Made computed_output an optional argument to backward_inplace() so there is

symmetry between the non-inplace version. This also enables additional
optimizations in the resulting network.

dlib/dnn/core.h

@@ -113,6 +113,15 @@ namespace dlib
             return true;
         }
 
+        template <typename layer_type, typename SUBNET>
+        constexpr auto backward_requires_forward_output(
+            layer_type& layer,
+            SUBNET& sub
+        ) -> typename alwaysbool<decltype(layer.backward_inplace(rt(),sub.get_gradient_input(),rt()))>::type
+        {
+            return false;
+        }
+
         template <typename layer_type, typename SUBNET>
         constexpr auto has_inplace_backward(
             layer_type& layer,
@@ -140,6 +149,15 @@ namespace dlib
             return true;
         }
 
+        template <typename layer_type, typename SUBNET>
+        constexpr auto has_inplace_backward(
+            layer_type& layer,
+            SUBNET& sub
+        ) -> typename alwaysbool<decltype(layer.backward_inplace(rt(),sub.get_gradient_input(),rt()))>::type
+        {
+            return true;
+        }
+
         template <typename layer_type, typename SUBNET>
         constexpr auto is_inplace_layer(
             layer_type& layer,
@@ -194,6 +212,18 @@ namespace dlib
             layer.backward_inplace(computed_output,gradient_input,sub.get_gradient_input(),params_grad);
         }
 
+        template <typename layer_type, typename SUBNET>
+        auto call_layer_backward(
+            layer_type& layer,
+            const tensor& , 
+            const tensor& gradient_input, 
+            SUBNET& sub, 
+            tensor& params_grad
+        ) -> decltype(layer.backward_inplace(gradient_input,sub.get_gradient_input(),params_grad))
+        {
+            layer.backward_inplace(gradient_input,sub.get_gradient_input(),params_grad);
+        }
+
 
         template <typename layer_type, typename SUBNET>
         auto call_layer_forward(

dlib/dnn/layers.h

@@ -559,7 +559,6 @@ namespace dlib
         } 
 
         void backward_inplace(
-            const tensor& /*computed_output*/,
             const tensor& gradient_input, 
             tensor& data_grad, 
             tensor& /*params_grad*/

dlib/dnn/layers_abstract.h

@@ -99,7 +99,7 @@ namespace dlib
                 to document the interface that a layer object must implement.
 
                 The central work of defining a layer is implementing the forward and backward
-                methods.  When you do this you have three options:
+                methods.  When you do this you have four options:
                     - Implement the forward() and backward() methods according to the
                       specification shown below.  Do not implement forward_inplace() and
                       backward_inplace().
@@ -113,6 +113,12 @@ namespace dlib
                       according to the specification shown below.  Do not implement
                       forward() and backward().  These in-place methods allow some types of
                       layers to be implemented more efficiently.
+                    - Implement the forward_inplace() and backward_inplace() methods
+                      according to the specification shown below, except exclude the
+                      computed_output parameter from backward_inplace().  Doing this will
+                      allow dlib to make some layers execute in-place and therefore run a
+                      little faster and use less memory.  Do not implement forward() and
+                      backward().
         !*/
 
     public:
@@ -239,7 +245,7 @@ namespace dlib
         !*/
 
         void backward_inplace(
-            const tensor& computed_output,
+            const tensor& computed_output, // this parameter is optional
             const tensor& gradient_input,
             tensor& data_grad,
             tensor& params_grad
@@ -503,7 +509,7 @@ namespace dlib
 
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
-        void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        void backward_inplace(const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!