1- Numpy 2- tqdm 3- termcolor 4- matplotlib 5-pickle
The code provided herein is implemented in TF1.12 and compatible with Eager mode. To run the code, simply run the estimate.py with specifying the following arguments directly in terminal.
- '-n', '--network_size', default=None, type=int, help= '(9: RES9E, 16:RES16E) -- default set to: RES9ER'
- '-b', '--batch_size',default=8, type=int,help='(between 1<=b<=215 (smallest log size=215). depends on GPU/CPU ram capacity -- default set to: 8 '
- '-t', '--train_mode', default=0, type=int, help='0: No training, 1: continue with existing checkpoint, 2: train from scratch) -- set to default: 0 '
- '-e', '--training_epochs', default=10, type=int, help='-- default set to 10'
- '-v', '--visualize', default=1, type=int, help='(0, No visualization, 1: validate and visualize log signal) -- defualt set to: 1 '
- '-l', '--logs', default=2, type=int, help='(Logs to visualize--> 0: train logs, 1: validate logs, 2: test logs) -- defualt set to: 2 '
This runs in training mode with existing checkpoints then visualize the predicted signal of the test log/s
- python3 estimate.py -t 1
- Test accuracy of test log using RES9_ER should give an accuracy of 99.45% and if trained with option 1 for 1 epoch (i.e. python3 estimate.py -t 1 -e 1), accuracy can top 99.67%. This attached code is tested with TF1.12 and compabatible with linux and windows machines. Also, make sure to include/install all TF dependencies as per used in the code.
- When training, checkpoints for certain accuracies are automatically saved in generated_checkpoints folder inside the main checkpoints folder
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Gradcam code is provided separately in the Gradcam_visualization folder, navigate to the Readme file in that folder for instructions on usage.
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Live Gradcam - a fun feature to lively visualize predictions is available in Live_cam_visualization folder.
Muhammad K.A. Hamdan