Update src/mlpack/methods/ann/layer/convolution.hpp

src/mlpack/methods/ann/layer/convolution.hpp

@@ -29,26 +29,36 @@ namespace ann /** Artificial Neural Network. */ {
 /**
  * Implementation of the Convolution class. The Convolution class represents a
  * single layer of a neural network.
- *Example about how to organize the input matrix of the CNN. Say that I pass a matrix M(2744x100) to model.Add<Convolution<>>, which I have obtained from "flattening" 
- *100 images (or Mel cepstral coefficients, if we talk about speech, or whatever you like) of dimension 196x14. In other words, the first 196 columns of each row of M 
- *will be made of the 196 columns of the first row of each of the 100 images (or Mel cepstral coefficients). Then the next 295 columns of M (196 - 393) will be made 
- *of the 196 columns of the second row of the 100 images (or Mel cepstral coefficients), etc. I want my input to be 196x14 for, so my add will be something like this:
- 
- *model.Add<Convolution<>>
- *(1, // Number of input activation maps.
-    *14, // Number of output activation maps.
-        *3, // Filter width.
-            *3, // Filter height.
-                *1, // Stride along width.
-                    *1, // Stride along height.
-                        *0, // Padding width.
-                            *0, // Padding height.
-                                *196, // Input width.
-                                    *14 // Input height.
-                                        *);
- *By doing so, will recreate the original 196x14 matrix for each image (or Mel cepstral coefficients) that will be used as input for the 14 filters of this example.
-
-* @tparam ForwardConvolutionRule Convolution to perform forward process.
+ * Example usage:
+ * 
+ * Suppose we want to pass a matrix M (2744x100) to a `Convolution` layer;
+ * in this example, `M` was obtained from "flattening" 100 images (or Mel
+ * cepstral coefficients, if we talk about speech, or whatever you like) of
+ * dimension 196x14. In other words, the first 196 columns of each row of M
+ * will be made of the 196 columns of the first row of each of the 100 images
+ * (or Mel cepstral coefficients). Then the next 295 columns of M (196 - 393)
+ * will be made of the 196 columns of the second row of the 100 images (or Mel
+ * cepstral coefficients), etc.  Given that the size of our 2-D input images is
+ * 196x14, the parameters for our `Convolution` layer will be something like
+ * this:
+ *
+ * ```
+ * Convolution<> c(1, // Number of input activation maps.
+ *                 14, // Number of output activation maps.
+ *                 3, // Filter width.
+ *                 3, // Filter height.
+ *                 1, // Stride along width.
+ *                 1, // Stride along height.
+ *                 0, // Padding width.
+ *                 0, // Padding height.
+ *                 196, // Input width.
+ *                 14); // Input height.
+ * ```
+ *
+ * This `Convolution<>` layer will treat each column of the input matrix `M` as  * a 2-D image (or object) of the original 196x14 size, using this as the input
+ * for the 14 filters of this example.
+ *
+ * @tparam ForwardConvolutionRule Convolution to perform forward process.
  * @tparam BackwardConvolutionRule Convolution to perform backward process.
  * @tparam GradientConvolutionRule Convolution to calculate gradient.
  * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,