Why validation accuracy is increasing very slowly? My convolutional network seems to work well in learning the features. However, the accuracy of the validation set is increasing very slowly with respect to the learning rate as also illustrated in the figure below:

The loss of both training and validation sets are shown in the figure below:

If I decrease the learning rate, the validation accuracy will stay around 25% and it will not increase. Is there any method to speed up the validation accuracy increment while decreasing the rate of learning?
 A: Thanks for all the comments. First, I looked at this problem as overfitting and spend so much time on methods to solve this such as regularization and augmentation. Finally, after trying different methods, I couldn't improve the validation accuracy. Thus, I went through the data. I found a bug in my data preparation which was resulting in similar tensors being generated under different labels. I generated the correct data and the problem was solved to some extent (The validation accuracy increased around 60%). Then finally I improved the validation accuracy to 90% by the technique that @Jonathan mentioned in his comment: adding more "conv2d + maxpool" layers.
A: this is a classic case of overfitting - you have good results for your training set, but bad results for your validation set.
there are a few psossible things to do (the sulotion is not in the learning rate):

*

*use dropout layers, for example:
conv2d->maxpool->dropout -> conv2d->maxpool->dropout

*use l1 regularization or l2 regularization

*use data augmentation / data generation: before inserting the input image to your network, apply some random transformation- rotation, strech, flip, crop, enlargement and more

*add more conv2d->maxpool layers

good luck!
