ebm_sandbox.py [93:104]:
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    for i in range(1):
        emp_accuracies = []

        for data_corrupt, data, label_gt in tqdm(test_dataloader):
            feed_dict = {X: data, Y_GT: label_gt, Y: label_init, l1_norm: l1val, l2_norm: l2val}
            emp_accuracy = sess.run([accuracy], feed_dict)
            emp_accuracies.append(emp_accuracy)
            print(np.array(emp_accuracies).mean())

        print("Received total accuracy of {} for li of {} and l2 of {}".format(np.array(emp_accuracies).mean(), l1val, l2val))

    return np.array(emp_accuracies).mean()
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ebm_sandbox.py [140:152]:
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    for i in range(1):
        emp_accuracies = []

        for data_corrupt, data, label_gt in tqdm(test_dataloader):
            feed_dict = {X: data, Y_GT: label_gt, Y: label_init, l1_norm: l1val, l2_norm: l2val}
            emp_accuracy = sess.run([accuracy], feed_dict)
            emp_accuracies.append(emp_accuracy)
            print(np.array(emp_accuracies).mean())


        print("Received total accuracy of {} for li of {} and l2 of {}".format(np.array(emp_accuracies).mean(), l1val, l2val))

    return np.array(emp_accuracies).mean()
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