how can I predicate output for u data size 33 by 33 where inputs u for time steps t=10, t=15, and t=20 given as input in Convolutional Neural Network ,a sample code for it .p

 % Define your data and model parameters
 num_samples = 1;  % Replace with the actual number of samples
 num_channels = 1;   % Number of channels (assuming grayscale)
 height = 33;        % Replace with the height of your data (e.g., 33)
 width = 33;         % Replace with the width of your data (e.g., 33)
 num_classes = 3;    % Number of classes in your classification task
 % Generate random input data for U at t=10, t=15, and t=20
 X_train_t10 = rand(height, width, num_channels, num_samples); % Replace with your data generation
 X_train_t15 = rand(height, width, num_channels, num_samples); % Replace with your data generation
 X_train_t20 = rand(height, width, num_channels, num_samples); % Replace with your data generation
 % Concatenate the data for different time steps
 X_train = cat(4, X_train_t10, X_train_t15, X_train_t20);
 % Generate random output data for U at t=25 (classification task)
 y_train = randi([1, num_classes], [1, num_samples]); % Replace with your data generation
 % Define category names
 category_names = {'Category1', 'Category2', 'Category3'};
 % Convert numeric data to categorical using category names
 y_train = categorical(y_train, 1:num_classes, category_names);
 % Create the 2D CNN model
 model = create_cnn_model(height, width, num_channels, num_classes); % Define the function create_cnn_model
 % Compile the model
 model = compile_cnn_model(model, X_train, y_train); % Pass X_train and y_train as arguments
 % Train the model (you can use this random data for training, but it's usually real data)
 model = train_cnn_model(model, X_train, y_train);
 % Make predictions
 X_test = rand(height, width, num_channels, num_samples); % Replace with your data generation for testing
 predictions = predict_cnn_model(model, X_test);
 % Rest of your code remains the same...
 % Rest of your code remains the same...
 % Evaluate the model
 y_test = load_test_target_values(); % Implement this function for test target values
 evaluation_result = evaluate_cnn_model(model, X_test, y_test);
 % Plot the actual vs. predicted data
 figure;
 plot(y_test, predictions, 'o');
 xlabel('Actual U values at t=40');
 ylabel('Predicted U values at t=40');
 title('Actual vs. Predicted U values');
 % Plot 2D images of input data
 figure;
 for i = 1:min(16, num_samples) % Plot the first 16 samples
     subplot(4, 4, i);
     imshow(X_train(:,:,1,i), []);
     title(['Sample ', num2str(i)]);
 end
 sgtitle('Input Data Images');
 % Plot statistical analysis plots (e.g., histograms)
 figure;
 subplot(1, 2, 1);
 histogram(y_test, 'Normalization', 'probability', 'BinWidth', 0.1);
 title('Actual U values at t=40');
 xlabel('U values');
 ylabel('Probability');
 subplot(1, 2, 2);
 histogram(predictions, 'Normalization', 'probability', 'BinWidth', 0.1);
 title('Predicted U values at t=40');
 xlabel('U values');
 ylabel('Probability');
 % Save the model if desired
 save_model(model, 'u_prediction_cnn_2d.mat'); % Define the save_model function
 % Define the load_and_preprocess_data function
 % function X_train = load_and_preprocess_data()
 %     % Load and preprocess your 'X_train' data
 %     % Replace this with your actual data loading and preprocessing
 % end
 % 
 % % Define the load_target_values function
 % function y_train = load_target_values()
 %     % Load or generate your target values (y_train) for training
 %     % Replace this with your actual code to obtain training target data
 %     % For example:
 %     % y_train = load('target_values.mat'); % Load target values from a file
 % end
 % Define the create_cnn_model function
 function model = create_cnn_model(~, ~, ~)
 model = alexnet; % You can customize this architecture
 % Modify the model architecture as needed
 end
 % Define the compile_cnn_model function
 function model = compile_cnn_model(model, X_train, y_train)
 options = trainingOptions('adam', 'MaxEpochs', 10, 'MiniBatchSize', 32, 'Plots', 'training-progress');
 model = trainNetwork(X_train, y_train, model.Layers, options);
 end
 % Define the train_cnn_model function
 function model = train_cnn_model(model, ~, ~)
 % Training code, if any specific function is required
 end
 % Define the predict_cnn_model function
 function predictions = predict_cnn_model(model, X_test)
 predictions = predict(model, X_test);
 end
 % Define the evaluate_cnn_model function
 function evaluation_result = evaluate_cnn_model(~, ~, ~)
 % Evaluation code, if any specific function is required
 end
 % Define the save_model function
 function save_model(model, filename)
 save(filename, 'model');
 end
 % Define the load_and_preprocess_test_data function
 % function X_test = load_and_preprocess_test_data()
 %     % Load and preprocess your 'X_test' data for testing
 %     % Replace this with your actual data loading and preprocessing for testing
 % end
 % 
 % % Define the load_test_target_values function
 % function y_test = load_test_target_values()
 %     % Load or generate your target values (y_test) for testing
 %     % Replace this with your actual code to obtain testing target data
 %     % For example:
 %     % y_test = load('test_target_values.mat'); % Load test target values from a file
 % end

