I trained different classification models using Keras with different numbers of hidden layers and the same number of neurons in each layer. What I found was the accuracy of the models decreased as the number of hidden layers increased However, the decrease was more significant in larger numbers of hidden layers. The accuracies refer to the test data and were obtained using k-fold=5. Also, no regularization was used. The following graph shows the accuracies of different models where the number of hidden layers changed while the rest of the parameters stayed the same (each model has 64 neurons in each hidden layer):
My question is why is the drop in accuracy between 8 hidden layers and 16 hidden layers much greater than the drop between 1 hidden layer and 8 hidden layers, even though the difference in the number of hidden layers is the same (8).