318 lines
12 KiB
Plaintext
318 lines
12 KiB
Plaintext
{
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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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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-05-30T15:07:59.805322900Z",
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"start_time": "2024-05-30T15:07:57.075364700Z"
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}
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},
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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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"from algorithm.neat.genome import *\n",
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"from algorithm.neat.gene import *\n",
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"\n",
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"jnp.set_printoptions(precision=2, linewidth=150)"
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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": 2,
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"outputs": [],
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"source": [
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"# genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10)\n",
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"# state = genome.setup()\n",
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"# randkey = jax.random.key(0)\n",
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"# genome_key, input_key = jax.random.split(randkey)\n",
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"# nodes, conns = genome.initialize(state, genome_key)\n",
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"# inputs = jax.random.normal(input_key, (10, 3)) * 2 + 1 # std: 2, mean: 1\n",
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"# print(nodes, conns, sep='\\n')\n",
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"# print(inputs)"
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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": "2024-05-30T15:07:59.817325200Z",
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"start_time": "2024-05-30T15:07:59.809324300Z"
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}
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},
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"id": "c81fa2df52f01d93"
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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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"source": [
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"# transformed = genome.transform(state, nodes, conns)\n",
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"# batch_output = jax.vmap(genome.forward, in_axes=(None, 0, None))(state, inputs, transformed)\n",
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"# batch_output, transformed"
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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": "2024-05-30T15:07:59.817950Z",
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"start_time": "2024-05-30T15:07:59.812323Z"
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}
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},
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"id": "d4b9aa0449c8d706"
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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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"source": [
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"# batch_output2, new_transformed = genome.update_by_batch(state, inputs, transformed)\n",
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"# batch_output2, new_transformed"
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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": "2024-05-30T15:07:59.831323800Z",
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"start_time": "2024-05-30T15:07:59.821324100Z"
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}
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},
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"id": "d32986470dad3229"
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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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"source": [
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"# assert jnp.allclose(new_transformed[0], transformed[0], equal_nan=True)\n",
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"# assert jnp.allclose(new_transformed[1], transformed[1], equal_nan=True)\n",
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"# assert jnp.allclose(new_transformed[2], transformed[2], equal_nan=True)"
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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": "2024-05-30T15:07:59.832325200Z",
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"start_time": "2024-05-30T15:07:59.826324400Z"
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}
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},
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"id": "3c4007dfd6770faf"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0. 0. 0. 0. 0. 1. 1. 0.]\n",
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" [ 1. 0. 0. 0. 0. 1. 1. 0.]\n",
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" [ 2. 0. 0. 0. 0. 1. 1. 0.]\n",
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" [ 3. 0. 0. 0. 0. 1. 1. 0.]\n",
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" [ 4. 0. 0. 0. 0. 1. 1. 0.]\n",
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" [ 5. 0. 0. 0. 0. 1. 1. 0.]\n",
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" [nan 0. 0. 0. 0. 1. 1. 0.]\n",
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" [nan 0. 0. 0. 0. 1. 1. 0.]\n",
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" [nan 0. 0. 0. 0. 1. 1. 0.]\n",
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" [nan 0. 0. 0. 0. 1. 1. 0.]]\n",
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"[[ 0. 5. 1. 1.]\n",
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" [ 1. 5. 1. 1.]\n",
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" [ 2. 5. 1. 1.]\n",
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" [ 5. 3. 1. 1.]\n",
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" [ 5. 4. 1. 1.]\n",
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" [nan nan nan 1.]\n",
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" [nan nan nan 1.]\n",
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" [nan nan nan 1.]\n",
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" [nan nan nan 1.]\n",
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" [nan nan nan 1.]]\n",
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"[[-1.9 -3.53 0.94]\n",
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" [ 2.92 0.06 3.44]\n",
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" [-0.9 -0.06 2.94]\n",
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" ...\n",
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" [ 2.07 -1.43 1.55]\n",
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" [ 1.93 2.85 0.19]\n",
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" [ 0.91 -0.65 1.86]]\n"
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]
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},
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{
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"data": {
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"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))"
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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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"from algorithm.neat.gene.node.normalized import NormalizedNode\n",
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"from algorithm.neat.gene.conn import DefaultConnGene\n",
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"from tensorneat.utils import Act\n",
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"\n",
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"genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10,\n",
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" node_gene=NormalizedNode(activation_default=Act.identity, activation_options=(Act.identity,)),\n",
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" conn_gene=DefaultConnGene(weight_init_mean=1))\n",
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"state = genome.setup()\n",
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"randkey = jax.random.key(0)\n",
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"genome_key, input_key = jax.random.split(randkey)\n",
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"nodes, conns = genome.initialize(state, genome_key)\n",
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"nodes = nodes.at[:, 1:].set(genome.node_gene.new_custom_attrs(state))\n",
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"conns = conns.at[:, 3:].set(genome.conn_gene.new_custom_attrs(state))\n",
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"\n",
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"inputs = jax.random.normal(input_key, (10000, 3)) * 2 + 1 # std: 2, mean: 1\n",
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"print(nodes, conns, sep='\\n')\n",
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"print(inputs)\n",
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"transformed = genome.transform(state, nodes, conns)\n",
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"transformed"
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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": "2024-05-30T15:08:04.532243100Z",
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"start_time": "2024-05-30T15:07:59.832325200Z"
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}
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},
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"id": "da73909c3414366e"
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
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{
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"data": {
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"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)"
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},
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"execution_count": 7,
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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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"batch_output2 = jax.vmap(genome.forward, in_axes=(None, 0, None))(state, inputs, transformed)\n",
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"batch_output2"
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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": "2024-05-30T15:08:04.901593900Z",
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"start_time": "2024-05-30T15:08:04.527245300Z"
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}
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},
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"id": "8ef2402bc4c7908d"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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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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"batch_z: [-4.49 6.42 1.98 ... 2.19 4.97 2.12]\n",
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"batch_z_mean: 2.9496588706970215\n",
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"batch_z: [-2.15 1. -0.28 ... -0.22 0.58 -0.24]\n",
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"batch_z_mean: -2.1362303925798187e-08\n",
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"batch_z: [-2.15 1. -0.28 ... -0.22 0.58 -0.24]\n",
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"batch_z_mean: -2.1362303925798187e-08\n"
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]
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}
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],
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"source": [
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"batch_output, new_transformed = genome.update_by_batch(state, inputs, transformed)"
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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": "2024-05-30T15:08:05.269935400Z",
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"start_time": "2024-05-30T15:08:04.899594200Z"
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}
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},
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"id": "b3c085c7ca28f127"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [
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{
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"data": {
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"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)))"
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},
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"execution_count": 9,
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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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"batch_output, new_transformed"
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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": "2024-05-30T15:08:05.270935800Z",
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"start_time": "2024-05-30T15:08:05.261936200Z"
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}
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},
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"id": "60ce6747ebd95e10"
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"outputs": [
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{
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"data": {
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"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)"
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},
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"execution_count": 10,
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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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"batch_output2 = jax.vmap(genome.forward, in_axes=(None, 0, None))(state, inputs, new_transformed)\n",
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"batch_output2"
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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": "2024-05-30T15:08:05.415934Z",
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"start_time": "2024-05-30T15:08:05.269935400Z"
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}
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},
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"id": "7b092224d8f33b7"
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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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": "2024-05-30T15:08:05.416935400Z",
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"start_time": "2024-05-30T15:08:05.405934300Z"
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}
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},
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"id": "eec974242eb3867e"
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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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