Deep learning test loss curve won't go down
Your learning curves suggest overfitting.
In this respect you should try

increasing dropout
adjust l1/l2 regularisation to higher levels
inspect class ratios in training and validation tests (&quot;learning on easy predicting on difficult&quot; imbalances)

Additionally, you may want to

add batchnorm layer
try different activations
check to what extend weights/bias of each layer get updated during training iterations, in case of vahishing/exploding gradients