# based on feature map get reward and actions
actions, estimated_reward = ActorCritic.forward(AC_input)
# crop the feature map
cropped_F_map = Assistant.crop_feature_map(actions[0].squeeze().item(),
actions[1].squeeze().item(),
F_map_1).to(device)
# passed cropped feature map to FasteNet to get saliency map
saliency_map = FasteNet.module_two(cropped_F_map).to(
'cpu').squeeze().numpy()
# calculate 'reward' from saliency map
Assistant.parse_saliency_map(saliency_map)
reward = Assistant.calculate_loss()
# zero the gradients of both actor and critic
Actor_optimizer.zero_grad()
Critic_optimizer.zero_grad()
# calculate loss
loss = (estimated_reward - reward)**2
# backpropagate loss
loss.backward()
Actor_optimizer.step()
Critic_optimizer.step()
# checkpoint our training
weights_file = ActorCritic_helper.training_checkpoint(loss=loss,
