LSTM with data sequence including NaN values

Hello,

I am using LSTM training network but the training progress is not working and a blank loss plot is coming.

The datasequence is corresponding to a signal in time and it includes NaN values, even the validation dataset.

How to overcome this issue ? is it becuse of the NaN values.

I have to use NaN values to make sure all signals are at the same length for the sequenceInputLayer(numFeatures)

regards,

Mohanad

Answer 1

[Dheeraj Singh]

Too much padding can have negative impact on the network performance. To prevent the training process from adding too much padding, you can sort the training data by sequence length, and choose a mini-batch size such that the sequences in a mini-batch have similar length.

For more information refer to the following link:

https://in.mathworks.com/help/deeplearning/examples/classify-sequence-data-using-lstm-networks.html

[Mohanad Alkhodari]

I have 1 dimension of feature using this. like 1x3400, 1x5000, 1x3200, these are my data.

The sequence length will be 1 in this case right?

I have tried this and it shows an pattern in the training with very bad validation accuracy.,

[Dheeraj Singh]

If you have only one feature, your sequence length would be 3400, 5000, and 3200 for the above data, respectively. It is difficult to comment on the dataset without any description, so if you could provide the dataset or some description about it, it can be more helpful in understanding the issue.