Error using trainNetwork (line 184) Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences

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Jide Taiwo on 28 Mar 2024 at 11:09

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https://in.mathworks.com/matlabcentral/answers/2100086-error-using-trainnetwork-line-184-invalid-training-data-responses-must-be-a-matrix-of-numeric-res

Commented: Cris LaPierre on 8 Apr 2024 at 13:17

Good day Sir

Given a 3 year training data to train LSTM model, 1 year validation data to fine tune hyperparameters for optimization and regularization, and test data to evaluate the performance of the LSTM model, in which training, validation and test data was reshaped in the format as shown below.

input data:

X_train_reshaped = 1095x1 cell (4x24 double each);
X_val_reshaped = 366x1 cell (4x24 double each);
X_test_reshaped = 365x1 cell (4x24 double each)

Traget or output data:

y_train_cell = 1095x1 cell (1x24 double each);
y_val_cell = 366x1 cell (1x24 double each);
y_test_cell = 365x1 cell (1x24 double each)

% Reshape training data for LSTM input and output
num_features = size(X_train, 2);
num_samples_per_day = 24; % Number of hours in a day
num_days_train = size(X_train, 1) / num_samples_per_day;
X_train_reshaped = cell(num_days_train, 1);
y_train_cell = cell(num_days_train, 1);
% Repeat Reshape for validation and test data
% Apply grid search for hyperparameters
% Loop through hyperparameters
for num_layer = num_layers
    for hidden_unit = hidden_units
        for hidden_layer = hidden_layers
            for learning_rate = learning_rates
                for optimizer = optimizers
                    for dropout_rate = dropout_rates
                        for numEpochs = num_epochs
                            fprintf('Training LSTM model with num_layers=%d, hidden_units=%d, learning_rate=%.4f, optimizer=%s, dropout_rate=%.2f, num_epochs=%d\n', num_layer, hidden_unit, learning_rate, optimizer{1}, dropout_rate, numEpochs);
                            
                            % Build LSTM model
                            layers = [
                                sequenceInputLayer(num_features)
                                ];
                            for i = 1:hidden_layer
                                layers = [
                                    layers
                                    lstmLayer(hidden_unit, 'OutputMode', 'sequence')
                                    ];
                                if dropout_rate > 0
                                    layers = [
                                        layers
                                        dropoutLayer(dropout_rate)
                                        ];
                                end
                            end
                            % Output layer configuration
                            layers = [
                                layers
                                fullyConnectedLayer(num_samples_per_day)
                                regressionLayer
                                ];
                            
                            % Specify training options
                            options = trainingOptions(optimizer{1}, ...
                                'MaxEpochs', numEpochs, ...
                                'InitialLearnRate', learning_rate, ...
                                'GradientThreshold', 1, ...
                                'ExecutionEnvironment', 'cpu', ...
                                'Verbose', false, ...
                                'Plots', 'training-progress');
                            
                            % Train the LSTM model
                            net = trainNetwork(X_train_reshaped, y_train_cell, layers, options);
                            
                            % Predict on validation set
                            y_pred = predict(net, X_val_reshaped);
                            
                            % Evaluate the model
                            rmse = sqrt(mean((cell2mat(y_pred) - cell2mat(y_val_cell)).^2));
                            mae = mean(abs(cell2mat(y_pred) - cell2mat(y_val_cell)));
                            
                            % Check if current model is the best so far
                            if rmse < best_rmse
                                best_rmse = rmse;
                                best_hyperparameters.hidden_units = hidden_unit;
                                best_hyperparameters.num_layers = hidden_layer;
                                best_hyperparameters.learning_rate = learning_rate;
                                best_hyperparameters.optimizer = optimizer{1};
                                best_hyperparameters.dropout_rate = dropout_rate;
                                best_hyperparameters.num_epochs = numEpochs;
                                best_mae = mae; % Update best MAE
                            end
                        end
                    end
                end
            end
        end
    end
end

Training LSTM model with num_layers=1, hidden_units=12, learning_rate=0.0010, optimizer=adam, dropout_rate=0.10, num_epochs=50

Error using trainNetwork (line 184)

Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences, where N is the number of sequences. The feature dimension of all sequences must be the same.

2 Comments
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Ben on 8 Apr 2024 at 12:34

I notice fullyConnectedLayer(num_samples_per_day) where num_samples_per_day = 24 is the "sequence length" for each input/output sequence. In particular you say each of y_train_cell{i} is 1x24, i.e. a sequence with 1 feature and 24 samples. For that you need to use fullyConnectedLayer(1) at the end of the network.

Besides that, perhaps check that num_features is 4, and that all the entries X_train_reshaped{i} have size 4x24, and X_val_reshaped{i}. Similar for the output data. It could be easy for an empty array to accidentally get into one of these cells during your pre-processing.