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Currently running the adaptive HyperNEAT example using device = "cuda:0"
Population size: 100
Elitism: 10%
No multiprocessing
It runs okay for several generations, up until the following exception is thrown from the cppn.py
Any help to sort out this issue is appreciated.
File "/pytorch_neat/cppn.py", line 108, in __call__
return self.get_activs(shape)
File "/pytorch_neat/cppn.py", line 93, in get_activs
self.activs = self.activate(xs, shape)
File "/pytorch_neat/cppn.py", line 87, in activate
raise Exception("Failed to activate node {}".format(self.name))
Exception: Failed to activate node delta_w
The text was updated successfully, but these errors were encountered:
Additional info on Traceback : RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
Could this be because self.aggregation(inputs) is not correctly implemented for cuda:0 device?
Traceback (most recent call last):
File "/pytorch_neat/cppn.py", line 83, in activate
pre_activs = self.aggregation(inputs)
File "/pytorch_neat/aggregations.py", line 20, in sum_aggregation
return sum(inputs)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
Currently running the adaptive HyperNEAT example using
device = "cuda:0"
Population size: 100
Elitism: 10%
No multiprocessing
It runs okay for several generations, up until the following exception is thrown from the
cppn.py
Any help to sort out this issue is appreciated.
The text was updated successfully, but these errors were encountered: