Adding specific parameters interface on fc_ layer (#213)

dlib/dnn/layers.h

@@ -1086,6 +1086,30 @@ namespace dlib
             tt::gemm(1,sub.get_gradient_input(), 1,gradient_input,false, w,true);
         }
 
+        alias_tensor_instance get_weights()
+        {
+            return weights(params, 0);
+        }
+
+        alias_tensor_const_instance get_weights() const
+        {
+            return weights(params, 0);
+        }
+
+        alias_tensor_instance get_biases()
+        {
+            static_assert(bias_mode == FC_HAS_BIAS, "This fc_ layer doesn't have a bias vector "
+                "to be retrieved, as per template parameter 'bias_mode'.");
+            return biases(params, weights.size());
+        }
+
+        alias_tensor_const_instance get_biases() const
+        {
+            static_assert(bias_mode == FC_HAS_BIAS, "This fc_ layer doesn't have a bias vector "
+                "to be retrieved, as per template parameter 'bias_mode'.");
+            return biases(params, weights.size());
+        }
+
         const tensor& get_layer_params() const { return params; }
         tensor& get_layer_params() { return params; }
 

dlib/dnn/layers_abstract.h

@@ -539,6 +539,62 @@ namespace dlib
                 - #get_bias_weight_decay_multiplier() == val
         !*/
 
+        alias_tensor_const_instance get_weights(
+        ) const;
+        /*!
+            ensures
+                - returns an alias of get_layer_params(), containing the weights matrix of
+                  the fully connected layer.
+                - #get_weights().num_samples() is the number of elements in input sample,
+                  i.e. sublayer's output's k * nc * nr.
+                - #get_bias().k() == #get_num_outputs()
+                - if get_bias_mode() == FC_HAS_BIAS:
+                    - #get_layer_params().size() == (#get_weights().size() + #get_biases().size())
+                - else:
+                    - #get_layer_params().size() == #get_weights().size()
+        !*/
+
+        alias_tensor_instance get_weights(
+        );
+        /*!
+            ensures
+                - returns an alias of get_layer_params(), containing the weights matrix of
+                  the fully connected layer.
+                - #get_weights().num_samples() is the number of elements in input sample,
+                  i.e. sublayer's output's k * nc * nr.
+                - #get_bias().k() == #get_num_outputs()
+                - if get_bias_mode() == FC_HAS_BIAS:
+                    - #get_layer_params().size() == (#get_weights().size() + #get_biases().size())
+                - else:
+                    - #get_layer_params().size() == #get_weights().size()
+        !*/
+
+        alias_tensor_const_instance get_biases(
+        ) const;
+        /*!
+            requires
+                - #get_bias_mode() == FC_HAS_BIAS
+            ensures
+                - returns an alias of get_layer_params(), containing the bias vector of
+                  the fully connected layer.
+                - #get_bias().num_samples() == 1
+                - #get_bias().k() == #get_num_outputs()
+                - #get_layer_params().size() == (#get_weights().size() + #get_biases().size())
+        !*/
+
+        alias_tensor_instance get_biases(
+        );
+        /*!
+            requires
+                - #get_bias_mode() == FC_HAS_BIAS
+            ensures
+                - returns an alias of get_layer_params(), containing the bias vector of
+                  the fully connected layer.
+                - #get_bias().num_samples() == 1
+                - #get_bias().k() == #get_num_outputs()
+                - #get_layer_params().size() == (#get_weights().size() + #get_biases().size())
+        !*/
+
         template <typename SUBNET> void setup (const SUBNET& sub);
         template <typename SUBNET> void forward(const SUBNET& sub, resizable_tensor& output);
         template <typename SUBNET> void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad);