add pretrained LSTM regression model to app designer

classdef app1 < matlab.apps.AppBase

% Properties that correspond to app components

properties (Access = public)

UIFigure matlab.ui.Figure

Gauge matlab.ui.control.Gauge

GaugeLabel matlab.ui.control.Label

ShowdatagraphButton matlab.ui.control.Button

TDSSpinner matlab.ui.control.Spinner

TDSSpinnerLabel matlab.ui.control.Label

THSpinner matlab.ui.control.Spinner

THSpinnerLabel matlab.ui.control.Label

predictButton matlab.ui.control.Button

UIAxes matlab.ui.control.UIAxes

end

properties (Access = private)

net;

Y_predict;

end

methods (Access = private)

function startupFcn(app)

app.net = load("C:\Users\SABER\AppData\TESTapp\net.mat");

app.net = app.net.net;

end

end

% Callbacks that handle component events

methods (Access = private)

% Button pushed function: predictButton

function predictButtonPushed(app, event)

%assignin('base','TDS', app.TDSSpinner.Value);

%assignin('base','TH', app.THSpinner.Value);

%new = [num2str(app.TDSSpinner.Value); num2str(app.THSpinner.Value)];

%assignin('base','new', new);

app.Y_predict = predict(app.net,input);

end

end

% Component initialization

methods (Access = private)

% Create UIFigure and components

function createComponents(app)

% Create UIFigure and hide until all components are created

app.UIFigure = uifigure('Visible', 'off');

app.UIFigure.Position = [100 100 643 449];

app.UIFigure.Name = 'UI Figure';

% Create UIAxes

app.UIAxes = uiaxes(app.UIFigure);

title(app.UIAxes, 'DATA View')

xlabel(app.UIAxes, 'X')

ylabel(app.UIAxes, 'Y')

app.UIAxes.Position = [1 183 640 241];

% Create predictButton

app.predictButton = uibutton(app.UIFigure, 'push');

app.predictButton.ButtonPushedFcn = createCallbackFcn(app, @predictButtonPushed, true);

app.predictButton.Position = [254 51 100 22];

app.predictButton.Text = 'predict';

% Create THSpinnerLabel

app.THSpinnerLabel = uilabel(app.UIFigure);

app.THSpinnerLabel.HorizontalAlignment = 'right';

app.THSpinnerLabel.Position = [214 119 25 22];

app.THSpinnerLabel.Text = 'TH';

% Create THSpinner

app.THSpinner = uispinner(app.UIFigure);

app.THSpinner.Limits = [0 Inf];

app.THSpinner.HorizontalAlignment = 'center';

app.THSpinner.Position = [254 119 100 22];

% Create TDSSpinnerLabel

app.TDSSpinnerLabel = uilabel(app.UIFigure);

app.TDSSpinnerLabel.HorizontalAlignment = 'right';

app.TDSSpinnerLabel.Position = [212 82 29 22];

app.TDSSpinnerLabel.Text = 'TDS';

% Create TDSSpinner

app.TDSSpinner = uispinner(app.UIFigure);

app.TDSSpinner.Limits = [0 Inf];

app.TDSSpinner.HorizontalAlignment = 'center';

app.TDSSpinner.Position = [256 82 100 22];

% Create ShowdatagraphButton

app.ShowdatagraphButton = uibutton(app.UIFigure, 'push');

app.ShowdatagraphButton.Position = [47 162 106 22];

app.ShowdatagraphButton.Text = 'Show data graph';

% Create GaugeLabel

app.GaugeLabel = uilabel(app.UIFigure);

app.GaugeLabel.HorizontalAlignment = 'center';

app.GaugeLabel.Position = [461 6 42 22];

app.GaugeLabel.Text = 'Gauge';

% Create Gauge

app.Gauge = uigauge(app.UIFigure, 'circular');

app.Gauge.Limits = [0 300];

app.Gauge.ScaleColors = [0 1 0;1 1 0;1 0.4118 0.1608;1 0 0];

app.Gauge.ScaleColorLimits = [0 50;50 100;100 150;150 300];

app.Gauge.Position = [421 43 120 120];

% Show the figure after all components are created

app.UIFigure.Visible = 'on';

end

end

% App creation and deletion

methods (Access = public)

% Construct app

function app = app1

% Create UIFigure and components

createComponents(app)

% Register the app with App Designer

registerApp(app, app.UIFigure)

if nargout == 0

clear app

end

end

% Code that executes before app deletion

function delete(app)

% Delete UIFigure when app is deleted

delete(app.UIFigure)

end

end

end

[Kojiro Saito]

So, what was the issue you got?

Answer 1

[prabhat kumar sharma]

Hi Kouadri,

I assume that you are facing issue with using a pretrained LSTM network inside the app designer based application.

So for Integrating a pre-trained LSTM regression model into a MATLAB App Designer app involves loading the model within the app and using it to make predictions based on user input. The code snippet you've provided an idea about the structure of an App Designer app class. To complete the LSTM integration, You can focus on two main aspects:

1. Loading the Pre-trained Model

2. Making Predictions

You can refer the below code snippet for understanding the basic workflow and outline.

classdef app1 < matlab.apps.AppBase
    % Properties that correspond to app components
    properties (Access = public)
        UIFigure             matlab.ui.Figure
        Gauge                matlab.ui.control.Gauge
        GaugeLabel           matlab.ui.control.Label
        ShowdatagraphButton  matlab.ui.control.Button
        TDSSpinner           matlab.ui.control.Spinner
        TDSSpinnerLabel      matlab.ui.control.Label
        THSpinner            matlab.ui.control.Spinner
        THSpinnerLabel       matlab.ui.control.Label
        predictButton        matlab.ui.control.Button
        UIAxes               matlab.ui.control.UIAxes
    end
    
    properties (Access = private)
        net; % The pre-trained LSTM network
        Y_predict; % The prediction result
    end
    
    methods (Access = private)
        function startupFcn(app)
            % Here you can load the pre-trained LSTM network
            modelStruct = load("C:\Users\SABER\AppData\TESTapp\net.mat");
            app.net = modelStruct.net;
        end
    end
   
    % Callbacks that handle component events
    methods (Access = private)
        % Button pushed function: predictButton
        function predictButtonPushed(app, event)
            % Gather input values
            TDS = app.TDSSpinner.Value;
            TH = app.THSpinner.Value;
            
            % Assuming your model expects a 1x2 input array
            % You might need to adjust preprocessing based on your model's requirements
            input = [TDS, TH];
            
            % Reshape input if necessary for your LSTM model
            % For example, LSTM might expect input as [features, timesteps, samples]
            % input = reshape(input, [1, 1, 2]); % Adjust based on your model's input shape
            
            % Predict using the pre-trained LSTM network
            app.Y_predict = predict(app.net, input);
            
            % Update UI elements based on the prediction
            % For example, updating the Gauge value
            % Ensure to adjust the value to fit within the Gauge limits
            app.Gauge.Value = app.Y_predict;
        end
    end
    
    % Component initialization and other app methods...
    
    % App creation, component creation, and app deletion methods remain unchanged
end

I hope it helps!