Repository navigation
Number of output and input in layer of networks #7
Description
hello, wanted to ask u if possible, why did u choose this number of input and output in the layers?
layer_1 = self.layer_manager.create_conv_layer(self.tf_inputs_norm, 32, 8, strides=4, activation_fn='relu',
padding='valid', scope='tf_layer_1')
layer_2 = self.layer_manager.create_conv_layer(layer_1, 64, 4, strides=2, activation_fn='relu', padding='valid',
scope='tf_layer_2')
layer_3 = self.layer_manager.create_conv_layer(layer_2, 64, 3, strides=1, activation_fn='relu', padding='valid',
scope='tf_layer_3')
layer_4 = self.layer_manager.create_fully_connected_layer(layer_3, 512, activation_fn='relu',
scope='tf_layer_4')
as i know the number of outputs in the output layer should be the number of possible actions in the environment, and the input is the state
Activity
garlicdevs commentedon Jul 18, 2020
Hi, which file is this code in?
TyrandeWhisperwind commentedon Jul 18, 2020
Fruit-API-master\fruit\configs\multi_objectives.py
and also why did u choose that number of neural in the hidden layers?
garlicdevs commentedon Jul 18, 2020
The number of hidden layers is based on this paper
https://arxiv.org/abs/1803.02965
It suits for Deep Sea Treasure, you can tune the network to suit your problem