add sympy support; which can transfer your network into sympy expression;
add visualize in genome; add related tests.
This commit is contained in:
wls2002
211
tensorneat/examples/interpret_visualize/genome_sympy.ipynb
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211
tensorneat/examples/interpret_visualize/genome_sympy.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
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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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"from algorithm.neat import *\n",
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"from algorithm.neat.genome.advance import AdvanceInitialize\n",
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"from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse\n",
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"from utils.graph import topological_sort_python\n",
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"from utils import Act, Agg"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:46.886073700Z",
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"start_time": "2024-06-12T11:35:46.042288800Z"
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}
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},
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"id": "9531a569d9ecf774"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [],
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"source": [
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"genome = AdvanceInitialize(\n",
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" num_inputs=3,\n",
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" num_outputs=1,\n",
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" hidden_cnt=1,\n",
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" max_nodes=50,\n",
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" max_conns=500,\n",
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" node_gene=NodeGeneWithoutResponse(\n",
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" # activation_default=Act.tanh,\n",
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" aggregation_default=Agg.sum,\n",
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" # activation_options=(Act.tanh,),\n",
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" aggregation_options=(Agg.sum,),\n",
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" )\n",
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")\n",
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"\n",
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"state = genome.setup()\n",
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"\n",
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"randkey = jax.random.PRNGKey(42)\n",
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"nodes, conns = genome.initialize(state, randkey)\n",
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"\n",
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"network = genome.network_dict(state, nodes, conns)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:52.274062400Z",
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"start_time": "2024-06-12T11:35:46.892042200Z"
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}
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},
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"id": "4013c9f9d5472eb7"
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"data": {
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"text/plain": "[-0.535*sigmoid(0.346*i0 + 0.044*i1 - 0.482*i2 + 0.875) - 0.264]"
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import sympy as sp\n",
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"\n",
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"symbols, input_symbols, nodes_exprs, output_exprs, forward_func = genome.sympy_func(state, network, precision=3, )\n",
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"output_exprs"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:52.325161800Z",
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"start_time": "2024-06-12T11:35:52.282008300Z"
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}
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},
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"id": "addea793fc002900"
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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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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"- 0.535 \\mathrm{sigmoid}\\left(0.346 i_{0} + 0.044 i_{1} - 0.482 i_{2} + 0.875\\right) - 0.264\n"
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]
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}
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],
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"source": [
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"print(sp.latex(output_exprs[0]))"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:52.341639700Z",
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"start_time": "2024-06-12T11:35:52.323163700Z"
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}
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},
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"id": "967cb87e24373f77"
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "88eee4db9eb857cd"
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [
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{
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"data": {
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"text/plain": "[-0.7940936986556304]"
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"random_inputs = np.random.randn(3)\n",
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"res = forward_func(random_inputs)\n",
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"res "
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:52.342638Z",
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"start_time": "2024-06-12T11:35:52.330160600Z"
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}
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},
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"id": "c5581201d990ba1c"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"outputs": [
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{
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"data": {
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"text/plain": "Array([-0.7934886], dtype=float32, weak_type=True)"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"transformed = genome.transform(state, nodes, conns)\n",
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"res = genome.forward(state, transformed, random_inputs)\n",
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"res"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:53.273851900Z",
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"start_time": "2024-06-12T11:35:52.384588600Z"
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}
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},
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"id": "fe3449a5bc688bc3"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T11:35:53.274854100Z",
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"start_time": "2024-06-12T11:35:53.265856700Z"
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}
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},
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"id": "174c7dc3d9499f95"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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30
tensorneat/examples/interpret_visualize/genome_sympy.py
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30
tensorneat/examples/interpret_visualize/genome_sympy.py
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import jax, jax.numpy as jnp
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from algorithm.neat import *
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from algorithm.neat.genome.advance import AdvanceInitialize
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from utils.graph import topological_sort_python
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if __name__ == '__main__':
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genome = AdvanceInitialize(
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num_inputs=17,
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num_outputs=6,
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hidden_cnt=8,
