Making sure the derivative is approximately matching your result from backpropagation should help in locating where is the problem. Choosing a good minibatch size can influence the learning process indirectly, since a larger mini-batch will tend to have a smaller variance (law-of-large-numbers) than a smaller mini-batch. What's the difference between a power rail and a signal line? Why is this sentence from The Great Gatsby grammatical? learning rate) is more or less important than another (e.g. Find centralized, trusted content and collaborate around the technologies you use most. The reason that I'm so obsessive about retaining old results is that this makes it very easy to go back and review previous experiments. train the neural network, while at the same time controlling the loss on the validation set. Might be an interesting experiment. Suppose that the softmax operation was not applied to obtain $\mathbf y$ (as is normally done), and suppose instead that some other operation, called $\delta(\cdot)$, that is also monotonically increasing in the inputs, was applied instead. How to handle a hobby that makes income in US. If you don't see any difference between the training loss before and after shuffling labels, this means that your code is buggy (remember that we have already checked the labels of the training set in the step before). Choosing and tuning network regularization is a key part of building a model that generalizes well (that is, a model that is not overfit to the training data). LSTM Training loss decreases and increases, Sequence lengths in LSTM / BiLSTMs and overfitting, Why does the loss/accuracy fluctuate during the training? What could cause this? When my network doesn't learn, I turn off all regularization and verify that the non-regularized network works correctly. Your learning could be to big after the 25th epoch. Redoing the align environment with a specific formatting. Also it makes debugging a nightmare: you got a validation score during training, and then later on you use a different loader and get different accuracy on the same darn dataset. You want the mini-batch to be large enough to be informative about the direction of the gradient, but small enough that SGD can regularize your network. AFAIK, this triplet network strategy is first suggested in the FaceNet paper. All the answers are great, but there is one point which ought to be mentioned : is there anything to learn from your data ? Making statements based on opinion; back them up with references or personal experience. (Keras, LSTM), Changing the training/test split between epochs in neural net models, when doing hyperparameter optimization, Validation accuracy/loss goes up and down linearly with every consecutive epoch.
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