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Commit 5e1dc620 authored by Davis King's avatar Davis King
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updated specs

parent e2a2a26a
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Showing with 39 additions and 3 deletions
dlib/dnn/tensor_tools.h
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......@@ -292,6 +292,7 @@ namespace dlib { namespace tt
- dest.nr()==src.nr() || src.nr()==1
- dest.nc()==src.nc() || src.nc()==1
- dest.k()==src.k() || src.k()==1
- 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
......@@ -316,12 +317,13 @@ namespace dlib { namespace tt
- 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 adds
it to grad.
- Then this function computes the gradient of f() with respect to BIAS and
assigns it to grad.
!*/
// ----------------------------------------------------------------------------------------
......@@ -359,6 +361,8 @@ namespace dlib { namespace tt
requires
- The dimensions of data and filters are the same as the ones given
to the last call to setup().
- is_same_object(output,data) == false
- is_same_object(output,filters) == false
ensures
- convolves filters over data.
- filters contains filters.num_samples() filters.
......@@ -380,6 +384,8 @@ namespace dlib { namespace tt
- data_gradient has the same dimensions as the data object give to the last
call to setup().
- gradient_input has the same dimensions as the output of operator().
- is_same_object(data_gradient,filters) == false
- is_same_object(data_gradient,gradient_input) == false
ensures
- let OUT be the output of (*this)(OUT,data,filters).
- let f(data,filters) == dot(OUT, gradient_input)
......@@ -399,10 +405,12 @@ namespace dlib { namespace tt
- data has the same dimensions as the data object give to the last call to
setup().
- gradient_input has the same dimensions as the output of operator().
- is_same_object(filters_gradient,data) == false
- is_same_object(filters_gradient,gradient_input) == false
ensures
- let OUT be the output of (*this)(OUT,data,filters).
- let f(data,filters) == dot(OUT, gradient_input)
- This function finds the gradient of f() with respect to filters and adds
- This function finds the gradient of f() with respect to filters and assigns
this gradient to filters_gradient.
!*/
......@@ -444,6 +452,8 @@ namespace dlib { namespace tt
const tensor& src
);
/*!
requires
- is_same_object(dest,src) == false
ensures
- #dest.num_samples() == src.num_samples()
- #dest.k() == src.k()
......@@ -468,6 +478,9 @@ namespace dlib { namespace tt
- have_same_dimensions(gradient_input,dest) == true
- have_same_dimensions(src,grad) == true
- dest contains the result of calling (*this)(dest,src)
- is_same_object(grad,gradient_input) == false
- is_same_object(grad,dest) == false
- is_same_object(grad,src) == false
ensures
- Recalling that dest is the output of (*this)(dest,src),
let f(src) == dot(gradient_input,dest)
......@@ -500,6 +513,8 @@ namespace dlib { namespace tt
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 (
......@@ -511,11 +526,14 @@ namespace dlib { namespace tt
requires
- have_same_dimensions(softmaxed_data,gradient_input) == true
- have_same_dimensions(softmaxed_data,grad) == true
- is_same_object(grad, softmaxed_data)==false
ensures
- We interpret softmaxed_data as the output of softmax(softmaxed_data,SRC) for
some SRC tensor. Then let f(SRC) == dot(gradient_input,softmaxed_data) Then
this function computes the gradient of f() with respect to SRC and adds it to
grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
// ----------------------------------------------------------------------------------------
......@@ -529,6 +547,8 @@ namespace dlib { namespace tt
- have_same_dimensions(#dest, src) == true
- 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 (
......@@ -543,11 +563,15 @@ namespace dlib { namespace tt
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling sigmoid(dest,src)
- is_same_object(grad,src) == false
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of sigmoid(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and
adds it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
// ----------------------------------------------------------------------------------------
......@@ -561,6 +585,8 @@ namespace dlib { namespace tt
- have_same_dimensions(#dest, src) == true
- 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 (
......@@ -575,11 +601,15 @@ namespace dlib { namespace tt
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling relu(dest,src)
- is_same_object(grad,src) == false
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of relu(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and adds
it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
// ----------------------------------------------------------------------------------------
......@@ -593,6 +623,8 @@ namespace dlib { namespace tt
- have_same_dimensions(#dest, src) == true
- 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 (
......@@ -607,11 +639,15 @@ namespace dlib { namespace tt
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling tanh(dest,src)
- is_same_object(grad,src) == false
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of tanh(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and adds
it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
// ----------------------------------------------------------------------------------------
......
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