neural networks - Explanation of Spikes in training loss vs.
I am training a neural network using i) SGD and ii) Adam Optimizer. When using normal SGD, I get a smooth training loss vs. iteration curve as seen below (the red one). However, when I used the Adam Optimizer, the training loss curve has some spikes. What's the explanation of these spikes? Model Details:
Extremely large spike in training loss that destroys training progress
2020年11月29日 · I am training a super-resolution neural network that takes a low-resolution image and predicts its high-resolution counterpart. However, once in a while (e.g. a few hours), the training loss will spike to an extremely large value …
recurrent neural network - Why does the loss/accuracy fluctuate …
2018年5月14日 · There are several reasons that can cause fluctuations in training loss over epochs. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters.
Training loss increases with time [duplicate] - Cross Validated
2018年1月25日 · I am training a model (Recurrent Neural Network) to classify 4 types of sequences. As I run my training I see the training loss going down until the point where I correctly classify over 90% of the samples in my training batches. However a couple of epochs later I notice that the training loss increases and that my accuracy drops.
Why is the validation accuracy fluctuating? - Cross Validated
2017年1月8日 · In such case, though your network is stepping into convergence, you might see lots of fluctuations in validation loss after each train-step. But if you wait for a bigger picture, you can see that your network is actually converging to a minima with fluctuations wearing out.(see the attached images for one such example).
deep learning - What could cause my neural network model's loss ...
2016年3月1日 · Based on my experience, just saying what i have seen, a few things could cause this: 1. learning rate. if it's too large, it could cause rising loss; just make it smaller, magnitudes smallers 2. in case of RNN, it could all blow up, weights, gradients, loss; there's paper published dealing with that.
Training loss goes down and up again. What is happening?
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Weird spikes in Loss function - Cross Validated
2021年1月21日 · You’re training a neural network, so your loss function exists in dozens, hundreds, or billions of dimensions. $\endgroup$ – Dave Commented Jan 22, 2021 at 1:09
How do I make my neural network better at predicting sine waves?
2017年10月10日 · A neural network never learns a function analytically, only approximately via statistics - this is true for nearly all supervised learning ML techniques. The more advanced algorithms can get arbitrarily close to a chosen function given enough examples (and free parameters in the model), but will still only do so in the range of supplied ...
L2-norms of gradients increasing during training of deep neural …
2015年9月27日 · I'm training a convolutional neural network (CNN) with 5 conv-layers and 2 fully-connected layers for binary classification using stochastic gradient descent (SGD) with momentum. The accuracy of the network is fine, but I am curious about the behavior of …