use floating-point in docs (#6659)

* use `floating-point` in docs

* Get back capital letter.

docs/ops/activation/Clamp_1.md

@@ -23,14 +23,14 @@ clamp( x_{i} )=\min\big( \max\left( x_{i}, min\_value \right), max\_value \big)
 * *min*
 
   * **Description**: *min* is the lower bound of values in the output.
-  * **Range of values**: arbitrary floating point number
+  * **Range of values**: arbitrary floating-point number
   * **Type**: `float`
   * **Required**: *yes*
 
 * *max*
 
   * **Description**: *max* is the upper bound of values in the output.
-  * **Range of values**: arbitrary floating point number
+  * **Range of values**: arbitrary floating-point number
   * **Type**: `float`
   * **Required**: *yes*
 

docs/ops/activation/HSigmoid_5.md

@@ -27,7 +27,7 @@ The HSigmoid operation is introduced in the following [article](https://arxiv.or
 
 **Types**
 
-* *T*: any floating point type.
+* *T*: any floating-point type.
 
 **Examples**
 

docs/ops/activation/HSwish_4.md

@@ -27,7 +27,7 @@ The HSwish operation is introduced in the following [article](https://arxiv.org/
 
 **Types**
 
-* *T*: arbitrary supported floating point type.
+* *T*: arbitrary supported floating-point type.
 
 
 **Example**

docs/ops/activation/HardSigmoid_1.md

@@ -30,7 +30,7 @@ For each element from the input tensor calculates corresponding
 
 **Types**
 
-* *T*: any floating point type.
+* *T*: any floating-point type.
 
 **Examples**
 

docs/ops/activation/LogSoftmax_5.md

@@ -32,7 +32,7 @@ LogSoftmax(x, axis) = t - Log(ReduceSum(Exp(t), axis))
 
 **Types**
 
-* *T*: any floating point type.
+* *T*: any floating-point type.
 
 **Mathematical Formulation**
 

docs/ops/activation/Sigmoid_1.md

@@ -20,11 +20,11 @@ sigmoid( x ) = \frac{1}{1+e^{-x}}
 
 **Inputs**:
 
-*   **1**: Input tensor *x* of any floating point type. **Required.**
+*   **1**: Input tensor *x* of any floating-point type. **Required.**
 
 **Outputs**:
 
-*   **1**: Result of Sigmoid function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
+*   **1**: Result of Sigmoid function applied to the input tensor *x*. Floating-point tensor with shape and type matching the input tensor.
 
 **Example**
 

docs/ops/arithmetic/Acosh_3.md

@@ -20,7 +20,7 @@
 
 **Types**
 
-* *T*: any floating point type.
+* *T*: any floating-point type.
 
 *Acosh* does the following with the input tensor *a*:
 

docs/ops/arithmetic/Atanh_3.md

@@ -20,7 +20,7 @@
 
 **Types**
 
-* *T*: any floating point type.
+* *T*: any floating-point type.
 
 *Atanh* does the following with the input tensor *a*:
 

docs/ops/arithmetic/Erf_1.md

@@ -27,7 +27,7 @@ erf(x) = \pi^{-1} \int_{-x}^{x} e^{-t^2} dt
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Examples**
 

docs/ops/detection/DeformablePSROIPooling_1.md

@@ -66,7 +66,7 @@ This operation is compatible with [MXNet DeformablePSROIPooling](https://mxnet.a
 
 * *trans_std*
   * **Description**: *trans_std* is the value that all third input values (offests) are multiplied with to modulate the magnitude of the offsets.
-  * **Range of values**: floating point number
+  * **Range of values**: floating-point number
   * **Type**: `float`
   * **Default value**: 1
   * **Required**: *no*
@@ -93,7 +93,7 @@ Batch indices must be in the range of `[0, N_in-1]`.
 
 **Types**:
 
-* *T*: Any floating point type.
+* *T*: Any floating-point type.
 
 
 **Example**

docs/ops/detection/DetectionOutput_1.md

@@ -69,14 +69,14 @@ At each feature map cell, *DetectionOutput* predicts the offsets relative to the
 * *nms_threshold*
 
   * **Description**: threshold to be used in the NMS stage
-  * **Range of values**: floating point values
+  * **Range of values**: floating-point values
   * **Type**: float
   * **Required**: *yes*
 
 * *confidence_threshold*
 
   * **Description**: only consider detections whose confidences are larger than a threshold. If not provided, consider all boxes.
-  * **Range of values**: floating point values
+  * **Range of values**: floating-point values
   * **Type**: float
   * **Default value**: 0
   * **Required**: *no*
@@ -145,7 +145,7 @@ At each feature map cell, *DetectionOutput* predicts the offsets relative to the
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 
 **Example**

docs/ops/detection/ExperimentalDetectronDetectionOutput_6.md

@@ -58,15 +58,15 @@ tensor elements.
 
