PyTorch-NEAT issues

Create setup.py

2

Adding this file will allow this repository to be used as a Python package.

ValueError: expected sequence of length 4 at dim 2 (got 0)

def dense_from_coo(shape, conns, dtype=torch.float64): mat = torch.zeros(shape, dtype=dtype) idxs, weights = conns if len(idxs) == 0: return mat rows, cols = np.array(idxs).transpose() mat[torch.LongTensor(rows), torch.LongTensor(cols)] = torch.tensor( weights, dtype=dtype) return mat...

shizi-19

I cannot get this to run.

2

I'm doing some research in continual learning, and a HyperNEAT implementation would come in handy. I have tried many things, and for the life of me I cannot get this...

paarthtandon

Fixed for cuda implementation

1

After running the _adaptive_ _example_ I was able to spot some errors. These were mainly related to the the tensors having different device type (cpu and cuda). Tensors expected to...

steviestevepy

Exception: Failed to activate node delta_w

1

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...

steviestevepy

Problem with torch

1

When running pip install -r requirements.txt, I got the error : ERROR: No matching distribution found for torch==0.4.0 (from -r requirements.txt (line 5))

ColineVL

AssertionError: assert len(leaf_names) == len(genome_config.input_keys)

No matter how I attempt to start one of the examples (adaptive) I get this error. Full log here. ```Traceback (most recent call last): File "g:/Emulators/ML AI open AI/env2.py", line...

SilverDash

RuntimeError: tensors used as indices must be long or byte tensors

1

I am trying to run the examples.simple.main.py and I encounter the following exception: `RuntimeError: tensors used as indices must be long or byte tensors` I have the following packages installed...

Supermaxman