exportNetworkToTensorFlow

net = 
  dlnetwork with properties:

         Layers: [68×1 nnet.cnn.layer.Layer]
    Connections: [75×2 table]
     Learnables: [52×3 table]
          State: [0×3 table]
     InputNames: {'data'}
    OutputNames: {'prob_flatten'}
    Initialized: 1

  View summary with summary.

classdef sreluLayer < nnet.layer.Layer ...
        & nnet.layer.Acceleratable
    % Example custom SReLU layer.

    properties (Learnable)
        % Layer learnable parameters.

        LeftSlope
        RightSlope
        LeftThreshold
        RightThreshold
    end

    methods
        function layer = sreluLayer(args)
            % layer = sreluLayer creates a SReLU layer.
            %
            % layer = sreluLayer(Name=name) also specifies the layer name.

            arguments
                args.Name = "";
            end

            % Set layer name.
            layer.Name = args.Name;

            % Set layer description.
            layer.Description = "SReLU";
        end

        function layer = initialize(layer,layout)
            % layer = initialize(layer,layout) initializes the learnable
            % parameters of the layer for the specified input layout.

            % Find number of channels.
            idx = finddim(layout,"C");
            numChannels = layout.Size(idx);

            % Initialize empty learnable parameters.
            sz = ones(1,numel(layout.Size));
            sz(idx) = numChannels;

            if isempty(layer.LeftSlope)
                layer.LeftSlope = rand(sz);
            end

            if isempty(layer.RightSlope)
                layer.RightSlope = rand(sz);
            end

            if isempty(layer.LeftThreshold)
                layer.LeftThreshold = rand(sz);
            end

            if isempty(layer.RightThreshold)
                layer.RightThreshold = rand(sz);
            end
        end

        function Y = predict(layer, X)
            % Y = predict(layer, X) forwards the input data X through the
            % layer and outputs the result Y.

            tl = layer.LeftThreshold;
            al = layer.LeftSlope;
            tr = layer.RightThreshold;
            ar = layer.RightSlope;

            Y = (X <= tl) .* (tl + al.*(X-tl)) ...
                + ((tl < X) & (X < tr)) .* X ...
                + (tr <= X) .* (tr + ar.*(X-tr));
        end
    end
end

Warning: Layer "srelu": Layer class "sreluLayer" was exported into an incomplete TensorFlow custom layer file. The custom layer definition must be completed or the file must be replaced before the model can be loaded into TensorFlow.

#    This file was created by
#    MATLAB Deep Learning Toolbox Converter for TensorFlow Models.
#    05-Sep-2024 19:12:28

import tensorflow as tf
import sys     # Remove this line after completing the layer definition.

class sreluLayer(tf.keras.layers.Layer):
    # Add any additional layer hyperparameters to the constructor's
    # argument list below.
    def __init__(self, LeftSlope_Shape_=None, RightSlope_Shape_=None, LeftThreshold_Shape_=None, RightThreshold_Shape_=None, name=None):
        super(sreluLayer, self).__init__(name=name)
        # Learnable parameters: These have been exported from MATLAB and will be loaded automatically from the weight file:
        self.LeftSlope = tf.Variable(name="LeftSlope", initial_value=tf.zeros(LeftSlope_Shape_), trainable=True)
        self.RightSlope = tf.Variable(name="RightSlope", initial_value=tf.zeros(RightSlope_Shape_), trainable=True)
        self.LeftThreshold = tf.Variable(name="LeftThreshold", initial_value=tf.zeros(LeftThreshold_Shape_), trainable=True)
        self.RightThreshold = tf.Variable(name="RightThreshold", initial_value=tf.zeros(RightThreshold_Shape_), trainable=True)

    def call(self, input1):
        # Add code to implement the layer's forward pass here.
        # The input tensor format(s) are: BSSC
        # The output tensor format(s) are: BSSC
        # where B=batch, C=channels, T=time, S=spatial(in order of height, width, depth,...)

        # Remove the following 3 lines after completing the custom layer definition:
        print("Warning: load_model(): Before you can load the model, you must complete the definition of custom layer sreluLayer in the customLayers folder.")
        print("Exiting...")
        sys.exit("See the warning message above.")

        return output1