt>
-<dd>A 1-D values of (height, width).</dd>
-</dl>
-
-#### Inputs
-
-<dl>
-<dt><tt>input</tt> : T</dt>
-<dd>Input tensor of shape [N,C,H,W]</dd>
-</dl>
-
-#### Outputs
-
-<dl>
-<dt><tt>output</tt> : T</dt>
-<dd>Result, has same type as input, with H and W dimensions reduced.</dd>
-</dl>
-
-#### Type Constraints
-
-<dl>
-<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
-<dd>Constrain input and output types to float tensors.</dd>
-</dl>
-
 ### <a name="DepthToSpace-1"></a>**DepthToSpace-1**</a>
 
   DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
@@ -1850,44 +1772,6 @@ This version of the operator has been available since version 1 of the default O
 <dd>Only bool</dd>
 </dl>
 
-### <a name="ImageScaler-1"></a>**ImageScaler-1**</a>
-
-  Scale and bias the input image. Bias values are stored in
-  the same ordering as the image pixel format.
-
-#### Version
-
-No versioning maintained for experimental ops.
-#### Attributes
-
-<dl>
-<dt><tt>bias</tt> : list of floats</dt>
-<dd>Bias applied to each channel, same size as C.</dd>
-<dt><tt>scale</tt> : float (default is 1.0)</dt>
-<dd>The scale to apply.</dd>
-</dl>
-
-#### Inputs
-
-<dl>
-<dt><tt>input</tt> : T</dt>
-<dd>Input tensor of shape [N,C,H,W]</dd>
-</dl>
-
-#### Outputs
-
-<dl>
-<dt><tt>output</tt> : T</dt>
-<dd>Result, has same shape and type as input</dd>
-</dl>
-
-#### Type Constraints
-
-<dl>
-<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
-<dd>Constrain input and output types to float tensors.</dd>
-</dl>
-
 ### <a name="InstanceNormalization-1"></a>**InstanceNormalization-1**</a>
 
   Carries out instance normalization as described in the paper
@@ -3071,45 +2955,6 @@ This version of the operator has been available since version 1 of the default O
 <dd>Constrain input and output types to float tensors.</dd>
 </dl>
 
-### <a name="ParametricSoftplus-1"></a>**ParametricSoftplus-1**</a>
-
-  ParametricSoftplus takes one input data (Tensor<T>) and produces one output data
-  (Tensor<T>) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to
-  the tensor elementwise.
-
-#### Version
-
-No versioning maintained for experimental ops.
-#### Attributes
-
-<dl>
-<dt><tt>alpha</tt> : float</dt>
-<dd>Value of alpha</dd>
-<dt><tt>beta</tt> : float</dt>
-<dd>Value of beta</dd>
-</dl>
-
-#### Inputs
-
-<dl>
-<dt><tt>X</tt> : T</dt>
-<dd>1D input tensor</dd>
-</dl>
-
-#### Outputs
-
-<dl>
-<dt><tt>Y</tt> : T</dt>
-<dd>1D input tensor</dd>
-</dl>
-
-#### Type Constraints
-
-<dl>
-<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
-<dd>Constrain input and output types to float tensors.</dd>
-</dl>
-
 ### <a name="Pow-1"></a>**Pow-1**</a>
 
   Pow takes input data (Tensor<T>) and exponent Tensor, and
@@ -4803,7 +4648,6 @@ This version of the operator has been available since version 1 of the default O
     -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
      contains the indices of the top k elements (original indices from the input
      tensor).
-
   Given two equivalent values, this operator uses the indices along the axis  as
    a tiebreaker. That is, the element with the lower index will appear first.
 
@@ -6976,9 +6820,9 @@ This version of the operator has been available since version 7 of the default O
 
 ### <a name="Dropout-7"></a>**Dropout-7**</a>
 
-  Dropout takes one input floating tensor and produces two tensor outputs,
-  output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is
-  in test mode or not, the output Y will either be a random dropout, or a simple
+  Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
+  output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
+  test mode or not, the output Y will either be a random dropout, or a simple
   copy of the input. Note that our implementation of Dropout does scaling in
   the training phase, so during testing nothing needs to be done.
   This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
@@ -7006,7 +6850,7 @@ This version of the operator has been available since version 7 of the default O
 <dl>
 <dt><tt>output</tt> : T</dt>
 <dd>The output.</dd>
-<dt><tt>mask</tt> (optional) : T1</dt>
+<dt><tt>mask</tt> (optional) : T</dt>
 <dd>The output mask.</dd>
 </dl>
 
@@ -7015,8 +6859,6 @@ This version of the operator has been available since version 7 of the default O
 <dl>
 <dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
 <dd>Constrain input and output types to float tensors.</dd>
-<dt><tt>T1</tt> : tensor(bool)</dt>
-<dd>Constrain output mask types to boolean tensors.</dd>
 </dl>
 
 ### <a name="Equal-7"></a>**Equal-7**</a>
@@ -9049,6 +8891,47 @@ This version of the operator has been available since version 9 of the default O
 <dd>Constrain index tensor to int64</dd>
 </dl>
 
+### <a name="MeanVarianceNormalization-9"></a>**MeanVarianceNormalization-9**</a>
+
+  A MeanVarianceNormalization Function: Perform mean variance normalization
+        on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Use [0,2,3] (without C axis for N-D cases) for calculating means and variances along channels. Two variables with the same C-coordinate are associated with the same mean and variance.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+#### Function
+
+The Function can be represented as a function.
+
 ### <a name="NonZero-9"></a>**NonZero-9**</a>
 
   Returns the indices of the elements that are non-zero
@@ -9717,3 +9600,54 @@ This version of the operator has been available since version 10 of the default
 #### Type Constraints
 
 
+### <a name="TopK-10"></a>**TopK-10**</a>
+
+  Retrieve the top-K elements along a specified axis. Given an input tensor of
+  shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+    -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+      which contains the values of the top k elements along the specified axis
+    -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+     contains the indices of the top k elements (original indices from the input
+     tensor).
+
+  Given two equivalent values, this operator uses the indices along the axis  as
+   a tiebreaker. That is, the element with the lower index will appear first.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>Dimension on which to do the sort.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_n, r]</dd>
+<dt><tt>K</tt> : tensor(int64)</dt>
+<dd>A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Values</tt> : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor</dd>
+<dt><tt>Indices</tt> : I</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
diff --git a/docs/Functions-ml.md b/docs/Functions-ml.md
diff --git a/docs/Functions.md b/docs/Functions.md
diff --git a/docs/FunctionsChangelog-ml.md b/docs/FunctionsChangelog-ml.md