     * **Description**: The *score_threshold* attribute specifies a threshold to consider only detections whose score are
     larger than the threshold.
-    * **Range of values**: non-negative floating point number
+    * **Range of values**: non-negative floating-point number
     * **Type**: float
     * **Default value**: None
     * **Required**: *yes*
 
 * *nms_threshold*
 
     * **Description**: The *nms_threshold* attribute specifies a threshold to be used in the NMS stage.
-    * **Range of values**: non-negative floating point number
+    * **Range of values**: non-negative floating-point number
     * **Type**: float
     * **Default value**: None
     * **Required**: *yes*
@@ -109,15 +109,15 @@ tensor elements.
 * *max_delta_log_wh*
 
     * **Description**: The *max_delta_log_wh* attribute specifies maximal delta of logarithms for width and height.
-    * **Range of values**: floating point number
+    * **Range of values**: floating-point number
     * **Type**: float
     * **Default value**: None
     * **Required**: *yes*
 
 * *deltas_weights*
 
     * **Description**: The *deltas_weights* attribute specifies weights for bounding boxes sizes deltas.
-    * **Range of values**: a list of non-negative floating point numbers
+    * **Range of values**: a list of non-negative floating-point numbers
     * **Type**: float[]
     * **Default value**: None
     * **Required**: *yes*
@@ -146,7 +146,7 @@ should be the same. **Required.**
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 * *T_IND*: `int64` or `int32`.
 

docs/ops/detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md

@@ -23,15 +23,15 @@ is less than *post_nms_count* returns output tensors filled with zeroes.
 * *min_size*
 
     * **Description**: The *min_size* attribute specifies minimum box width and height.
-    * **Range of values**: non-negative floating point number
+    * **Range of values**: non-negative floating-point number
     * **Type**: float
     * **Default value**: None
     * **Required**: *yes*
 
 * *nms_threshold*
 
     * **Description**: The *nms_threshold* attribute specifies threshold to be used in the NMS stage.
-    * **Range of values**: non-negative floating point number
+    * **Range of values**: non-negative floating-point number
     * **Type**: float
     * **Default value**: None
     * **Required**: *yes*
@@ -73,7 +73,7 @@ Height and width for third and fourth inputs should be equal. **Required.**
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Example**
 

docs/ops/detection/ExperimentalDetectronPriorGridGenerator_6.md

@@ -81,7 +81,7 @@ rest output tensor elements.
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Example**
 

docs/ops/detection/ExperimentalDetectronROIFeatureExtractor_6.md

@@ -86,7 +86,7 @@ must be the same as for 1 input: `[number_of_ROIs, 4]`.
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Example**
 

docs/ops/detection/PSROIPooling_1.md

@@ -72,7 +72,7 @@ Batch indices must be in the range of `[0, N-1]`.
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Example**
 

docs/ops/detection/PriorBoxClustered_1.md

@@ -11,7 +11,7 @@
 * *width (height)*
 
   * **Description**: *width (height)* specifies desired boxes widths (heights) in pixels.
-  * **Range of values**: floating point positive numbers
+  * **Range of values**: floating-point positive numbers
   * **Type**: float[]
   * **Default value**: 1.0
   * **Required**: *no*
@@ -29,30 +29,30 @@
 * *step (step_w, step_h)*
 
   * **Description**: *step (step_w, step_h)* is a distance between box centers. For example, *step* equal 85 means that the distance between neighborhood prior boxes centers is 85. If both *step_h* and *step_w* are 0 then they are updated with value of *step*. If after that they are still 0 then they are calculated as input image width(height) divided with first input width(height).
-  * **Range of values**: floating point positive number
+  * **Range of values**: floating-point positive number
   * **Type**: float
   * **Default value**: 0.0
   * **Required**: *no*
 
 * *offset*
 
   * **Description**: *offset* is a shift of box respectively to top left corner. For example, *offset* equal 85 means that the shift of neighborhood prior boxes centers is 85.
-  * **Range of values**: floating point positive number
+  * **Range of values**: floating-point positive number
   * **Type**: float
   * **Required**: *yes*
 
 * *variance*
 
   * **Description**: *variance* denotes a variance of adjusting bounding boxes.
-  * **Range of values**: floating point positive numbers
+  * **Range of values**: floating-point positive numbers
   * **Type**: float[]
   * **Default value**: []
   * **Required**: *no*
 
