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authorTim Meinhardt <meinhardt.tim@gmail.com>2015-09-15 14:56:45 (GMT)
committerTim Meinhardt <meinhardt.tim@gmail.com>2015-09-25 10:05:54 (GMT)
commit9b2d267941411d9727a88ead18e3531bad50d14d (patch)
tree0151b8fd47959bbd953a0c863048ce1b9fe005c5
parentc77d5e5156f94720c1decd13f7f87fe78df9d4eb (diff)
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Update ArgMaxLayer documentation for axis param
-rw-r--r--include/caffe/common_layers.hpp12
1 files changed, 9 insertions, 3 deletions
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 491f9ed..d1ddaee 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -21,7 +21,8 @@ namespace caffe {
*
* Intended for use after a classification layer to produce a prediction.
* If parameter out_max_val is set to true, output is a vector of pairs
- * (max_ind, max_val) for each image.
+ * (max_ind, max_val) for each image. The axis parameter specifies an axis
+ * along which to maximise.
*
* NOTE: does not implement Backwards operation.
*/
@@ -34,7 +35,11 @@ class ArgMaxLayer : public Layer<Dtype> {
* - top_k (\b optional uint, default 1).
* the number @f$ K @f$ of maximal items to output.
* - out_max_val (\b optional bool, default false).
- * if set, output a vector of pairs (max_ind, max_val) for each image.
+ * if set, output a vector of pairs (max_ind, max_val) unless axis is set then
+ * output max_val along the specified axis.
+ * - axis (\b optional int).
+ * if set, maximise along the specified axis else maximise the flattened
+ * trailing dimensions for each index of the first / num dimension.
*/
explicit ArgMaxLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
@@ -54,7 +59,8 @@ class ArgMaxLayer : public Layer<Dtype> {
* the inputs @f$ x @f$
* @param top output Blob vector (length 1)
* -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val
- * @f$ (N \times 2 \times K \times 1) @f$
+ * @f$ (N \times 2 \times K \times 1) @f$ unless axis set than e.g.
+ * @f$ (N \times K \times H \times W) @f$ if axis == 1
* the computed outputs @f$
* y_n = \arg\max\limits_i x_{ni}
* @f$ (for @f$ K = 1 @f$).