Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling
A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.
 This paper provides an optimization approach for large batch training of CNN with layer-wise adaptive learning rates. It starts from the observation that the ratio between the L2-norm of parameters and that of gradients on parameters varies significantly in the optimization, and then introduce a local learning rate to consider this observation for a more stable and efficient optimization. Experimental results show improvements compared with the state-of-the-art algorithm. Review: (1) Pros The proposed optimization method considers the dynamic self-adjustment of the learning rate in the optimization based on the ratio between the L2-norm of parameters and that of gradients on parameters when the batch size increases, and shows improvements in experiments compared with previous methods. (2) Cons i) LR "warm-up" can mitigate the unstable training in the initial phase and the proposed method is also motivated by the stability but uses a different approach. However, it seems that the authors also combine with LR "warm-up" in your proposed method in the experimental part, e.g., Table 3. So does it mean that the proposed method cannot handle the problem in general? ii) There is one coefficient that is independent from layers and needs to be set manually in the proposed local learning rate. The authors do not have a detail explanation and experiments about it. In fact, as can be seen in the Algorithm 1, this coefficient can be as an independent hyper-parameter (even is put with the global learning rate together as one fix term). iii) In the section 6, when increase the training steps, experiments compared with previous methods should be implemented since they can also get better results with more epochs. iv) Writing should be improved, e.g., the first paragraph in section 6. Some parts are confusing, for example, the authors claim that they use initial LR=0.01, but in Table 1(a) it is 0.02.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling
A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper provides an optimization approach for large batch training of CNN with layer-wise adaptive learning rates. It starts from the observation that the ratio between the L2-norm of parameters and that of gradients on parameters varies significantly in the optimization, and then introduce a local learning rate to consider this observation for a more stable and efficient optimization. Experimental results show improvements compared with the state-of-the-art algorithm. Review: (1) Pros The proposed optimization method considers the dynamic self-adjustment of the learning rate in the optimization based on the ratio between the L2-norm of parameters and that of gradients on parameters when the batch size increases, and shows improvements in experiments compared with previous methods. (2) Cons i) LR "warm-up" can mitigate the unstable training in the initial phase and the proposed method is also motivated by the stability but uses a different approach. However, it seems that the authors also combine with LR "warm-up" in your proposed method in the experimental part, e.g., Table 3. So does it mean that the proposed method cannot handle the problem in general? ii) There is one coefficient that is independent from layers and needs to be set manually in the proposed local learning rate. The authors do not have a detail explanation and experiments about it. In fact, as can be seen in the Algorithm 1, this coefficient can be as an independent hyper-parameter (even is put with the global learning rate together as one fix term). iii) In the section 6, when increase the training steps, experiments compared with previous methods should be implemented since they can also get better results with more epochs. iv) Writing should be improved, e.g., the first paragraph in section 6. Some parts are confusing, for example, the authors claim that they use initial LR=0.01, but in Table 1(a) it is 0.02.
assistant
 This paper provides an optimization approach for large batch training of CNN with layer-wise adaptive learning rates. It starts from the observation that the ratio between the L2-norm of parameters and that of gradients on parameters varies significantly in the optimization, and then introduce a local learning rate to consider this observation for a more stable and efficient optimization. Experimental results show improvements compared with the state-of-the-art algorithm. Review: (1) Pros The proposed optimization method considers the dynamic self-adjustment of the learning rate in the optimization based on the ratio between the L2-norm of parameters and that of gradients on parameters when the batch size increases, and shows improvements in experiments compared with previous methods. (2) Cons i) LR "warm-up" can mitigate the unstable training in the initial phase and the proposed method is also motivated by the stability but uses a different approach. However, it seems that the authors also combine with LR "warm-up" in your proposed method in the experimental part, e.g., Table 3. So does it mean that the proposed method cannot handle the problem in general? ii) There is one coefficient that is independent from layers and needs to be set manually in the proposed local learning rate. The authors do not have a detail explanation and experiments about it. In fact, as can be seen in the Algorithm 1, this coefficient can be as an independent hyper-parameter (even is put with the global learning rate together as one fix term). iii) In the section 6, when increase the training steps, experiments compared with previous methods should be implemented since they can also get better results with more epochs. iv) Writing should be improved, e.g., the first paragraph in section 6. Some parts are confusing, for example, the authors claim that they use initial LR=0.01, but in Table 1(a) it is 0.02.