 * *img_h (img_w)*
 
   * **Description**: *img_h (img_w)* specifies height (width) of input image. These attributes are taken from the second input `image_size` height(width) unless provided explicitly as the value for this attributes.
-  * **Range of values**: floating point positive number
+  * **Range of values**: floating-point positive number
   * **Type**: float
   * **Default value**: 0
   * **Required**: *no*

docs/ops/detection/PriorBox_1.md

@@ -46,7 +46,7 @@
 * *min_size (max_size)*
 
   * **Description**: *min_size (max_size)* is the minimum (maximum) box size (in pixels).
-  * **Range of values**: positive floating point numbers
+  * **Range of values**: positive floating-point numbers
   * **Type**: `float[]`
   * **Default value**: []
   * **Required**: *no*
@@ -82,22 +82,22 @@
 * *step*
 
   * **Description**: *step* is a distance between box centers.
-  * **Range of values**: floating point non-negative number
+  * **Range of values**: floating-point non-negative number
   * **Type**: `float`
   * **Default value**: 0
   * **Required**: *no*
 
 * *offset*
 
   * **Description**: *offset* is a shift of box respectively to top left corner.
-  * **Range of values**: floating point non-negative number
+  * **Range of values**: floating-point non-negative number
   * **Type**: `float`
   * **Required**: *yes*
 
 * *variance*
 
   * **Description**: *variance* denotes a variance of adjusting bounding boxes. The attribute could contain 0, 1 or 4 elements.
-  * **Range of values**: floating point positive numbers
+  * **Range of values**: floating-point positive numbers
   * **Type**: `float[]`
   * **Default value**: []
   * **Required**: *no*
@@ -149,7 +149,7 @@
 **Types**
 
 * *T_INT*: any supported integer type.
-* *T_OUT*: supported floating point type.
+* *T_OUT*: supported floating-point type.
 
 **Example**
 

docs/ops/detection/Proposal_1.md

@@ -141,7 +141,7 @@
 
 **Types**
 
-* *T*: floating point type.
+* *T*: floating-point type.
 
 **Example**
 

docs/ops/detection/Proposal_4.md

@@ -152,7 +152,7 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
 
 **Types**
 
-* *T*: floating point type.
+* *T*: floating-point type.
 
 **Example**
 

docs/ops/detection/ROIAlign_3.md

@@ -69,7 +69,7 @@ The box height and width are calculated the following way: `roi_width = max(spat
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 * *IND_T*: any supported integer type.
 

docs/ops/detection/ROIPooling_1.md

@@ -64,7 +64,7 @@ Batch indices must be in the range of `[0, N-1]`.
 
 **Types**
 
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Example**
 

docs/ops/detection/RegionYolo_1.md

@@ -13,7 +13,7 @@
 * *anchors*
 
   * **Description**: *anchors* codes a flattened list of pairs `[width, height]` that codes prior box sizes. This attribute is not used in output computation, but it is required for post-processing to restore real box coordinates.
-  * **Range of values**: list of any length of positive floating point number
+  * **Range of values**: list of any length of positive floating-point number
   * **Type**: `float[]`
   * **Default value**: None
   * **Required**: *no*
@@ -83,7 +83,7 @@
     `output.shape = [data.shape[0], ..., data.shape[axis-1], flat_dim, data.shape[end_axis + 1], ...]`
 
 **Types**
-* *T*: any supported floating point type.
+* *T*: any supported floating-point type.
 
 **Example**
 

docs/ops/generation/Range_4.md

@@ -51,7 +51,7 @@ the i-th element is calculated by the following formula:
 val[i+1]=val[i]+step.
 \f]
 
-The calculations are done after casting all values to `accumulate_type(output_type)`. `accumulate_type` is a type that have better or equal accuracy for accumulation than `output_type` on current hardware, e.g. `fp64` for `fp16`. The number of elements is calculated in the floating point type according to the following formula:
+The calculations are done after casting all values to `accumulate_type(output_type)`. `accumulate_type` is a type that have better or equal accuracy for accumulation than `output_type` on current hardware, e.g. `fp64` for `fp16`. The number of elements is calculated in the floating-point type according to the following formula:
 
 \f[
 max(ceil((end − start) / step), 0)
@@ -103,7 +103,7 @@ This is aligned with PyTorch's operation `torch.arange`, to align with tensorflo
 </layer>
 ```
 
-*Example 3: floating point*
+*Example 3: floating-point*
 
 ```xml
 <layer ... type="Range">