I came across the following code :. I am using the above strategy.
Source code for torch.optim.lr_scheduler
The only thing that I am not able to understand is that how are they using test data to step up the accuracy and decreasing learning rate on the basis of that via scheduler? It is the last line of the code. Can we during training show the test accuracy to the scheduler and ask that it to reduce the learning rate? I found the similar thing on github resnet main. Can someone please clarify? What the code actually refers to by test is the validation set not the actual test set.
The difference is that the validation set is used during training to see how well the model generalizes. Normally people just cut off a part of the training data and use that for validation.
To me it seems like your code is using the same data for training and validation but that's just my assumption because I don't know what. To work in a strictly scientific way, your model should never see actual test data during training, only training and validation. This way we can assess the models actual ability to generalize on unseen data after training. The reason why you use validation data called test data in your case to reduce the learning rate is probably because if you did this using the actual training data and training accuracy the model is more likely to overfit.
When you are on a plateau of the training accuracy it does not necessarily imply that it's a plateau of the validation accuracy and the other way round. Meaning you could be stepping in a promising direction regarding the validation accuracy and thus in a direction of parameters that generalize well and suddenly you reduce or increase the learning rate because there was a plateau or non in the training accuracy. Learn more.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. ReduceLROnPlateau fails when the variable passed to. PyTorch version: 1. How about copy. It seems to work for tensors cpu and cudaand will work for ndarrays, floats, etc. This incurs a small overhead, in cases where copying is not necessary.
If that's a problem, it could be handled with a switch, e. RuntimeError: Only Tensors created explicitly by the user graph leaves support the deepcopy protocol. I opted for a solution, which clones tensors and deepcopies anything else. Hi sorenrasmussenaiashermancinelli. Is this issue considered resolved. I'm curious does the above solution pass the test? I'm interested. Please let me know. I'm landing as a fix for this. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Labels good first issue. Copy link Quote reply.The minute blitz is the most common starting point, and provides a broad view into how to use PyTorch from the basics all the way into constructing deep neural networks. Learning PyTorch with Examples. What is torch. Transfer Learning for Computer Vision Tutorial. Adversarial Example Generation. Sequence-to-Sequence Modeling with nn. Transformer and TorchText. Text Classification with TorchText.
Language Translation with TorchText. Introduction to TorchScript. Pruning Tutorial. Getting Started with Distributed Data Parallel.
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Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. The GPU memory keep increasing when calling torch. ReduceLROnPlateau multiple times. Using torch. StepLR does not have such issue. Collecting environment information PyTorch version: 1. Python version: 3. I cant reproduce the issue in latest master.
I have been running the following code snippet for hours. Please check with 1. I am trying to run k-fold cross-validation.Embed youtube playlist with thumbnails
In each loop of my cross-validation code, I initialize a model, optimizer and scheduler. It does not seem to matter which scheduler I use.
Moving from Keras to Pytorch
I originally ran into this issue using the schedulers for pytorch-transformers so I opened an issue therebut after trying YujiaBao example with various pytorch schedulers I run into the same issue. I expect to be able to initialize a new instance of torch. If I am simply doing this wrong, is someone able to point out how I might initialize a new scheduler for each fold without this issue of accumulating memory?
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pytorch + apex 生活变得更美好
New issue. Jump to bottom. Labels module: optimizer. Copy link Quote reply.Pytorch is great. A while back, I was working with a competition on Kaggle on text classification, and as a part of the competition, I had to somehow move to Pytorch to get deterministic results.
So Pytorch did come to rescue. And I am glad that I considered moving. As a side note : if you want to know more about NLPI would like to recommend this awesome course on Natural Language Processing in the Advanced machine learning specialization.
This course covers a wide range of tasks in Natural Language Processing from basic to advanced: Sentiment Analysis, summarization, dialogue state tracking, to name a few.Infrastructures in and of ethnography
While Keras is great to start with deep learning, with time you are going to resent some of its limitations. I also thought about moving to Tensorflow. It seemed like a good transition as TF is the backend of Keras. With the whole session. It was not Pythonic at all. Pytorch helps in that since it is much more Pythonic. You have things under your control and you are not losing anything on the performance front.
Maybe gaining actually. In the words of Andrej Karpathy:. I have more energy. My skin is clearer. My eye sight has improved. So without further ado let me translate Keras to Pytorch for you. Let us create an example network in keras first which we will try to port into Pytorch. Here I would like to give a piece of advice too. When you try to move from Keras to Pytorch take any network you have and try porting it to Pytorch.
It will make you understand Pytorch in a much better way. Here I am trying to write one of the networks that gave pretty good results in the Quora Insincere questions classification challenge for me. This model has all the bells and whistles which at least any Text Classification deep learning network could contain with its GRU, LSTM and embedding layers and also a meta input layer.
And thus would serve as a good example. So a model in pytorch is defined as a class therefore a little more classy which inherits from nn. I found it beneficial due to a these reasons:. So fewer chances of error. Although this one is really up to the skill level. This is pretty helpful in the Encoder-Decoder architecture where you can return both the encoder and decoder output. Or in the case of autoencoder where you can return the output of the model and the hidden layer embedding for the data.
The LSTM layer has different initializations for biases, input layer weights, and hidden layer weights.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.In cold blood sparknotes
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As far as I understand, patience only determines after how many epochs the LR will be reduced, but has nothing to do with how much worse the value has to be for the LR to be reduced which should be handled by threshold. Nevertheless, with the same losses, which stop decreasing after around epoch 10, the point at which the LR is reduced is far more than patience epochs, and how much more depends weirdly on the value of patience.
From my understanding, however, in all cases the LR should be reduced after epoch Python version: 3. Versions of relevant libraries: [pip] numpy 1. Ok, sorry, never mind, apparently this is how it is intended to work and patients does not only apply to the first X epochs Skip to content.
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Sign up. New issue. Jump to bottom. Copy link Quote reply. Issue description As far as I understand, patience only determines after how many epochs the LR will be reduced, but has nothing to do with how much worse the value has to be for the LR to be reduced which should be handled by threshold. Adam model.
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