Proper datashape and model architecture for recognizing highs and lows in a chart
I am using a Keras LSTM model to try to pinpoint the highs and lows (relative high points and low points) in a chart (I need the actual coordinates to those highs and lows, not just an image). The training process has no errors in it but the prediction output is completely irrelevant to the training output.
what I've done so far is, I created the output data by feeding the input data to an algorithm from Scipy, argrelextrema. For personal reasons I want to be able to replicate that behavior using Machine learning.
Each individual input is an array of 500 values. If I print an individual input, I'll get:
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> 1.
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER>
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER>
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER>
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> 0.9991296
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER>
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER>
<PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER> <PHONE_NUMBER>
<PHONE_NUMBER> 0. 856