Update pool docs. (tensorflow#1357)

DOC

src/ops/pool.ts

@@ -28,8 +28,10 @@ import {op} from './operation';
  *
  * @param x The input tensor, of rank 4 or rank 3 of shape
  *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size, a tuple `[filterHeight, filterWidth]`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`.
+ * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
  *     in which we sample input values across the height and width dimensions
  *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
@@ -47,7 +49,6 @@ import {op} from './operation';
  *     dimensions if pad is a number. If none is provided, it will not round
  *     and error if the output is of fractional size.
  */
-/** @doc {heading: 'Operations', subheading: 'Convolution'} */
 function maxPoolImpl_<T extends Tensor3D|Tensor4D>(
     x: T|TensorLike, filterSize: [number, number]|number,
     strides: [number, number]|number, dilations: [number, number]|number,
@@ -100,8 +101,10 @@ function maxPoolImpl_<T extends Tensor3D|Tensor4D>(
  *
  * @param x The input tensor, of rank 4 or rank 3 of shape
  *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size, a tuple `[filterHeight, filterWidth]`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`.
+ * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  * @param pad The type of padding algorithm.
  *    - `same` and stride 1: output will be of same size as input,
  *       regardless of filter size.
@@ -127,8 +130,10 @@ function maxPool_<T extends Tensor3D|Tensor4D>(
  *
  * @param x The input tensor, of rank 4 or rank 3 of shape
  *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size, a tuple `[filterHeight, filterWidth]`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`.
+ * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
  *     in which we sample input values across the height and width dimensions
  *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
@@ -146,7 +151,6 @@ function maxPool_<T extends Tensor3D|Tensor4D>(
  *     dimensions if pad is a number. If none is provided, it will not round
  *     and error if the output is of fractional size.
  */
-/** @doc {heading: 'Operations', subheading: 'Convolution'} */
 function avgPoolImpl_<T extends Tensor3D|Tensor4D>(
     x: T|TensorLike, filterSize: [number, number]|number,
     strides: [number, number]|number, dilations: [number, number]|number,
@@ -199,8 +203,10 @@ function avgPoolImpl_<T extends Tensor3D|Tensor4D>(
  *
  * @param x The input tensor, of rank 4 or rank 3 of shape
  *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size, a tuple `[filterHeight, filterWidth]`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`.
+ * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  * @param pad The type of padding algorithm:
  *    - `same` and stride 1: output will be of same size as input,
  *       regardless of filter size.
@@ -222,11 +228,12 @@ function avgPool_<T extends Tensor3D|Tensor4D>(
 }
 
 /**
- * Computes the 2D average pooling of an image.
+ * Performs an N-D pooling operation
  *
  * @param input The input tensor, of rank 4 or rank 3 of shape
  *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param windowShape The filter size, a tuple `[filterHeight, filterWidth]`.
+ * @param windowShape The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
  * @param poolingType The type of pooling, either 'max' or 'avg'.
  * @param pad The type of padding algorithm:
  *    - `same` and stride 1: output will be of same size as input,
@@ -236,13 +243,15 @@ function avgPool_<T extends Tensor3D|Tensor4D>(
  *    - For more info, see this guide:
  *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
  *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+ * @param dilationRate The dilation rates: `[dilationHeight, dilationWidth]`
  *     in which we sample input values across the height and width dimensions
- *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
+ *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single
  *     number, then `dilationHeight == dilationWidth`. If it is greater than
  *     1, then all values of `strides` must be 1.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  */
+/** @doc {heading: 'Operations', subheading: 'Convolution'} */
 function pool_<T extends Tensor3D|Tensor4D>(
     input: T|TensorLike, windowShape: [number, number]|number,
     poolingType: 'avg'|'max', padding: 'valid'|'same'|number,
@@ -270,8 +279,10 @@ function pool_<T extends Tensor3D|Tensor4D>(
  *     [batchSize, height, width, channels].
  * @param output The original output image, of rank 4, of shape
  *     [batchSize, outHeight, outWidth, channels].
- * @param filterSize The filter size, a tuple [filterHeight, filterWidth].
- * @param strides The strides of the pooling: [strideHeight, strideWidth].
+ * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  * @param pad A string from: 'same', 'valid'. The type of padding algorithm
  *     used in the forward prop of the op.
  * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The
@@ -331,8 +342,10 @@ function maxPoolBackprop(
  * @param input The input image, of rank 4 or rank 3 of shape
  *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
  * assumed.
- * @param filterSize The filter size, a tuple [filterHeight, filterWidth].
- * @param strides The strides of the pooling: [strideHeight, strideWidth].
+ * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+ *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+ * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+ *     `strides` is a single number, then `strideHeight == strideWidth`.
  * @param pad A string from: 'same', 'valid'. The type of padding algorithm
  *     used in the forward prop of the op.
  */