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Entry  Thu May 23 15:37:30 2019, Milind, Update, Cameras, Simulation enhancements and performance of contour detection 6x
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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
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Message ID: 14706     Entry time: Thu Jun 27 20:48:22 2019     In reply to: 14697     Reply to this: 14726
Author: Milind 
Type: Update 
Category: Cameras 
Subject: Convolutional neural networks for beam tracking 

And finally, a network is trained!

Result summary (TLDR :-P) : No memory was used. Model trained. Results were garbage. Will tune hyperparameters now. Code pushed to github.

 

More details of the experiment:

Aim:

  1. To train a network to check that training occurs and get a feel for what the learning might be like.
  2. To set up the necessary framework to perform mulitple experiments and record results in a manner facilitating comparison.
  3. To track beam spot motion.

What I did:

  1. Set up a network that learns a framewise mapping as described in here.
  2. Training data: 0.9 x 1791 frames. Validation data: 0.1 x 1791 frames. Test data (only prediction): all the 1791 frames
  3. Hyperparameters: Attachment #1
  4. Did no tuning of hyperparameters.
  5. Compiled and fit the models and saved the results.

 

What I saw

  1. Attachment #2: data fed to the network after pre-processing - median blur + crop
  2. Attachment #3: learning curves.
  3. Attachment #4: true and predicted motion. Nothing great.

What I think is going wrong-

  1. No hyperparameter tuning. This was only a first pass but is being reported as it will form the basis of all future experiments.
  2. Too little data.
  3. Maybe wrong architecture.

Well, what now?

  1. Tune hyperparmeters (try to get the network to overfit on the data and then test on that. We'll then know for sure that all we probably need is more data?)
  2. Currently the network has around 200k parameters. Maybe reduce that.
  3. Set up a network that takes as input (one example corresponding to one forward pass)  a bunch of frames and predicts a vector of position values that can be used as continuous data).
Quote:

I got to speak to Gabriele about the project today and he suggested that if I am using Rana's memory based approach, then I had better be careful to ensure that the network does not falsely learn to predict a sinusoid at all points in time and that if I use the frame wise approach I try to somehow incorporate the fact that certain magnitudes and frequencies of motion are simply not physically possible. Something that Rana and Gautam emphasized as well.

 
Quote:
 
  1. Network training for beam spot tracking: I will begin training the convolutional network with the data pre-processed as described above. I will also simultaneously prepare data acquired from the GigE and train networks on that. Note: I planned to experiment with framewize predictions and hence did some of the work described above. However, I will restrict the number of experiments on that and perform more of those that use 3D convolution. Rana also pointed out that it would be interesting to have the network output uncertainity in the predictions. I am not sure how this can be done, but I will look into it.

 

 

Attachment 1: readme.txt  396 Bytes  Uploaded Thu Jun 27 22:33:59 2019  | Hide | Hide all
Experiment file: train.py
batch_size: 32
dropout_probability: 0.8
eta: 0.0001
filter_size: 19
filter_type: median
initializer: xavier
num_epochs: 50
activation_function: relu
dense_layer_units: 64
... 10 more lines ...
Attachment 2: frame0.pdf  16 kB  Uploaded Thu Jun 27 22:35:07 2019  | Hide | Hide all
frame0.pdf
Attachment 3: Learning_curves.png  66 kB  Uploaded Thu Jun 27 22:35:55 2019  | Hide | Hide all
Learning_curves.png
Attachment 4: Motion.png  121 kB  Uploaded Thu Jun 27 22:38:10 2019  | Hide | Hide all
Motion.png
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