Validation loss much higher than training loss
Your model is completely overfitting. The training loss is constantly decreasing but the validation loss isn't. This means that the your current model is complex enough to 'memorize' the patterns in the training data. In such situations, you need to regularize your model.

To regularize your data you may try any or all combination of the following:

1) Reduce the layers of the neural network.

2) Reduce the number of neurons in each layer of the network to reduce the number of parameters.

3) Add dropout and tune its rate.

4) Use L2 normalisation on the parameter weights and tune the lambda value.

5) If possible add more data for training. 

6) Adding more data may not always be economically viable option so 'Data Augmentation' can play a big role here. Data Augmentation depends on the type of input to the neural nets. For images they comprise of rotation, translation, affine transformation like shear etc.