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Number of output and input in layer of networks #7

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TyrandeWhisperwind opened this issue Jul 16, 2020 · 3 comments
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Number of output and input in layer of networks #7

TyrandeWhisperwind opened this issue Jul 16, 2020 · 3 comments

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@TyrandeWhisperwind
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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

@garlicdevs
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Hi, which file is this code in?

@TyrandeWhisperwind
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Fruit-API-master\fruit\configs\multi_objectives.py
and also why did u choose that number of neural in the hidden layers?

@garlicdevs
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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

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