{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2024-05-30T15:07:59.805322900Z", "start_time": "2024-05-30T15:07:57.075364700Z" } }, "outputs": [], "source": [ "import jax, jax.numpy as jnp\n", "from algorithm.neat.genome import *\n", "from algorithm.neat.gene import *\n", "\n", "jnp.set_printoptions(precision=2, linewidth=150)" ] }, { "cell_type": "code", "execution_count": 2, "outputs": [], "source": [ "# genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10)\n", "# state = genome.setup()\n", "# randkey = jax.random.key(0)\n", "# genome_key, input_key = jax.random.split(randkey)\n", "# nodes, conns = genome.initialize(state, genome_key)\n", "# inputs = jax.random.normal(input_key, (10, 3)) * 2 + 1 # std: 2, mean: 1\n", "# print(nodes, conns, sep='\\n')\n", "# print(inputs)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:07:59.817325200Z", "start_time": "2024-05-30T15:07:59.809324300Z" } }, "id": "c81fa2df52f01d93" }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "# transformed = genome.transform(state, nodes, conns)\n", "# batch_output = jax.vmap(genome.forward, in_axes=(None, 0, None))(state, inputs, transformed)\n", "# batch_output, transformed" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:07:59.817950Z", "start_time": "2024-05-30T15:07:59.812323Z" } }, "id": "d4b9aa0449c8d706" }, { "cell_type": "code", "execution_count": 4, "outputs": [], "source": [ "# batch_output2, new_transformed = genome.update_by_batch(state, inputs, transformed)\n", "# batch_output2, new_transformed" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:07:59.831323800Z", "start_time": "2024-05-30T15:07:59.821324100Z" } }, "id": "d32986470dad3229" }, { "cell_type": "code", "execution_count": 5, "outputs": [], "source": [ "# assert jnp.allclose(new_transformed[0], transformed[0], equal_nan=True)\n", "# assert jnp.allclose(new_transformed[1], transformed[1], equal_nan=True)\n", "# assert jnp.allclose(new_transformed[2], transformed[2], equal_nan=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:07:59.832325200Z", "start_time": "2024-05-30T15:07:59.826324400Z" } }, "id": "3c4007dfd6770faf" }, { "cell_type": "code", "execution_count": 6, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 0. 0. 0. 1. 1. 0.]\n", " [ 1. 0. 0. 0. 0. 1. 1. 0.]\n", " [ 2. 0. 0. 0. 0. 1. 1. 0.]\n", " [ 3. 0. 0. 0. 0. 1. 1. 0.]\n", " [ 4. 0. 0. 0. 0. 1. 1. 0.]\n", " [ 5. 0. 0. 0. 0. 1. 1. 0.]\n", " [nan 0. 0. 0. 0. 1. 1. 0.]\n", " [nan 0. 0. 0. 0. 1. 1. 0.]\n", " [nan 0. 0. 0. 0. 1. 1. 0.]\n", " [nan 0. 0. 0. 0. 1. 1. 0.]]\n", "[[ 0. 5. 1. 1.]\n", " [ 1. 5. 1. 1.]\n", " [ 2. 5. 1. 1.]\n", " [ 5. 3. 1. 1.]\n", " [ 5. 4. 1. 1.]\n", " [nan nan nan 1.]\n", " [nan nan nan 1.]\n", " [nan nan nan 1.]\n", " [nan nan nan 1.]\n", " [nan nan nan 1.]]\n", "[[-1.9 -3.53 0.94]\n", " [ 2.92 0.06 3.44]\n", " [-0.9 -0.06 2.94]\n", " ...\n", " [ 2.07 -1.43 1.55]\n", " [ 1.93 2.85 0.19]\n", " [ 0.91 -0.65 1.86]]\n" ] }, { "data": { "text/plain": "(Array([ 0, 1, 2, 5, 3, 4, 2147483647, 2147483647, 2147483647, 2147483647], dtype=int32, weak_type=True),\n Array([[ 0., 0., 0., 0., 0., 1., 1., 0.],\n [ 1., 0., 0., 0., 0., 1., 1., 0.],\n [ 2., 0., 0., 0., 0., 1., 1., 0.],\n [ 3., 0., 0., 0., 0., 1., 1., 0.],\n [ 4., 0., 0., 0., 0., 1., 1., 0.],\n [ 5., 0., 0., 0., 0., 1., 1., 0.],\n [nan, 0., 0., 0., 0., 1., 1., 0.],\n [nan, 0., 0., 0., 0., 1., 1., 0.],\n [nan, 0., 0., 0., 0., 1., 1., 0.],\n [nan, 0., 0., 0., 0., 1., 1., 0.]], dtype=float32, weak_type=True),\n Array([[[nan, nan, nan, nan, nan, 1., nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, 1., nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, 1., nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, 1., 1., nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]]], dtype=float32, weak_type=True))" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from algorithm.neat.gene.node.normalized import NormalizedNode\n", "from algorithm.neat.gene.conn import DefaultConnGene\n", "from tensorneat.utils import ACT\n", "\n", "genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10,\n", " node_gene=NormalizedNode(activation_default=ACT.identity, activation_options=(ACT.identity,)),\n", " conn_gene=DefaultConnGene(weight_init_mean=1))\n", "state = genome.setup()\n", "randkey = jax.random.key(0)\n", "genome_key, input_key = jax.random.split(randkey)\n", "nodes, conns = genome.initialize(state, genome_key)\n", "nodes = nodes.at[:, 1:].set(genome.node_gene.new_custom_attrs(state))\n", "conns = conns.at[:, 3:].set(genome.conn_gene.new_custom_attrs(state))\n", "\n", "inputs = jax.random.normal(input_key, (10000, 3)) * 2 + 1 # std: 2, mean: 1\n", "print(nodes, conns, sep='\\n')\n", "print(inputs)\n", "transformed = genome.transform(state, nodes, conns)\n", "transformed" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:08:04.532243100Z", "start_time": "2024-05-30T15:07:59.832325200Z" } }, "id": "da73909c3414366e" }, { "cell_type": "code", "execution_count": 7, "outputs": [ { "data": { "text/plain": "Array([[-4.49, -4.49],\n [ 6.42, 6.42],\n [ 1.98, 1.98],\n ...,\n [ 2.19, 2.19],\n [ 4.97, 4.97],\n [ 2.12, 2.12]], dtype=float32, weak_type=True)" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_output2 = jax.vmap(genome.forward, in_axes=(None, 0, None))(state, inputs, transformed)\n", "batch_output2" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:08:04.901593900Z", "start_time": "2024-05-30T15:08:04.527245300Z" } }, "id": "8ef2402bc4c7908d" }, { "cell_type": "code", "execution_count": 8, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "batch_z: [-4.49 6.42 1.98 ... 2.19 4.97 2.12]\n", "batch_z_mean: 2.9496588706970215\n", "batch_z: [-2.15 1. -0.28 ... -0.22 0.58 -0.24]\n", "batch_z_mean: -2.1362303925798187e-08\n", "batch_z: [-2.15 1. -0.28 ... -0.22 0.58 -0.24]\n", "batch_z_mean: -2.1362303925798187e-08\n" ] } ], "source": [ "batch_output, new_transformed = genome.update_by_batch(state, inputs, transformed)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:08:05.269935400Z", "start_time": "2024-05-30T15:08:04.899594200Z" } }, "id": "b3c085c7ca28f127" }, { "cell_type": "code", "execution_count": 9, "outputs": [ { "data": { "text/plain": "(Array([[-2.15, -2.15],\n [ 1. , 1. ],\n [-0.28, -0.28],\n ...,\n [-0.22, -0.22],\n [ 0.58, 0.58],\n [-0.24, -0.24]], dtype=float32, weak_type=True),\n (Array([ 0, 1, 2, 5, 3, 4, 2147483647, 2147483647, 2147483647, 2147483647], dtype=int32, weak_type=True),\n Array([[ 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00],\n [ 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00],\n [ 2.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00],\n [ 3.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -2.14e-08, 1.00e+00, 1.00e+00, 0.00e+00],\n [ 4.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -2.14e-08, 1.00e+00, 1.00e+00, 0.00e+00],\n [ 5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 2.95e+00, 3.46e+00, 1.00e+00, 0.00e+00],\n [ nan, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00],\n [ nan, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00],\n [ nan, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00],\n [ nan, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 1.00e+00, 0.00e+00]], dtype=float32, weak_type=True),\n Array([[[nan, nan, nan, nan, nan, 1., nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, 1., nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, 1., nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, 1., 1., nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]]], dtype=float32, weak_type=True)))" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_output, new_transformed" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:08:05.270935800Z", "start_time": "2024-05-30T15:08:05.261936200Z" } }, "id": "60ce6747ebd95e10" }, { "cell_type": "code", "execution_count": 10, "outputs": [ { "data": { "text/plain": "Array([[-2.15, -2.15],\n [ 1. , 1. ],\n [-0.28, -0.28],\n ...,\n [-0.22, -0.22],\n [ 0.58, 0.58],\n [-0.24, -0.24]], dtype=float32, weak_type=True)" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_output2 = jax.vmap(genome.forward, in_axes=(None, 0, None))(state, inputs, new_transformed)\n", "batch_output2" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:08:05.415934Z", "start_time": "2024-05-30T15:08:05.269935400Z" } }, "id": "7b092224d8f33b7" }, { "cell_type": "code", "execution_count": 10, "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-05-30T15:08:05.416935400Z", "start_time": "2024-05-30T15:08:05.405934300Z" } }, "id": "eec974242eb3867e" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }