I was trying to reshape my data array from 2D to 3D to be able to be loaded to LSTM for training, i encountered the dimensionality problem in the beginning when I could not reshape my data into ```[samples, time steps, features]``` (in my case, ```[2097 1000 1]```, since the MATLAB always automatically turns it into ```[[2097 1000]```.
The reshaping method I used is
num_samples = floor(length(final_filtered_signal) / sequence_length);
X = reshape(final_filtered_signal(1:num_samples * sequence_length), [num_samples, sequence_length, num_features]);
My initial layer structure is as following:
sequenceInputLayer(num_features, 'MinLength', sequence_length)
convolution1dLayer(3, 64, 'Padding', 'same')
maxPooling1dLayer(2, 'Stride', 2)
convolution1dLayer(3, 128, 'Padding', 'same')
maxPooling1dLayer(2, 'Stride', 2)
convolution1dLayer(3, 256, 'Padding', 'same')
lstmLayer(100, 'OutputMode', 'last')
With this layer set-up I run into error:
The training sequences are of feature dimension 1677 but the input layer expects sequences of feature dimension 1.
Error in noise_kernel_deconvolve (line 260)
net = trainNetwork(X_train, Y_train, layers, options);
Since I keep run into dimensionality problem, I changed the layer to :
sequenceInputLayer([sequence_length, num_features])
convolution1dLayer(3, 64, 'Padding', 'same')
maxPooling1dLayer(2, 'Stride', 2)
convolution1dLayer(3, 128, 'Padding', 'same')
maxPooling1dLayer(2, 'Stride', 2)
convolution1dLayer(3, 256, 'Padding', 'same')
lstmLayer(100, 'OutputMode', 'last')
basically changing the ```sequenceInputLayer(num_features, 'MinLength', sequence_length)``` to accept the 2D matrix format of my input data. After this I encountered new error:
The training sequences are of feature dimension 1677 1000 but the input layer expects sequences of feature dimension 1000 1.
Error in noise_kernel_deconvolve (line 230)
net = trainNetwork(X_train, Y_train, layers, options);
I am now a bit confused how does LSTM require the input data and the layer to be structured... since it seems doesn't matter if I have 2D input X or 3D input ```X```, I would always encounter the dimension problem when I start running the
net = trainNetwork(X_train, Y_train, layers, options);
