def evaluate():
print(eval_model)<EOL>eval_model.initialize(mx.init.Xavier(), ctx=context[<NUM_LIT:0>])<EOL>eval_model.hybridize(static_alloc=True, static_shape=True)<EOL>epoch = args.from_epoch if args.from_epoch else <NUM_LIT:0><EOL>while epoch < args.epochs:<EOL><INDENT>checkpoint_name = '<STR_LIT>'%(args.save, format(epoch, '<STR_LIT>'))<EOL>if not os.path.exists(checkpoint_name):<EOL><INDENT>print('<STR_LIT>')<EOL>time.sleep(<NUM_LIT>)<EOL>continue<EOL><DEDENT>eval_model.load_parameters(checkpoint_name)<EOL>print('<STR_LIT>'%(checkpoint_name))<EOL>start_epoch_time = time.time()<EOL>final_test_L = test(test_data, test_batch_size, ctx=context[<NUM_LIT:0>])<EOL>end_epoch_time = time.time()<EOL>print('<STR_LIT>'%<EOL>(epoch, final_test_L, math.exp(final_test_L)))<EOL>print('<STR_LIT>'%(epoch, end_epoch_time - start_epoch_time))<EOL>sys.stdout.flush()<EOL>epoch += <NUM_LIT:1><EOL><DEDENT>
Evaluate loop for the trained model