Questions tagged [epochs]

In terms of artificial neural networks, an epoch refers to the cycles through the full training dataset. Usually, training a neural network takes more than a few epochs. ... With a neural network, the goal of the model is generally to classify or generate material which is right or wrong.

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Is there any relationship between the batch size and the number of epochs?

I am currently running a program with a batch size of 17 instead of batch size 32. The benchmark results are obtained at a batch size of 32 with the number of epochs 700. Now I am running with batch ...
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Does increase in the number of epochs can lead to complete breakdown?

Recently, I ran a code on my system that involves deep neural networks. The number of epochs provided by the designers are 301. I tried to increase the number of epochs to 501. To my shock, the model ...
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Should an increased learning rate for an adaptive linear neuron (ADALINE) reduce the square error at every epoch?

I am completely new to neural networks and therefore, my query may have some basic conceptual problem. I am following Fundamentals of Neural Networks by Laurene Fusett. In this book, the author ...
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Is it a fair evaluation if each model of k-fold cross validation is trained with different epochs and the mean AUC score gathered out of k folds?

Let's say I have a dataset for binary classification. And I am going to conduct 5-fold cross-validation and get AUC scores for each fold (mean AUC score too). However, if I set the training epoch to $...
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Why does the accuracy drop while the loss decrease, as the number of epochs increases?

I've been trying to find the optimal number of epochs that I should train my neural network (that I just implemented) for. The visualizations below show the neural network being run with a variable ...
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How to shorten the development time of a neural network?

I am developing an LSTM for sequence tagging. During the development, I do various changes in the system, for example, add new features, change the number of nodes in the hidden layers, etc. After ...