Train Neural Networks With Noise to Reduce Overfitting

Weight noise tends to ‘simplify’ neural networks, in the sense of reducing the amount of information required to transmit the parameters, which improves generalisation.In a prior 2011 paper that studies different types of static and adaptive weight noise titled “Practical Variational Inference for Neural Networks,” Graves recommends using early stopping in conjunction with the addition of weight noise with LSTMs.… in practice early stopping is required to prevent overfitting when training with weight noise.This section provides some tips for adding noise during training with your neural network.Noise can be added to training regardless of the type of problem that is being addressed.It is appropriate to try adding noise to both classification and regression type problems.The type of noise can be specialized to the types of data used as input to the model, for example, two-dimensional noise in the case of images and signal noise in the case of audio data.Adding noise during training is a generic method that can be used regardless of the type of neural network that is being used.It was a method used primarily with multilayer Perceptrons given their prior dominance, but can be and is used with Convolutional and Recurrent Neural Networks.It is important that the addition of noise has a consistent effect on the model.This requires that the input data is rescaled so that all variables have the same scale, so that when noise is added to the inputs with a fixed variance, it has the same effect..The also applies to adding noise to weights and gradients as they too are affected by the scale of the inputs.This can be achieved via standardization or normalization of input variables.If random noise is added after data scaling, then the variables may need to be rescaled again, perhaps per mini-batch.You cannot know how much noise will benefit your specific model on your training dataset.Experiment with different amounts, and even different types of noise, in order to discover what works best.Be systematic and use controlled experiments, perhaps on smaller datasets across a range of values.Noise is only added during the training of your model.Be sure that any source of noise is not added during the evaluation of your model, or when your model is used to make predictions on new data.This section provides more resources on the topic if you are looking to go deeper.In this post, you discovered that adding noise to a neural network during training can improve the robustness of the network resulting in better generalization and faster learning.Specifically, you learned:Do you have any questions?.Ask your questions in the comments below and I will do my best to answer..…with just a few lines of python codeDiscover how in my new Ebook: Better Deep LearningIt provides self-study tutorials on topics like: weight decay, batch normalization, dropout, model stacking and much more…Skip the Academics..Just Results.Click to learn more.. More details

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