Validation loss increases and validation accuracy decreases
What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science.
Overfitting happens when a model begins to focus on the noise in the training data set and extracts features based on it. This helps the model to improve its performance on the training set but hurts its ability to generalize so the accuracy on the validation set decreases.
To deal with overfitting, you need to use regularization during the training. You can try adding dropout layers or batch-normalization layers, adding weight regularization, or you can artificially increase the size of your training set by performing some data augmentation.
You can read more about it in the following post:
What are the possible approaches to fixing Overfitting on a CNN?