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).

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