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https://github.com/guezoloic/neural-network.git
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fix: rework partial main.py (might change soon)
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157
nnetwork.ipynb
157
nnetwork.ipynb
@@ -1,160 +1,5 @@
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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": "9b3f1635",
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"metadata": {},
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"source": [
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"# Neural Network"
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]
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},
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{
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"cell_type": "markdown",
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"id": "478651c8",
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"metadata": {},
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"source": [
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"## What is a *Neuron* (artifical)\n",
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"\n",
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"First of all, **I'm not an Neurologist so i might say some nonsense, i only researched online**. \n",
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"\n",
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"An artifical *neuron* works similary to a biological *neuron* in the way it process information. In a brain, like yours, a *neuron* receives signals from other *neurons*, processes them and sends an *output*.\n",
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"\n",
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"An artifical *neuron* takes an **input** (such as numbers), applies updated values called **weights** to each *inputs*, adds a constant called **bias**, apply a specific function to normalize the value called **Activation function**, and then `returns` the *output* of the Activation function (such as: **sigmoid**, **ReLU**, etc...).\n",
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"\n",
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"## Vocabulary / key components\n",
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"\n",
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"1. **inputs**: inputs are usually a unique list of numbers, they are simply values sent to a neuron, which then process them.\n",
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"\n",
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"2. **weights**: weights are also a list of numbers that has the same size of inputs. The weight determines how important de the number of the input is. If it's high, the input matters. Else, if the weight is low, the number matters less.\n",
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"\n",
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"3. **bias**: the bias are constant that are added after all the inputs are multiplied by the weight. it helps shift the resultat up or down.\n",
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"\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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"id": "7d9d6072",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"\n",
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"# Neuron 1\n",
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"class Neuron:\n",
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" def __init__(self, input_size: int) -> None:\n",
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" self.input_size = input_size\n",
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" self.weight = [random.uniform(0, 1) for _ in range(self.input_size)]\n",
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" self.bias = random.uniform(0, 1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1aff9ee6",
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"metadata": {},
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"source": [
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"# 2"
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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": "7ca39a42",
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import random\n",
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"\n",
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"# Neuron 2\n",
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"class Neuron:\n",
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" def __init__(self, input_size: int) -> None:\n",
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" self.input_size = input_size\n",
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" self.weight = [random.uniform(0, 1) for _ in range(self.input_size)]\n",
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" self.bias = random.uniform(0, 1)\n",
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"\n",
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" def sigmoid(x: float) -> float:\n",
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" return 1/(1 + math.exp(-x))\n",
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" \n",
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" def forward(self, inputs: list) -> float:\n",
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" assert len(inputs) == self.input_size, \"error: misnumber inputs number\"\n",
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" total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
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" return self.sigmoid(total)"
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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": "6709c5c7",
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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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"Neuron output : 0.9001175686881125\n"
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]
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}
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],
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"source": [
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"# 8 for 8 bits (1 Byte)\n",
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"nbits: int = 8\n",
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"neuron = Neuron(nbits)\n",
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"inputs: list = [1, 0, 1, 0, 0, 1, 1, 0] \n",
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"\n",
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"output = neuron.forward(inputs)\n",
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"print(\"Neuron output :\", output)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aa57ae8e",
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"metadata": {},
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"source": [
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"# 3"
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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": "f6de25ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import random\n",
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"\n",
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"# Neuron 3\n",
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"class Neuron:\n",
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" def __init__(self, isize: int) -> None:\n",
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" self.isize = isize\n",
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" self.weight = [random.uniform(0, 1) for _ in range(self.isize)]\n",
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" self.bias = random.uniform(0, 1)\n",
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"\n",
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" def forward(self, inputs: list) -> float:\n",
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" assert len(inputs) == self.isize, \"error: incorrect inputs number\"\n",
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" total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
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" return self.sigmoid(total)\n",
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" \n",
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" def sigmoid(x: float) -> float:\n",
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" return 1/(1 + math.exp(-x))\n",
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"\n",
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" # target needs to be between 0 and 1\n",
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" def train(self, inputs: list, target: float, learning_rate: float = 0.1):\n",
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" z = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
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" output = self.sigmoid(z)\n",
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"\n",
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" error = output - target\n",
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" d_sigmoid = output * (1 - output)\n",
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" dz = error * d_sigmoid\n",
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"\n",
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" for i in range(self.isize):\n",
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" self.weight[i] -= learning_rate * dz * inputs[i]\n",
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"\n",
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" self.bias -= learning_rate * dz\n"
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]
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}
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],
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"cells": [],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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