deleted old, wrong, and duplicative function docs

dlib/cuda/cudnn_dlibapi.h

@@ -73,23 +73,6 @@ namespace dlib
             float alpha,
             const tensor& src
         );
-        /*!
-            requires
-                - One of the following is true: 
-                    - have_same_dimensions(src, dest)
-                    - src.num_samples()==1 && src.k()==dest.k() && src.nr()==1 && src.nc()==1
-                    - src.num_samples()==1 && src.k()==dest.k() && src.nr()==dest.nr() && src.nc()==dest.nc()
-                    - src.num_samples()==1 && src.k()==1 && src.nr()==dest.nr() && src.nc()==dest.nc()
-                - is_same_object(src,dest) == false
-            ensures
-                - performs: dest = beta*dest + alpha*src
-                  However, how the addition happens depends on the dimensions of src.  In
-                  particular, this function adds the scaled values of one src tensor to
-                  dest. Each dimension of the src tensor must match the corresponding
-                  dimension of the dest tensor or must be equal to 1. In the latter case,
-                  the same value from the src tensor, for those dimensions, will be used to
-                  add into the dest tensor.
-        !*/
 
     // ------------------------------------------------------------------------------------
 
@@ -97,22 +80,6 @@ namespace dlib
             tensor& grad,
             const tensor& gradient_input
         );
-        /*!
-            requires
-                - grad.num_samples() == 1
-                - grad.k()  >= 1
-                - grad.nr() == 1
-                - grad.nc() == 1
-                - gradient_input.k() == grad.k()
-                - gradient_input.size() > 0
-                - is_same_object(grad,gradient_input) == false
-            ensures
-                - let BIAS be a tensor with all dimensions equal to 1 except for k which is >= 1.
-                - let OUT be the output of add(1,OUT,1,BIAS)
-                - let f(gradient_input,BIAS) == dot(gradient_input,OUT)
-                - Then this function computes the gradient of f() with respect to BIAS and
-                  assigns it to grad.
-        !*/
 
     // ------------------------------------------------------------------------------------
 
@@ -352,39 +319,12 @@ namespace dlib
             tensor& dest,
             const tensor& src
         );
-        /*!
-            requires
-                - have_same_dimensions(dest, src) == true
-            ensures
-                - Note that the softmax function is a vector valued function: 
-                    s(x) == exp(x)/sum(exp(x)) 
-                - Computes the softmax function on src and writes the results to dest.  The
-                  softmax is computed per spatial location across the different channels at
-                  each location.  That is, softmax() outputs a new tensor, #dest, where
-                  each of the spatial locations in dest (i.e. image idx, row idx, and
-                  column idx) contains the output of s() evaluated over the channel values
-                  at each location.
-                - This function supports in-place operation, i.e. having
-                  is_same_object(dest, src)==true
-        !*/
 
         void softmax_gradient (
             tensor& grad,
             const tensor& dest,
             const tensor& gradient_input
         );
-        /*!
-            requires
-                - have_same_dimensions(dest,gradient_input) == true 
-                - have_same_dimensions(dest,grad) == true 
-                - is_same_object(grad, dest)==false
-            ensures
-                - We interpret dest as the output of softmax(dest,SRC) for some SRC tensor.
-                  Then let f(SRC) == dot(gradient_input,dest) Then this function computes
-                  the gradient of f() with respect to SRC and assigns it to grad.
-                - This function supports in-place operation, i.e. having
-                  is_same_object(grad, gradient_input)==true
-        !*/
 
     // ------------------------------------------------------------------------------------
 
@@ -405,34 +345,12 @@ namespace dlib
             tensor& dest,
             const tensor& src
         );
-        /*!
-            requires
-                - have_same_dimensions(dest, src) == true
-            ensures
-                - for all valid i:
-                    - #dest.host()[i] == 1/(1+std::exp(-src.host()[i])) 
-                - This function supports in-place operation, i.e. having
-                  is_same_object(dest, src)==true
-        !*/
 
         void sigmoid_gradient (
             tensor& grad,
             const tensor& dest,
             const tensor& gradient_input
         );
-        /*!
-            requires
-                - have_same_dimensions(dest,gradient_input) == true 
-                - have_same_dimensions(dest,grad) == true 
-                - is_same_object(grad,dest) == false
-            ensures
-                - Recalling that dest is the output of sigmoid(dest,SRC) for some SRC tensor,
-                  let f(SRC) == dot(gradient_input,dest)
-                - Then this function computes the gradient of f() with respect to SRC and
-                  assigns it to grad.
-                - This function supports in-place operation, i.e. having
-                  is_same_object(grad, gradient_input)==true
-        !*/
 
     // ------------------------------------------------------------------------------------
 
@@ -440,34 +358,12 @@ namespace dlib
             tensor& dest,
             const tensor& src
         );
-        /*!
-            requires
-                - have_same_dimensions(dest, src) == true
-            ensures
-                - for all valid i:
-                    - #dest.host()[i] == std::max(0,src.host()[i]) 
-                - This function supports in-place operation, i.e. having
-                  is_same_object(dest, src)==true
-        !*/
 
         void relu_gradient (
             tensor& grad,
             const tensor& dest,
             const tensor& gradient_input
         );
-        /*!
-            requires
-                - have_same_dimensions(dest,gradient_input) == true 
-                - have_same_dimensions(dest,grad) == true 
-                - is_same_object(grad,dest) == false
-            ensures
-                - Recalling that dest is the output of relu(dest,SRC) for some SRC tensor,
-                  let f(SRC) == dot(gradient_input,dest)
-                - Then this function computes the gradient of f() with respect to SRC and
-                  assigns it to grad.
-                - This function supports in-place operation, i.e. having
-                  is_same_object(grad, gradient_input)==true
-        !*/
 
     // ------------------------------------------------------------------------------------
 
@@ -475,36 +371,12 @@ namespace dlib
             tensor& dest,
             const tensor& src
         );
-        /*!
-            requires
-                - have_same_dimensions(dest, src) == true
-            ensures
-                - for all valid i:
-                    - #dest.host()[i] == std::tanh(src.host()[i]) 
-                - This function supports in-place operation, i.e. having
-                  is_same_object(dest, src)==true
-        !*/
 
         void tanh_gradient (
             tensor& grad,
             const tensor& dest,
             const tensor& gradient_input
         );
-        /*!
-            requires
-                - have_same_dimensions(dest,gradient_input) == true 
-                - have_same_dimensions(dest,grad) == true 
-                - is_same_object(grad,dest) == false
-            ensures
-                - Recalling that dest is the output of tanh(dest,SRC) for some SRC tensor,
-                  let f(SRC) == dot(gradient_input,dest)
-                - Then this function computes the gradient of f() with respect to SRC and
-                  assigns it to grad.
-                - This function supports in-place operation, i.e. having
-                  is_same_object(grad, gradient_input)==true
-        !*/
-
-
 
     // ------------------------------------------------------------------------------------