[Walter Roberson]

Please read http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer

And please format your code. Click on the > icon in the CODE section above the editor to create a code area, and paste the code into the code area.

[praveen kumar]

Thanks sir i arranged it kindly i beg you for help in this code sir. Thanks, in advanced.so that i can replace actual data to get required predication i think it is very easy for a person who has knowledge of CNN/NN THANKS SIR. say example, I have lid driven cavity problem I get velocities u and v of both sizes are 2 dimensional (say 33 by 33 example), it is time dependent so I want to use CNN to predict u for t=25 providing u for t= 10,15,20. And I will give actual u at t=25 for comparison and add statistical regressions, loss gain, training plots. Thanks sir. Please kindly help and I would be grateful and appreciative for kindness and support.

[Walter Roberson]

Sorry, I do not know enough about CNN to work on this.

[praveen kumar]

ok sir no problems

Answer 1

[Neha]

Hi Praveen,

I understand that you want to train a CNN model to predict the u component at t=25 based on u components at t=10, t=15, and t=20. I assume that your dataset looks like this:

Input Data:

- Sample 1:

- u component at t=10: 33x33 array

- u component at t=15: 33x33 array

- u component at t=20: 33x33 array

- Sample 2:

- u component at t=10: 33x33 array

- u component at t=15: 33x33 array

- u component at t=20: 33x33 array

...

- Sample N:

- u component at t=10: 33x33 array

- u component at t=15: 33x33 array

- u component at t=20: 33x33 array

Output Data:

- Sample 1:

- u component at t=25: 33x33 array

- Sample 2:

- u component at t=25: 33x33 array

...

- Sample N:

- u component at t=25: 33x33 array

Based on the above assumption, the input size is 33 x 33 x 3 x num_samples (3 channels for u at t=10, t=15, t=20) and the output size is 33 x 33 x 1 x num_samples (1 channel for predicting u at t=25).

After preparing the dataset, you can refer to the following documentation link for more information on training a CNN model for regression:

https://www.mathworks.com/help/deeplearning/ug/train-a-convolutional-neural-network-for-regression.html

Since the response size is 33x33x1, you may need to define a custom layer in the end after the fully connected layer. Please refer to the following documentation link for more information on defining a custom layer:

https://www.mathworks.com/help/deeplearning/ug/define-custom-deep-learning-layers.html

Hope this helps!

[praveen kumar]

thanks for understanding my dought madam small clarification thet yOu mentioned sample 1:

- u component at t=10: 33x33 array

- u component at t=15: 33x33 array

- u component at t=20: 33x33 array but i consider each array as same madam so 3 samples to predict u component at t=25 : 33x33 array thanks madam if it 120 by 120 data size each then ? if possible get code madam if i have 20 such date sets each is time dependent so to predict next time dependent nedent data.and in addition,general how many such data sets required to get desired aaccuracy(CNN or RNN works?)

Thanks in advance