Remove duplicated declaration

Author: Cydral

dlib/dnn/layers_abstract.h

@@ -4166,152 +4166,6 @@ namespace dlib
     template <long diag, long num, long den, typename SUBNET>
     using tril_diag = add_layer<tril_<diag, void, num, den>, SUBNET>;
 
-// ----------------------------------------------------------------------------------------
-
-    class positional_encodings_
-    {
-        /*!
-            WHAT THIS OBJECT REPRESENTS
-                This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface.
-                It defines a positional encoding layer that adds position information to
-                the input tensor. This is particularly useful in transformer architectures
-                where the order of the sequence matters.
-
-                The dimensions of the tensors output by this layer are the same as the input
-                tensor dimensions.
-
-                This implementation is based on the positional encoding described in:
-                Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., 
-                Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances 
-                in neural information processing systems (pp. 5998-6008).
-
-                The encoding uses sine and cosine functions of different frequencies:
-                PE(pos, 2i)   = sin(pos / 10000^(2i/d_model))
-                PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))
-                where pos is the position and i is the dimension.
-        !*/
-
-    public:
-
-        positional_encodings_(
-            unsigned long sequence_dim_ = 1,
-            unsigned long embedding_dim_ = 1
-        );
-        /*!
-            ensures
-                - #sequence_dim == sequence_dim_
-                - #embedding_dim == embedding_dim_
-        !*/
-
-        positional_encodings_ (
-            const positional_encodings_& item
-        );
-        /*!
-            ensures
-                - EXAMPLE_COMPUTATIONAL_LAYER_ objects are copy constructable
-        !*/
-
-        positional_encodings_& operator=(
-            const positional_encodings_& item
-        );
-        /*!
-            ensures
-                - EXAMPLE_COMPUTATIONAL_LAYER_ objects are assignable
-        !*/
-
-        template <typename SUBNET>
-        void setup (
-            const SUBNET& sub
-        );
-        /*!
-            requires
-                - SUBNET implements the SUBNET interface defined at the top of this file.
-            ensures
-                - performs any necessary setup for the layer, including the calculation
-                of positional encodings based on the dimensions of the input.
-        !*/
-
-        template <typename SUBNET>
-        void forward(
-            const SUBNET& sub,
-            resizable_tensor& output
-        );
-        /*!
-            requires
-                - SUBNET implements the SUBNET interface defined at the top of this file.
-                - setup() has been called.
-            ensures
-                - Adds the positional encodings to the output of the subnetwork and 
-                stores the results into #output.
-        !*/
-
-        template <typename SUBNET>
-        void backward(
-            const tensor& gradient_input,
-            SUBNET& sub,
-            tensor& params_grad
-        );
-        /*!
-            requires
-                - SUBNET implements the SUBNET interface defined at the top of this file.
-                - setup() has been called.
-                - #params_grad is unused in this layer as there are no learnable parameters.
-            ensures
-                - Computes the gradient of the layer with respect to the input, which
-                is simply the input gradient itself as positional encodings are constant.
-        !*/
-
-        const tensor& get_layer_params(
-        ) const;
-        /*!
-            ensures
-                - returns the parameters that define the behavior of forward().
-                Note: This layer has no learnable parameters, so this returns an empty tensor.
-        !*/
-
-        tensor& get_layer_params(
-        );
-        /*!
-            ensures
-                - returns the parameters that define the behavior of forward().
-                Note: This layer has no learnable parameters, so this returns an empty tensor.
-        !*/
-
-        const tensor& get_positional_encodings(
-        ) const;
-        /*!
-            ensures
-                - returns the computed positional encodings.
-        !*/
-
-        tensor& get_positional_encodings(
-        );
-        /*!
-            ensures
-                - returns the computed positional encodings.
-        !*/
-
-        friend void serialize(const positional_encodings_& item, std::ostream& out);
-        friend void deserialize(positional_encodings_& item, std::istream& in);
-        /*!
-            provides serialization support
-        !*/
-
-        friend std::ostream& operator<<(std::ostream& out, const positional_encodings_& item);
-        /*!
-            print a string describing this layer.
-        !*/
-
-        friend void to_xml(const positional_encodings_& item, std::ostream& out);
-        /*!
-            This function is optional, but required if you want to print your networks with
-            net_to_xml(). It prints a layer as XML.
-        !*/
-    };
-
-    template <typename SUBNET>
-    using positional_encodings = add_layer<positional_encodings_, SUBNET>;
-
 // ----------------------------------------------------------------------------------------
 
 }