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@@ -26,7 +26,7 @@ TensorNEAT is a JAX-based libaray for NeuroEvolution of Augmenting Topologies (N
- JAX-based network for neuroevolution:
- **Batch inference** across networks with different architectures, GPU-accelerated.
- Evolve networks with **irregular structures** and **fully customize** their behavior.
- Visualize the network and represent it in **mathematical formulas** or codes.
- Visualize the network and represent it in **mathematical formulas** or **codes**.
- GPU-accelerated NEAT implementation:
- Run NEAT and HyperNEAT on GPUs.
@@ -78,15 +78,15 @@ pipeline.show(state, best)
Obtain result in a few generations:
```
Fitness limit reached!
input: [0. 0. 0.], target: [0.], predict: [0.00066471]
input: [0. 0. 1.], target: [1.], predict: [0.9992988]
input: [0. 1. 0.], target: [1.], predict: [0.9988666]
input: [0. 1. 1.], target: [0.], predict: [0.00107922]
input: [1. 0. 0.], target: [1.], predict: [0.9987184]
input: [1. 0. 1.], target: [0.], predict: [0.00093677]
input: [1. 1. 0.], target: [0.], predict: [0.00060118]
input: [1. 1. 1.], target: [1.], predict: [0.99927646]
loss: 8.484730074087565e-07
input: [0. 0. 0.], target: [0.], predict: [0.00037953]
input: [0. 0. 1.], target: [1.], predict: [0.9990619]
input: [0. 1. 0.], target: [1.], predict: [0.9991497]
input: [0. 1. 1.], target: [0.], predict: [0.0004661]
input: [1. 0. 0.], target: [1.], predict: [0.998262]
input: [1. 0. 1.], target: [0.], predict: [0.00077246]
input: [1. 1. 0.], target: [0.], predict: [0.00082464]
input: [1. 1. 1.], target: [1.], predict: [0.99909043]
loss: 8.861396736392635e-07
```
4. **Visualize the best network**:
```python