Smooth Activations and Reproducibility in Deep Networks

1 INTRODUCTION . Recent developments in deep learning leave no question about the advantages of deep networks over classical methods , which relied heavily on linear convex optimization solutions . With their astonishing unprecedented success , deep models are providing solutions to a continuously increasing number of domains in our lives . These solutions , however , while much more accurate than their convex counterparts , are usually irreproducible in the predictions they provide . While average accuracy of deep models on some validation dataset is usually much higher than that of linear convex models , predictions on individual examples of two models , that were trained to be identical , may diverge substantially , exhibiting Prediction Differences that may be as high as non-negligible fractions of the actual predictions ( see , e.g. , Chen et al . ( 2020 ) ; Dusenberry et al . ( 2020 ) ) . Deep networks express ( only ) what they learned . Like humans , they may establish different beliefs as function of the order in which they had seen training data ( Achille et al. , 2017 ; Bengio et al. , 2009 ) . Due to the huge amounts of data required to train such models , enforcing determinism ( Nagarajan et al. , 2018 ) may not be an option . Deep networks may be trained on highly distributed , parallelized systems . Thus two supposedly identical models , with the same architecture , parameters , training algorithm and training hyper-parameters that are trained on the same training dataset , even if they are initialized identically , will exhibit some randomness in the order in which they see the training set and apply updates . Due to the highly non-convex objective , such models may converge to different optima , which may exhibit equal average objective , but can provide very different predictions to individual examples . Irreproducibility in deep models is not the classical type of epistemic uncertainty , widely studied in the literature , nor is it overfitting . It differs from these phenomena in several ways : It does not diminish with more training examples like classical epistemic uncertainty , and it does not cause degradation to test accuracy by overfitting unseen data to the training examples . While irreproducibilty may be acceptable for some applications , it can be very detrimental in applications , such as medical ones , where two different diagnoses to the same symptoms may be unacceptable . Furthermore , in online and/or re-enforcement systems , which rely on their predictions to determine actions , that in turn , determine the remaining training examples , even small initial irreproducibility can cause large divergence of models that are supposed to be identical . One example is sponsored advertisement online Click-Through-Rate ( CTR ) prediction ( McMahan et al. , 2013 ) . The effect of irreproducibility in CTR prediction can go far beyond changing the predicted CTR of an example , as it may affect actions that take place downstream in a complex system . Reproducibility is a problem even if one trains only a single model , as it may be impossible to determine whether the trained model provides acceptable solutions for applications that can not tolerate unacceptable ones . A major factor to the unprecedented success of deep networks in recent years has been the Rectified Linear Unit ( ReLU ) activation , ( Nair & Hinton , 2010 ) . ReLU nonlinearity together with back-propagation give simple updates , accompanied with superior accuracy . ReLU thus became the undisputed activation used in deep learning . However , is ReLU really the best to use ? While it gives better optima than those achieved with simple convex models , it imposes an extremely nonconvex objective surface with many such optima . The direction of a gradient update with a gradient based optimizer is determined by the specific example which generates the update . Thus the order of seeing examples or applying updates can determine which optimum is reached . Many such optima , as imposed by ReLU , thus provide a recipe for irreproducibility . In recent years , different works started challenging the dominance of ReLU , exploring alternatives . Overviews of various activations were reported in Nwankpa et al . ( 2018 ) ; Pedamonti ( 2018 ) . Variations on ReLU were studied in Jin et al . ( 2015 ) . Activations like SoftPlus ( Zheng et al. , 2015 ) , Exponential Linear Unit ( ELU ) ( Clevert et al. , 2015 ) , Scaled Exponential Linear Unit ( SELU ) ( Klambauer et al. , 2017 ; Sakketou & Ampazis , 2019 ; Wang et al. , 2017 ) , or Continuously differentiable Exponential Linear Unit ( CELU ) ( Barron , 2017 ) were proposed , as well as the Gaussian Error Linear Unit ( GELU ) ( Hendrycks & Gimpel , 2016 ) . Specifically , the Swish activation ( Ramachandran et al. , 2017 ) ( that can approximate GELU ) was found through automated search to achieve superior accuracy to ReLU . Further activations with similarity to GELU were proposed recently ; Mish ( Misra , 2019 ) , and TanhExp ( Liu & Di , 2020 ) . Unlike ReLU , many of these activations are smooth with continuous gradient . Good properties of smooth activations were studied as early as Mhaskar ( 1997 ) ( see also Du ( 2019 ) ; Lokhande et al . ( 2020 ) ) . These series of papers started suggesting that smooth activations , if configured properly , may be superior to ReLU in accuracy . Recent work by Xie et al . ( 2020 ) , that was done subsequently to our work , and was inspired from our results that we report in this paper , ( Lin & Shamir , 2019 ) , demonstrated also the advantage of smooth activations for adversarial training . Our Contributions : In this paper , we first demonstrate the advantages of smooth activations to reproducibility in deep networks . We show that not only can smooth activations improve accuracy of deep networks , they can also achieve superior tradeoffs between reproducibility and accuracy , by attaining a lower average Prediction Difference ( PD ) for the