trin_err, aux_trin_err, test_err, aux_test_err)) sys.stdout.forceflush() if saved_file_name: os.remove(saved_file_name) saved_file_name = pickle_file_name.format(test_err) with open(saved_file_name, 'wb') as pkl_file: pickle.dump(nn.get_init_params(), pkl_file, -1) ############################################ Training Loop print("Training ...") print("Epoch Cost Tr_Error Tr_{0} Te_Error Te_{0}".format(aux_err_name)) for epoch in range(nEpochs): total_cost = 0 for ibatch in range(nTrBatches): output = training_fn(ibatch) total_cost += output[0] if epoch % tr_prms['EPOCHS_TO_TEST'] == 0: print("{:3d} {:>8.2f}".format(nn.get_epoch(), total_cost), end=' ') do_test() nn.inc_epoch_set_rate() print('Optimization Complete!!!')