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@ -85,8 +85,8 @@ def parse_args():
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help='If set, use nvprof for CUDA.')
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parser.add_argument(
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'--no_test',
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action='store_false',
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help='If set, test the testset during training.')
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action='store_true',
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help='If set, do not test the testset during training.')
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parser.add_argument(
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'--memory_optimize',
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action='store_true',
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@ -229,9 +229,9 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
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print("Pass: %d, Iter: %d, Loss: %f\n" %
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(pass_id, iters, np.mean(train_losses)))
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print_train_time(start_time, time.time(), num_samples)
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print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses)))
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print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses))),
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# evaluation
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if not args.no_test and batch_acc != None:
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if not args.no_test and batch_acc:
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pass_test_acc = test(exe, infer_prog, test_reader, feeder,
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batch_acc)
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print(", Test Accuracy: %f" % pass_test_acc)
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@ -310,7 +310,7 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
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print("Pass %d, batch %d, loss %s" %
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(pass_id, batch_id, np.array(loss)))
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print_train_time(start_time, time.time(), num_samples)
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if not args.no_test and batch_acc != None:
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if not args.no_test and batch_acc:
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test_acc = test(startup_exe, infer_prog, test_reader, feeder,
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batch_acc)
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print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))
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