I am expecting specific outputs from the neural network which are the target values for my training. Let's say the target values are 0.8 for the upper output node and -0.3 for the lower output node.
The activations function used for the first 2 layers are ReLu or LeakyReLu while the last layer uses atan as an activation function.
For back propogation, instead of adjusting values to make the network's output approach 0.8,-0.3. is it suitable if I use the inverse function for atan -which is tan itself- to get "the ideal input to the output layer multiplied by weights and adjusted by biases".
The tan of 0.8 and -0.3 is 0.01396 and -0.00524 approximately.
My algorithm would then adjust weights and biases of the network so that the "pre-activated output" of the output layer -which is basically (sum(output_layer_weight*output_layer's inputs)+output_layer_biases)- approaches 0.01396 and -0.00524.
Is this suitable