Input Size Mismatch on LSTM model development

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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
layers = [layers
% 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, ...
% 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];
Error using trainNetwork (line 184)
Invalid network.
Caused by:
Layer 2: Input size mismatch. LSTM layers must have scalar input size, but input size (4×24×1) was received. Try
using a flatten layer before the LSTM layer.
when i first used sequenceInputLayer([num_features num_time_steps],'Name', 'input'), it displayed
Error using sequenceInputLayer (line 131)
Invalid argument at position 1. Expected input to be a scalar, 3 element vector or 4 element vector.'
then I included flattenedLayer before the LSTM layer, it displayed
;Error using trainNetwork (line 184)
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size
for that dimension.
Caused by:
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate
size for that dimension.

Accepted Answer

Cris LaPierre
Cris LaPierre on 2 Mar 2024
The input to the lstmLayer is the number of features (a scaler), not the size of the input.

More Answers (0)

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