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!!!')
