% % Inputs: % layer - Output layer % Y – Predictions made by network % T – Training targets % % Output: % loss - Loss between Y and T % Layer forward loss function goes here.
% Return the loss between the predictions Y and the training % targets T. end function loss = forwardLoss(layer,Y,T) end methods function layer = myRegressionLayer() % & % (Optional) properties % (Optional) Layer properties. end end endĬlassdef myRegressionLayer < %. % - For layers with multiple state parameters, replace dLdSin % and dLdSout with dLdSin1.,dLdSinK and % dLdSout1.,dldSoutK, respectively, where K is the number % of state parameters. % - For layers with multiple learnable parameters, replace % dLdW with dLdW1.,dLdWP, where P is the number of % learnable parameters. % - For layers with multiple outputs, replace Z and dlZ with % Z1.,ZM and dLdZ.,dLdZM, respectively, where M is the % number of outputs. % - For layers with multiple inputs, replace X and dLdX with % X1.,XN and dLdX1.,dLdXN, respectively, where N is % the number of inputs. % % Inputs: % layer - Layer to backward propagate through % X - Layer input data % Z - Layer output data % dLdZ - Derivative of loss with respect to layer % output % dLdSout - (Optional) Derivative of loss with respect % to state output % memory - Memory value from forward function % Outputs: % dLdX - Derivative of loss with respect to layer input % dLdW - (Optional) Derivative of loss with respect to % learnable parameter % dLdSin - (Optional) Derivative of loss with respect to % state input % % - For layers with state parameters, the backward syntax must % include both dLdSout and dLdSin, or neither. % (Optional) Backward propagate the derivative of the loss % function through the layer. end function = backward(layer,X,Z,dLdZ,dLdSout,memory)
% - For layers with multiple state parameters, replace state % with state1.,stateK, where K is the number of state % parameters. % - For layers with multiple outputs, replace Z with % Z1.,ZM, where M is the number of outputs. % % Inputs: % layer - Layer to forward propagate through % X - Layer input data % Outputs: % Z - Output of layer forward function % state - (Optional) Updated layer state % memory - (Optional) Memory value for custom backward % function % % - For layers with multiple inputs, replace X with X1.,XN, % where N is the number of inputs. % (Optional) Forward input data through the layer at training % time and output the result, the updated state, and a memory % value.
% % Inputs: % layer - Layer to forward propagate through % X - Input data % Outputs: % Z - Output of layer forward function % state - (Optional) Updated layer state % % - For layers with multiple inputs, replace X with X1.,XN, % where N is the number of inputs. % Forward input data through the layer at prediction time and % output the result and updated state. % Define layer initialization function here. % % Inputs: % layer - Layer to initialize % layout - Data layout, specified as a networkDataLayout % object % % Outputs: % layer - Initialized layer % % - For layers with multiple inputs, replace layout with % layout1.,layoutN, where N is the number of inputs. % (Optional) Initialize layer learnable and state parameters. end function layer = initialize(layer,layout) % Define layer constructor function here. % This function must have the same name as the class. % Declare nested networks with learnable and state parameters here. % (Optional) Nested dlnetwork objects with both learnable % parameters and state parameters. % (Optional) % & % (Optional) properties % (Optional) Layer properties. You can use the following templates to define new layers.Ĭlassdef myLayer < %. Specify a loss function, see Define Custom Regression Output Layer.
#Define make a pass at how to
Specify a loss function, see Define Custom Classification Output Layer.ĭefine a custom regression output layer and specify a lossĮxample showing how to define a custom regression output layer and See Define Custom Deep Learning Layer with Multiple Inputs.ĭefine a custom classification output layer and specify a lossįor more information, see Define Custom Deep Learning Output Layers.Įxample showing how to define a custom classification output layer and ForĪn example showing how to define a custom layer with multiple inputs, See Define Custom Deep Learning Layer with Learnable Parameters. Define a custom deep learning layer and specify optional learnableĭefine Custom Deep Learning Intermediate Layers.Įxample showing how to define a custom layer with learnable parameters,
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