I’d like to visualize the distribution of the neurons associated to each hidden layer in my deep neural network. In principle, given the tensor I can easily do it through tensorboard.
For example if I was interested in the distribution of weight between layers I could do something like that:
for n, w in self.model.named_parameters():
    self.writer.add_histogram('Weights connection {}'.format(n), w, global_step=t)

where n is the name of the module, w is the corresponding tensor of weight and t is the index at which our summary is saved.
Is there an equivalent way to iterate through hidden layers calling tensors associated with the neurons’ state  of each layer?