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experiments: add quantization experiment (with timestep issues)
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‎notebooks/quantization.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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import plotly.express as px\n",
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"import networkx\n",
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"from loguru import logger\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"from pim.models.network import Network\n",
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"from pim.models.new.stone import StoneExperiment, StoneResults\n",
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"from pim.models.new.stone.rate import CXRatePontin, CPU4PontinLayer\n",
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"\n",
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"from pim.models.stone import analysis\n",
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"\n",
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"logger.remove()\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(0, 1, 100)\n",
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"bins = np.linspace(0, 1, 10, endpoint=False)\n",
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"print(bins)\n",
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"y = (np.digitize(x, bins)-1) / 10\n",
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"plt.plot(x, x)\n",
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"plt.plot(x, y)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def create_quantized_layer(N):\n",
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" def closure(*args, **kwargs):\n",
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" return QuantizedCPU4PontinLayer(N, *args, **kwargs)\n",
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" return closure\n",
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"\n",
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"class QuantizedCPU4PontinLayer(CPU4PontinLayer):\n",
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" def __init__(self, N, *args, **kwargs):\n",
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" super().__init__(*args, **kwargs)\n",
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" self.N = N\n",
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" self.bins = np.linspace(0, 1, self.N, endpoint = False)\n",
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" \n",
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" def step(self, network: Network, dt: float):\n",
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" \"\"\"Memory neurons update.\n",
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" cpu4[0-7] store optic flow peaking at left 45 deg\n",
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" cpu[8-15] store optic flow peaking at right 45 deg.\"\"\"\n",
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" tb1 = network.output(self.TB1)\n",
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" tn1 = network.output(self.TN1) * dt\n",
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" tn2 = network.output(self.TN2) * dt\n",
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"\n",
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" mem_update = np.dot(self.W_TN, tn2)\n",
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" mem_update -= np.dot(self.W_TB1, tb1)\n",
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" mem_update = np.clip(mem_update, 0, 1)\n",
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" mem_update *= self.gain\n",
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" self.memory += mem_update\n",
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" self.memory -= 0.125 * self.gain * dt\n",
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" self.memory = np.clip((np.digitize(self.memory, self.bins) - 1) / self.N, 0.0, 1.0)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"parameters = {\n",
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" \"model\": \"stone\",\n",
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" \"T_outbound\": 1500,\n",
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" \"T_inbound\": 1000,\n",
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" \"time_subdivision\": 1,\n",
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" \"noise\": 0.1,\n",
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" \"cx\": \"pontin\"\n",
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"}\n",
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"\n",
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"def create_experiment(cpu4):\n",
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" cx = CXRatePontin(CPU4LayerClass=cpu4, noise = parameters[\"noise\"])\n",
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" cx.setup()\n",
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" experiment = StoneExperiment(parameters)\n",
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" experiment.cx = cx\n",
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" return experiment\n",
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"\n",
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"def run_experiment(cpu4, N = 0, ts = 1, report = False):\n",
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" experiment = create_experiment(cpu4)\n",
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" experiment.parameters[\"time_subdivision\"] = ts\n",
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" results = experiment.run(\"test\")\n",
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" if report:\n",
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" results.report()\n",
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" return np.linalg.norm(results.closest_position())\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"run_experiment(CPU4PontinLayer, N=1, ts=10, report=True)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"mean_benchmark = np.mean([run_experiment(CPU4PontinLayer) for i in tqdm(range(0, 10))])\n",
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"print(f\"Benchmark mean: {mean_benchmark}\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"Ns = range(10, 10000, 10)\n",
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"results1 = [run_experiment(create_quantized_layer(N), ts=1) for N in tqdm(Ns)]\n",
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"results2 = [run_experiment(create_quantized_layer(N), ts=2) for N in tqdm(Ns)]\n",
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"results10 = [run_experiment(create_quantized_layer(N), ts=10) for N in tqdm(Ns)]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"px.scatter(x=Ns, y=results1, labels={\"x\": \"resolution\", \"y\": \"smallest distance from nest\"})"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pim",
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"language": "python",
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"name": "pim"
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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": 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": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎requirements.txt

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networkx>=2.8.2
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numpydoc>=1.3.1
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asyncio
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websockets
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websockets
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plotly
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tqdm
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ipywidgets

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