Number of epochs to be used in a Keras sequential model
If the number of epochs are very high, your model may overfit and your training accuracy will reach 100%. In that approach you plot the error rate on training and validation data. The horizontal axis is the number of epochs and the vertical axis is the error rate. You should stop training when the error rate of validation data is minimum.
You need to have a trade-off between your regularization parameters. Major problem in Deep Learning is overfitting model. Various regularization techniques are used,as
i) Reducing batch-size
ii) Data Augmentation(only if your data is not diverse)
iii) Batch Normalization
iv) Reducing complexity in architecture(mainly convolutional layers)
v) Introducing dropout layer(only if you are using any dense layer)
vi) Reduced learning rate.
vii) Transfer learning
Batch-size vs epoch tradeoff is quite important. Also it is dependent on your data and varies from application to application. In that case, you have to play with your data a little bit to know the exact figure. Normally a batch size of 32 medium size images requires 10 epochs for good feature extraction from the convolutional layers. Again, it is relative
I'm building a Keras sequential model to do a binary image classification. Now when I use like 70 to 80 epochs I start getting good validation accuracy (81%). But I was told that this is a very big number to be used for epochs which would affect the performance of the network. 
My question is: is there a limited number of epochs that I shouldn't exceed, note that I have 2000 training images and 800 validation images.