same or better accuracy . Smooth activations , like Swish , GELU , Mish , and TanhExp , all have a very similar non-monotonic form , that does not provide a clear stop region ( with strict 0 ; not only approaching 0 ) , and slope 1 region . While these activations approximate the mathematical form of ReLU , they lack these properties of ReLU . All these activations including SoftPlus also require more expensive mathematical expressions , involving exponents , and , in some , logarithms , or even numerically computed values ( e.g. , GELU ) . This can make deployment harder , especially with certain simplified hardware that supports only a limited number of operations , as well as can slow down training due to the heavier computations . Unlike ReLU , which can be transformed into Leaky ReLU , the smooth activations described can not be easily transformed into more general forms . In this work , we propose Smooth ReLU ( SmeLU ) , which is mathematically simple and based only on linear and quadratic expressions . It can be more easily deployed with limited hardware , and can provide faster training when hardware is limited . SmeLU provides a clearly defined 0 activation region , as well as a slope 1 region , is monotonic , and is also extendable to a leaky or more general form . SmeLU gives the good properties of smooth activations providing better reproducibility as well as better accuracy-reproducibility tradeoffs . Its generalized form allows even further accuracy improvements . The methodology to construct SmeLU is shown to be even more general , allowing for more complex smooth activations , which are all clustered under the category of Rectified Smooth Continuous Units ( RESCUs ) . Related Work : Ensembles ( Dietterich , 2000 ) have been used to reduce uncertainty ( Lakshminarayanan et al. , 2017 ) . They are useful also for reducing irreproducibility . However , they make models more complex , and can trade off accuracy in favor of reproducibility if one attempts to keep constant computation costs ( which require reducing capacity of each component of the ensemble ) . Compression of deep networks into smaller networks that attempt to describe the same information is the emerging area of distillation ( Hinton et al. , 2015 ) . Predictions of a strong teacher train a weaker student model . The student is then deployed . This approach is very common if there are ample training resources , but deployment is limited , as for mobile network devices . Co-distillation , proposed by Anil et al . ( 2018 ) ( see also Zhang et al . ( 2018 ) ) , took advantage of distillation to address irreproducibility . Instead of unidirectional transfer of knowledge , several models distill information between each other . They attempt to converge to the same solution . The method requires more training resources to co-train models , but deployment only requires a single model . A somewhat opposite approach ; Anti-Distillation , to address irreproducibility was proposed by Shamir & Coviello ( 2020 ) , embracing ensembles , with an additional loss that forces their components away from one another . Each component is forced to capture a ( more ) different part of the objective space , and as a whole , the predictions of the ensemble are more reproducible . To the best of our knowledge , all previously reported techniques to address irreproducibility in deep networks required some form of ensembles . In this work , we leverage smooth activations , and do not require ensembles . Outline : Section 2 proposes several PD metrics , which we use to measure irreproducibility . Next , we overview our setup and smooth activations in Section 3 , and describe SmeLU and its generalization in Section 4 . Experimental results are shown in Section 5 . 2 PREDICTION DIFFERENCE . Average individual per-example Prediction Difference ( PD ) over a set of models that are configured , trained , and supposed to be identical over some validation dataset can be defined in various ways . We refer to Shamir & Coviello ( 2020 ) for a more detailed discussion . We opt to the same definitions they used , where we describe the classification case , in which we measure the PD using some Lp norm on the actual label predictions . Following their notation , denote the number of models by M , and the number of validation examples by N . Let Pn , m be the distribution over labels predicted by model m for example n. ( Pn , m ( ` ) is the probability predicted for label ` . ) Let Pn 4 = ∑ m Pn , m/M be the expected distribution over labels over all M models . Then , the pth norm PD , ∆p , is given by ∆p = 1 N N∑ n=1 · 1 M M∑ m=1 ‖Pn , m − Pn‖p = 1 N N∑ n=1 · 1 M M∑ m=1 · [ ∑ ` |Pn , m ( ` ) − Pn ( ` ) |p ] 1/p . ( 1 ) With practical large scale systems , with very high training costs , we can use M = 2 , where for binary labels , ∆1 = 1/N · ∑ n |Pn,1 ( 1 ) − Pn,2 ( 1 ) | , where 1 is the positive label . As in Shamir & Coviello ( 2020 ) , we can consider relative PD , ∆r1 , normalizing the innermost summand in ( 1 ) by Pn ( ` ) , which for binary problems can be tweaked into ∆̃r1 , normalizing by Pn ( 1 ) instead . PD , ∆ L 1 , can be computed only on the observed true label , by replacing the innermost sum on ` in ( 1 ) by |Pn , m ( ` true ) − Pn ( ` true ) | for the true label , normalizing by Pn ( ` true ) . Its computation , however , requires knowledge of the true label . Finally , in classification , one can use Hamming PD ; ∆H , specifying the average fraction of labels predicted differently between model pairs .

Summary: The paper claims that smooth activations are more reproducible than ReLU. The accuracy gain claims seem marginal and not carefully carried out, further ablation studies are needed to strengthen the conclusion on accuracy. However, the main point of the paper is reproducibility where the feature is measured by the ‘Prediction Difference’. PD (introduced in section 2) is a measure over a set of models where the PD score is low if the models output consistent estimates for the same validation samples.