You have to scale your target or you need a better initialization of output weight.
I am assuming that you doing regression here.
The key thing is, your network outputs should start at the average of the targets, in your case it should be around -2700 if you don’t want to scale your targets. Then the training just needs to adjust a little bit to move outputs up and down to match the targets.
If your initialized outputs is too far away from the target, then loss function is big. As a result the gradient is too big. After several updates, your weights move to far and fall into saturation area of the tanh() activation and stop learning. I guess this is what happened to your model.