evonet 0.1.0.dev23

Evolvable neural network core for integration with EvoLib

EvoNet

EvoNet is a modular and evolvable neural network core designed for integration with EvoLib. It supports dynamic topologies, recurrent connections, per-neuron activation, and structural evolution – with a strong emphasis on clarity, transparency, and didactic value.

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Features

• Layer-based but flexible – allows skip connections, cycles, and recurrent paths
• Typed neuron roles and connection types (NeuronRole, ConnectionType)
• Topology-aware mutation system – add/remove neurons and connections, mutate weights, change activations
• Per-neuron activation functions – configurable, extensible, evolvable
• 1-step recurrent state logic – avoids multi-pass stabilization
• Topology can grow at runtime – with add_neuron, add_connection, split_connection
• Debug-friendly architecture – explicit IDs, labels, roles, directional graphs
• Designed for evolutionary learning – mutation, crossover, speciation ready
• Lightweight & extensible – pure Python, NumPy-based, no hard dependencies

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⚠️ This project is in early development (alpha). Interfaces and structure may change.

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Quick Example

from evonet.core import Nnet

net = Nnet()
net.add_layer()  # Input
net.add_layer()  # Output

net.add_neuron(layer_idx=0, activation="linear", lable="in")
net.add_neuron(layer_idx=1, activation="linear", bias=0.5, lable="out", connect_layer=True)

print(net.calc([1.0]))

License

MIT License - see MIT License.