Add tutorials
This commit is contained in:
wls2002
@@ -1,21 +1,206 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7c6d7313",
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"metadata": {},
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"source": [
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"# Tutorial 1: Genome\n",
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"The genome is the core component of TensorNEAT. It represents the network’s genotype."
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]
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},
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{
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"cell_type": "markdown",
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"id": "939c40b4",
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"metadata": {},
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"source": [
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"\n",
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"Before using the Genome, we need to create a `Genome` instance, which controls the behavior of the genome in use. After creating it, use `setup` to initialize."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "286b5fc2-e629-4461-ad95-b69d3d606a78",
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"execution_count": 66,
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"id": "3b1836c3",
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorneat.genome import DefaultGenome, BiasNode, DefaultConn, DefaultMutation\n",
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"\n",
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"genome = DefaultGenome(\n",
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" num_inputs=3, # 3 inputs\n",
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" num_outputs=1, # 1 output\n",
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" max_nodes=5, # the network will have at most 5 nodes\n",
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" max_conns=10, # the network will have at most 10 connections\n",
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" node_gene=BiasNode(), # node with 3 attributes: bias, aggregation, activation\n",
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" conn_gene=DefaultConn(), # connection with 1 attribute: weight\n",
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" mutation=DefaultMutation(\n",
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" node_add=0.9,\n",
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" node_delete=0.0,\n",
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" conn_add=0.9,\n",
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" conn_delete=0.0\n",
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" ) # high mutation rate for testing\n",
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")\n",
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"\n",
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"state = genome.setup()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f64c565c",
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"metadata": {},
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"source": [
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"After creating the genome, we can use it to perform various network operations, including random generation, forward passes, mutation, distance calculation, and more. These operations are JIT-compilable and also support vectorization with `jax.vmap`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"id": "ef66ba76",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"output=Array([-1.5388116], dtype=float32, weak_type=True)\n",
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"batch_output=Array([[ 2.5477247 ],\n",
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" [ 2.2334106 ],\n",
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" [ 1.8713341 ],\n",
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" [-3.7539673 ],\n",
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" [ 1.5344429 ],\n",
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" [ 2.7640016 ],\n",
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" [ 0.5649997 ],\n",
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" [-0.32709932],\n",
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" [ 3.5273829 ],\n",
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" [-0.64774114]], dtype=float32, weak_type=True)\n"
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]
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}
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],
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"source": [
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"import jax, jax.numpy as jnp\n",
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"\n",
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"# Initialize a network\n",
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"nodes, conns = genome.initialize(state, jax.random.PRNGKey(0))\n",
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"\n",
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"# Network forward\n",
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"single_input = jax.random.normal(jax.random.PRNGKey(1), (3, ))\n",
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"transformed = genome.transform(state, nodes, conns)\n",
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"output = genome.forward(state, transformed, single_input)\n",
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"print(f\"{output=}\")\n",
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"\n",
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"# Network batch forward\n",
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"batch_inputs = jax.random.normal(jax.random.PRNGKey(2), (10, 3))\n",
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"batch_output = jax.vmap(genome.forward, in_axes=(None, None, 0))(state, transformed, batch_inputs)\n",
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"print(f\"{batch_output=}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"id": "85433b5e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pop_outputs.shape=(1000, 1)\n"
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]
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}
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],
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"source": [
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"# Initialize a population of networks (1000 networks)\n",
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"pop_nodes, pop_conns = jax.vmap(genome.initialize, in_axes=(None, 0))(\n",
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" state, jax.random.split(jax.random.PRNGKey(0), 1000)\n",
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")\n",
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"\n",
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"# Population forward\n",
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"pop_inputs = jax.random.normal(jax.random.PRNGKey(1), (1000, 3))\n",
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"pop_transformed = jax.vmap(genome.transform, in_axes=(None, 0, 0))(\n",
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" state, pop_nodes, pop_conns\n",
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")\n",
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"pop_outputs = jax.vmap(genome.forward, in_axes=(None, 0, 0))(\n",
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" state, pop_transformed, pop_inputs\n",
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")\n",
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"\n",
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"print(f\"{pop_outputs.shape=}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"id": "f1f9df60",
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"metadata": {},
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"outputs": [],
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"source": [
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"# visualize the network\n",
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"network = genome.network_dict(state, nodes, conns) # Transform the network from JAX arrays to a Python dict\n",
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"genome.visualize(network, save_path=\"./origin_network.svg\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "da5ad8ae",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"id": "16966b69",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Mutate the network\n",
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"mutated_nodes, mutated_conns = genome.execute_mutation(\n",
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" state,\n",
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" jax.random.PRNGKey(2),\n",
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" nodes,\n",
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" conns,\n",
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" new_node_key=jnp.asarray(5), # at most 1 node can be added in each mutation\n",
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" new_conn_keys=jnp.asarray([6, 7, 8]), # at most 3 connections can be added\n",
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")\n",
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"\n",
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"# Visualize the mutated network\n",
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"mutated_network = genome.network_dict(state, mutated_nodes, mutated_conns)\n",
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"genome.visualize(mutated_network, save_path=\"./mutated_network.svg\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4367424",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "f9922d34",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "",
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"name": ""
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"display_name": "jax_env",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": ""
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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