What is the effect of adding more hidden layers to the network?

1) Increasing the number of hidden layers might improve the accuracy or might not, it really depends on the complexity of the problem that you are trying to solve. Where in the left picture they try to fit a linear function to the data.

Can you have too many hidden layers?

Using too many neurons in the hidden layers can result in several problems. First, too many neurons in the hidden layers may result in overfitting.

What happens if we manipulate the value of dropout?

With dropout (dropout rate less than some small value), the accuracy will gradually increase and loss will gradually decrease first(That is what is happening in your case). When you increase dropout beyond a certain threshold, it results in the model not being able to fit properly.

How does number of hidden units affect convergence?

More or less hidden layers should not affect convergence though the generalization power of the two would be different. More hidden layers shouldn’t prevent convergence, although it becomes more challenging to get a learning rate that updates all layer weights efficiently.

Does increasing the number of layers in a neural network affect accuracy?

2) Increasing the number of hidden layers much more than the sufficient number of layers will cause accuracy in the test set to decrease, yes. It will cause your network to overfit to the training set, that is, it will learn the training data, but it won’t be able to generalize to new unseen data.

How many hidden layers are there in a neural network?

And these hidden layers are not visible to the external systems and these are private to the neural networks. There should be zero or more than zero hidden layers in the neural networks. For large majority of problems one hidden layer is sufficient.

What makes Neural networks superior to machine learning algorithms?

The Hidden layers make the neural networks as superior to machine learning algorithms. The hidden layers are placed in between the input and output layers that’s why these are called as hidden layers. And these hidden layers are not visible to the external systems and these are private to the neural networks.

Does adding more hidden layers give more accuracy?

It’s true for some reasons that on adding more hidden layers, it will give more accuracy. This is true for larger datasets, as more layers with less stride factor will extract more features for your input data.