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Entry  Thu May 23 15:37:30 2019, Milind, Update, Cameras, Simulation enhancements and performance of contour detection 6x
    Reply  Sat May 25 20:29:08 2019, Milind, Update, Cameras, Simulation enhancements and performance of contour detection residue_normalised_x.pdfresidue_normalised_x.pdfresidue_normalised_x.pdfpredicted_motion_x.pdfnormalised_comparison_y.pdf
       Reply  Wed Jun 12 22:02:04 2019, Milind, Update, Cameras, Simulation enhancements  simulated_motion0.mp4simulated_motion0.mp4simulated_motion0.mp4
          Reply  Mon Jun 17 14:36:13 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
             Reply  Tue Jun 18 22:54:59 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
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                      Reply  Thu Jun 27 20:48:22 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking readme.txtframe0.pdfLearning_curves.pngMotion.png
                         Reply  Thu Jul 4 18:19:08 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking Motion.pdfError.pdfLearning_curves.pdf
                            Reply  Mon Jul 8 17:52:30 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking Motion.pdf
                               Reply  Tue Jul 9 22:13:26 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking predicted_motion_first.pdfpcdev5_time.png
                                  Reply  Wed Jul 10 22:32:38 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
                               Reply  Mon Jul 15 14:09:07 2019, Milind, Update, Cameras, CNN LSTM for beam tracking cnn-lstm.pngfft_yaw.pdfyaw_motion.pdf
                                  Reply  Fri Jul 19 16:47:06 2019, Milind, Update, Cameras, CNNs for beam tracking || Analysis of results 7x
                                     Reply  Sat Jul 20 12:16:39 2019, gautam, Update, Cameras, CNNs for beam tracking || Analysis of results 
                                        Reply  Sat Jul 20 14:43:45 2019, Milind, Update, Cameras, CNNs for beam tracking || Analysis of results frame0.pdfsubplot_yaw_test.pdfintensity_histogram.mp4network2.pdf
                                           Reply  Wed Jul 24 20:05:47 2019, Milind, Update, Cameras, CNNs for beam tracking || Tales of desperation saturation_percentage.pdf
                   Reply  Thu Jul 25 00:26:47 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking centroid.pdfsubplot_yaw_test.pdf
          Reply  Mon Jun 17 22:19:04 2019, Milind, Update, Computer Scripts / Programs, PMC autolocker 
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          Reply  Tue Jun 25 23:52:37 2019, Milind, Update, Cameras, Simulation enhancements  
             Reply  Mon Jul 1 20:11:34 2019, Milind, Update, Cameras, Simulation enhancements  
Message ID: 14760     Entry time: Mon Jul 15 14:09:07 2019     In reply to: 14734     Reply to this: 14779
Author: Milind 
Type: Update 
Category: Cameras 
Subject: CNN LSTM for beam tracking 

I've set up network with a CNN encoder (front end) feeding into a single LSTM cell followed by the output layer (see attachment #1). The network requires significantly more memory than the previous ones. It takes around 30s for one epoch of training. Attached are the predicted yaw motion and the fft of the same. The FFT looks rather curious. I still haven't done any tuning and these are only the preliminary results.

Quote:

 Rana also suggested I try LSTMs today. I'll maybe code it up tomorrow. What I have in mind- A conv layer encoder, flatten, followed by an LSTM layer (why not plain RNNs? well LSTMs handle vanishing gradients, so why the hassle).

Well, what about the previous conv nets?

What I did:

  1. Extensive tuning - of learning rate, batch size, dropout ratio, input size using a grid search
  2. Trained each network for 75 epochs and obtained weights, predicted motion and corresponding FFT, error etc.

What I observed:

  1. Loss curves look okay, validation loss isn't going up, so I don't think overfitting is the issue
  2. Training for over (even) 75 epochs seems to be pointless.

What I think is going wrong:

  1. Input size- relatively large input size: 350 x 350. Here, the input image size seems to be 128 x 128.
  2. Inadequate pre-processing.
    1. I have not applied any filters/blurs etc. to the frames.
    2. I have also not tried dimensionality reduction techniques such as PCA

What I will try now:

  1. Collect new data: with smaller amplitudes and different frequencies
  2. Tune the LSTM network for the data I have
  3. Try new CNN architectures with more aggressive max pooling and fewer parameters
  4. Ensembling the models (see this and this). Right now, I have multiple models trained either with same architecture and different hyperparameters or with different architectures. As a first pass, I intend to average the predictions of all the models and see if that improves performance.
Attachment 1: cnn-lstm.png  79 kB  Uploaded Mon Jul 15 15:13:34 2019  | Hide | Hide all
cnn-lstm.png
Attachment 2: fft_yaw.pdf  12 kB  Uploaded Mon Jul 15 15:25:28 2019  | Hide | Hide all
fft_yaw.pdf
Attachment 3: yaw_motion.pdf  20 kB  Uploaded Mon Jul 15 15:25:28 2019  | Hide | Hide all
yaw_motion.pdf
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