Hey all, I’d like to dedicate this thread for discussing some TP implementation and practical questions, namely those associated with the introductory file to the TP, hello-tp.py.
Below is the print out of a TP with 50 columns, 1 cell per column being trained as described in the py file for 2 iterations on the sequence A->B->C->D->E. Each pattern is fed directly into the TP as an active network state. I’ve been playing around with the configurations and have a few questions. 1) Why are there no predictive states being seen for the first training pass (i.e. seeing the entire sequence once)? Even if activationThreshold and minThreshold are set sufficiently low to make segments sensitive, no lateral activation happens. Are cells initialized with no segments? 2) if segments are created during initialization, how is their connectivity to the cells of the region configured? How are permanence values allocated? Same as proximal synapses in the TP? 3) in the second training pass, we go from no predictive cells to perfectly predictive cells associated with the next character. I would typically expect the network to show scattered predictive cells before it hones in on the right prediction (consecutive 10 on-bits in this example). Why the abrupt shift in predictive behavior? Is this related to getBestMatchingCell()? 4) finally, the printCells() function outputs the following. Can you please explain what each entry means? Column 41 Cell 0 : 1 segment(s) Seg #0 ID:41 True 0.2000000 ( 3/3 ) 0 [30,0]1.00 [31,0]1.00 [32,0]1.00 [33,0]1.00 [34,0]1.00 [35,0]1.00 [36,0]1.00 [37,0]1.00 [38,0]1.00 [39,0]1.00 Thanks, Nick ————————————— PRINT OUT———————————— ————— All the active and predicted cells: Inference Active state 1111111111 0000000000 0000000000 0000000000 0000000000 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 0000000000 All the active and predicted cells: Inference Active state 0000000000 1111111111 0000000000 0000000000 0000000000 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 0000000000 All the active and predicted cells: Inference Active state 0000000000 0000000000 1111111111 0000000000 0000000000 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 0000000000 All the active and predicted cells: Inference Active state 0000000000 0000000000 0000000000 1111111111 0000000000 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 0000000000 All the active and predicted cells: Inference Active state 0000000000 0000000000 0000000000 0000000000 1111111111 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 0000000000 ############ Training Pass #1 Complete ############ All the active and predicted cells: Inference Active state 1111111111 0000000000 0000000000 0000000000 0000000000 Inference Predicted state 0000000000 1111111111 0000000000 0000000000 0000000000 All the active and predicted cells: Inference Active state 0000000000 1111111111 0000000000 0000000000 0000000000 Inference Predicted state 0000000000 0000000000 1111111111 0000000000 0000000000 All the active and predicted cells: Inference Active state 0000000000 0000000000 1111111111 0000000000 0000000000 Inference Predicted state 0000000000 0000000000 0000000000 1111111111 0000000000 All the active and predicted cells: Inference Active state 0000000000 0000000000 0000000000 1111111111 0000000000 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 1111111111 All the active and predicted cells: Inference Active state 0000000000 0000000000 0000000000 0000000000 1111111111 Inference Predicted state 0000000000 0000000000 0000000000 0000000000 0000000000 ############ Training Pass #2 Complete ############
