How do I properly tune parameters for a neural network?
Hidden layers, Epochs, batch-size: Try different numbers.
Optimizers: Adam (gives better results), Rmsprop
Dropout: 0.2 works well in most case
Also as a plus, you should also try different activation functions ( like you can use ReLu in the hidden layers and for output layer use sigmoid for binary class classification and softmax for multiclass classification.
How do I tune the parameters for a neural network such as the amount of layers, the types of layers, the width, etc.?  Right now I simply guess for good parameters.  This becomes very expensive and time consuming for me as I will tune a network and then find out that it didn't do any better than the previous model.  Is there better way to tune the model to get a good test and validation score?