Input Size Mismatch on LSTM model development

Question

Babajide Osonuga 2024-3-1

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2089256-input-size-mismatch-on-lstm-model-development

评论： Babajide Osonuga 2024-3-2

Good day,

Given that the input to the LSTM has three dimensions: num_features, num_time_steps as 24, and num_training_samples. i intend to predict a 24 hours day-ahead GHI. X_train_3D = 4xs4x1461; X_test_3D = 4x24x365; y_train_reshaped = 24x1x1461.

Below are the codes:

best_rmse = inf;

best_mae = inf;

best_r2 = -inf;;

best_hyperparameters = [];;

% Perform grid search

for optimizer_val = optimizers

for num_lstm_layers_val = num_lstm_layers

for num_hidden_layers_val = num_hidden_layers

for hidden_units_val = hidden_units

for learning_rate_val = learning_rates

for num_epochs_val = num_epochs

for dropout_rate_val = dropout_rates

for batch_size_val = batch_sizes

% Build LSTM model

layers = [ ...

sequenceInputLayer([num_features num_time_steps 1],'Name', 'input')

];

for i = 1:num_lstm_layers_val

layers = [layers

lstmLayer(hidden_units_val,'OutputMode','sequence')

dropoutLayer(dropout_rate_val)

];

end

layers = [layers

fullyConnectedLayer(num_time_steps)

regressionLayer];

% Specify options for training

options = trainingOptions(optimizer_val{1}, ...

'LearnRateSchedule','piecewise', ...

'LearnRateDropPeriod',10, ...

'LearnRateDropFactor',0.9, ...

'InitialLearnRate',learning_rate_val, ...

'MaxEpochs',num_epochs_val, ...

'MiniBatchSize',batch_size_val, ...

'SequenceLength','longest', ...

'Shuffle','never', ...

'Verbose',0, ...

'Plots','training-progress');

% Train LSTM model

net = trainNetwork(X_train_3D, y_train_3D, layers, options);

% Make predictions on test set

y_pred = predict(net, X_test_3D);

% Denormalize predicted GHI values

y_pred_denormalized = y_pred * range(features.GHI) + min(features.GHI);

% Compute evaluation metrics

rmse = sqrt(mean((y_test - y_pred_denormalized).^2));

mae = mean(abs(y_test - y_pred_denormalized));

r2 = 1 - sum((y_test - y_pred_denormalized).^2) / sum((y_test - mean(y_test)).^2);

% Update best hyperparameters if metrics improve

if rmse < best_rmse

best_rmse = rmse;

best_mae = mae;

best_r2 = r2;

best_hyperparameters = [optimizer_val{1}, num_lstm_layers_val, num_hidden_layers_val, hidden_units_val, learning_rate_val, num_epochs_val, dropout_rate_val, batch_size_val];

end