MesoNet: wrapper training loop and checkpoint review

wrapper loop check: normalize, encode, run, count, clear, rest. reshape return + capture_period condition look worth slowing down for

entry point: base_model/main.py → evalInstance.train(num_examples, target_weight, single_run_time, rest_time, capture_period). Each iteration normalizes input weights, encodes one image, supplies two spike streams, runs the presentation interval, records activity, clears the input streams and runs a rest interval.

artifacts: save_connections writes synapse.w as a NumPy array under weights/; the wrapper tracks a sample-by-neuron result_monitor and input labels. Those arrays are needed to reproduce the readout, not only the final weight file.

review flags visible in the printed listing:

• temp_conn.reshape(784, self.n_neurons) is called without assigning its return before two-dimensional indexing; confirm the actual array shape.
• if (j % capture_period and j > 0) saves on nonmultiples of the interval, rather than the usual “every N iterations” condition.
• The label expression indexes self.trainset[1][...][0]; confirm the label array shape from keras.datasets.mnist.load_data() in the actual environment.
• previous_spikes is reset after the rest interval, so explicitly decide whether rest-period activity belongs in the sample response.

These are review questions arising from an archived prototype listing, not claims that the final experiment used this exact broken or incomplete code. Use the executable revision to resolve them.

Training loop and checkpoints