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max_nodes=50,
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max_conns=500,
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)
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state = genome.setup()
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randkey = jax.random.PRNGKey(42)
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nodes, conns = genome.initialize(state, randkey)
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network = genome.network_dict(state, nodes, conns)
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print(set(network["nodes"]), set(network["conns"]))
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order, _ = topological_sort_python(set(network["nodes"]), set(network["conns"]))
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print(order)
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input_idx, output_idx = genome.get_input_idx(), genome.get_output_idx()
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print(input_idx, output_idx)
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print(genome.repr(state, nodes, conns))
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print(network)
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2455
tensorneat/examples/interpret_visualize/graph.svg
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2455
tensorneat/examples/interpret_visualize/graph.svg
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After Width: | Height: | Size: 90 KiB |
191
tensorneat/examples/interpret_visualize/network.json
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191
tensorneat/examples/interpret_visualize/network.json
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@@ -0,0 +1,191 @@
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{
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"nodes": {
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"0": {
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"bias": 0.13710324466228485,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"1": {
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"bias": -1.4202250242233276,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"2": {
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"bias": -0.4653860926628113,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"3": {
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"bias": 0.5835710167884827,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"4": {
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"bias": 2.187405824661255,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"5": {
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"bias": 0.24963024258613586,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"6": {
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"bias": -0.966821551322937,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"7": {
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"bias": 0.4452081620693207,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"8": {
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"bias": -0.07293166220188141,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"9": {
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"bias": -0.1625899225473404,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"10": {
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"bias": -0.8576332330703735,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"11": {
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"bias": -0.18487468361854553,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"12": {
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"bias": 1.4335486888885498,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"13": {
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"bias": -0.8690621256828308,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"14": {
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"bias": -0.23014676570892334,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"15": {
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"bias": 0.7880322337150574,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"16": {
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"bias": -0.22258250415325165,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"17": {
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"bias": 0.2773352861404419,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"18": {
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"bias": -0.40279051661491394,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"19": {
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"bias": 1.092000961303711,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"20": {
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"bias": -0.4063087999820709,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"21": {
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"bias": 0.3895529806613922,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"22": {
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"bias": -0.18007506430149078,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"23": {
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"bias": -0.8112533092498779,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"24": {
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"bias": 0.2946726381778717,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"25": {
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"bias": -1.118497371673584,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"26": {
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"bias": 1.3674490451812744,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"27": {
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"bias": -1.6514816284179688,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"28": {
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"bias": 0.9440701603889465,
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"res": 1.0,
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"agg": "sum",
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"act": "sigmoid"
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},
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"29": {
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"bias": 1.564852237701416,
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"res": 1.0,
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"agg": "sum",
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||||
"act": "sigmoid"
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},
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"30": {
|
||||
"bias": -0.5568665266036987,
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"res": 1.0,
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"agg": "sum",
|
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"act": "sigmoid"
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}
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},
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"conns": {
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2455
tensorneat/examples/interpret_visualize/network.svg
Normal file
2455
tensorneat/examples/interpret_visualize/network.svg
Normal file
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Load Diff
|
After Width: | Height: | Size: 89 KiB |
103
tensorneat/examples/interpret_visualize/visualize_genome.ipynb
Normal file
103
tensorneat/examples/interpret_visualize/visualize_genome.ipynb
Normal file
File diff suppressed because one or more lines are too long
13
tensorneat/examples/interpret_visualize/visualize_genome.py
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13
tensorneat/examples/interpret_visualize/visualize_genome.py
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import networkx as nx
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import matplotlib.pyplot as plt
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# 创建一个空白的有向图
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G = nx.DiGraph()
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# 添加边
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G.add_edge('A', 'B')
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G.add_edge('A', 'C')
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G.add_edge('B', 'C')
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G.add_edge('C', 'D')
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# 绘制有向图
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