fix: 🐛 Fix dependencies and blinder link
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84D3DF1528A84511
@ -1 +1,184 @@
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{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Notebook entrainement d'une IA à résoudre le XOR\n[](https://mybinder.org/v2/git/http%3A%2F%2Fgitea.louisgallet.fr%2FCours-particulier%2FNotebooks/master?urlpath=%2Fdoc%2Ftree%2FNotebook_IA_Training.ipynb)\n\nLa table de veritée du XOR est la suivante:\n> x: entrée 1 ; y: entrée 2 ; r: sortie\n\nx = 0 ; y = 0 ; r = 0 \nx = 1 ; y = 0 ; r = 1 \nx = 0 ; y = 1 ; r = 1 \nx = 1 ; y = 1 ; r = 0 "},{"metadata":{"id":"xwVyblbIKSYS","trusted":true},"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"id":"wgVHhV0zKeOV","trusted":true},"cell_type":"code","source":"def sigmoid(x):\n return 1 / (1 + np.exp(-x))\n\ndef sigmoid_derivative(x):\n return x * (1 - x)","execution_count":null,"outputs":[]},{"metadata":{"id":"Bv03so9jKhZH","trusted":true},"cell_type":"code","source":"# Entrées et sorties du XOR\nX = np.array([[0,0],[0,1],[1,0],[1,1]])\ny = np.array([[0],[1],[1],[0]])","execution_count":null,"outputs":[]},{"metadata":{"id":"-utexs_vKjws","trusted":true},"cell_type":"code","source":"np.random.seed(42)\n\n# 2 neurones en entrée, 3 neurones cachés, 1 neurone en sortie\nw1 = np.random.randn(2, 4)\nb1 = np.zeros((1, 4))\n\nw2 = np.random.randn(4, 1)\nb2 = np.zeros((1, 1))\n\n# Pour enregistrer la perte au fil des époques\nloss_history = []","execution_count":null,"outputs":[]},{"metadata":{"id":"AGlpWtKdKmcZ","trusted":true},"cell_type":"code","source":"learning_rate = 0.1\nepochs = 10000\n\nfor epoch in range(epochs):\n # Forward pass\n z1 = np.dot(X, w1) + b1\n a1 = sigmoid(z1)\n\n z2 = np.dot(a1, w2) + b2\n a2 = sigmoid(z2)\n\n # Calcul de l'erreur\n loss = np.mean((y - a2) ** 2)\n loss_history.append(loss)\n\n # Backpropagation\n d_a2 = (a2 - y)\n d_z2 = d_a2 * sigmoid_derivative(a2)\n\n d_w2 = np.dot(a1.T, d_z2)\n d_b2 = np.sum(d_z2, axis=0, keepdims=True)\n\n d_a1 = np.dot(d_z2, w2.T)\n d_z1 = d_a1 * sigmoid_derivative(a1)\n\n d_w1 = np.dot(X.T, d_z1)\n d_b1 = np.sum(d_z1, axis=0, keepdims=True)\n\n # Mise à jour des poids\n w2 -= learning_rate * d_w2\n b2 -= learning_rate * d_b2\n w1 -= learning_rate * d_w1\n b1 -= learning_rate * d_b1","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":474},"id":"m60PeuYnKoie","outputId":"d5e217cf-a0b9-49a8-a01b-d9575b7f797f","trusted":true},"cell_type":"code","source":"plt.plot(loss_history)\nplt.title(\"Perte d'erreurs au fil des époques\")\nplt.xlabel(\"Époques\")\nplt.ylabel(\"Taux d'erreur moyen\")\nplt.grid(True)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A2sN1_fHKrh8","outputId":"fa906657-126c-4e13-cb6e-c6e7afd9bfd7","trusted":true},"cell_type":"code","source":"print(\"Sortie finale après entraînement :\")\na1 = sigmoid(np.dot(X, w1) + b1)\na2 = sigmoid(np.dot(a1, w2) + b2)\nfor i in range(4):\n print(f\"Entrée : {X[i]} → Prédit : {a2[i][0]:.4f} → Arrondi : {round(a2[i][0])}\")","execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"nbformat":4,"nbformat_minor":2}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Notebook entrainement d'une IA à résoudre le XOR\n",
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"[](https://mybinder.org/v2/git/http%3A%2F%2Fgitea.louisgallet.fr%2FCours-particulier%2FNotebooks/master?urlpath=%2Fdoc%2Ftree%2FNotebook_IA_Training.ipynb)\n",
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"\n",
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"La table de veritée du XOR est la suivante:\n",
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"> x: entrée 1 ; y: entrée 2 ; r: sortie\n",
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"\n",
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"x = 0 ; y = 0 ; r = 0 \n",
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"x = 1 ; y = 0 ; r = 1 \n",
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"x = 0 ; y = 1 ; r = 1 \n",
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"x = 1 ; y = 1 ; r = 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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"id": "xwVyblbIKSYS",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"!pip install numpy\n",
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"!pip install matplotlib\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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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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"id": "wgVHhV0zKeOV",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"def sigmoid(x):\n",
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" return 1 / (1 + np.exp(-x))\n",
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"\n",
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"def sigmoid_derivative(x):\n",
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" return x * (1 - x)"
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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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"id": "Bv03so9jKhZH",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"# Entrées et sorties du XOR\n",
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"X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
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"y = np.array([[0],[1],[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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"id": "-utexs_vKjws",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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"\n",
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"# 2 neurones en entrée, 3 neurones cachés, 1 neurone en sortie\n",
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"w1 = np.random.randn(2, 4)\n",
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"b1 = np.zeros((1, 4))\n",
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"\n",
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"w2 = np.random.randn(4, 1)\n",
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"b2 = np.zeros((1, 1))\n",
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"\n",
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"# Pour enregistrer la perte au fil des époques\n",
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"loss_history = []"
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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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"id": "AGlpWtKdKmcZ",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"learning_rate = 0.1\n",
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"epochs = 10000\n",
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"\n",
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"for epoch in range(epochs):\n",
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" # Forward pass\n",
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" z1 = np.dot(X, w1) + b1\n",
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" a1 = sigmoid(z1)\n",
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"\n",
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" z2 = np.dot(a1, w2) + b2\n",
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" a2 = sigmoid(z2)\n",
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"\n",
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" # Calcul de l'erreur\n",
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" loss = np.mean((y - a2) ** 2)\n",
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" loss_history.append(loss)\n",
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"\n",
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" # Backpropagation\n",
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" d_a2 = (a2 - y)\n",
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" d_z2 = d_a2 * sigmoid_derivative(a2)\n",
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"\n",
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" d_w2 = np.dot(a1.T, d_z2)\n",
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" d_b2 = np.sum(d_z2, axis=0, keepdims=True)\n",
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"\n",
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" d_a1 = np.dot(d_z2, w2.T)\n",
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" d_z1 = d_a1 * sigmoid_derivative(a1)\n",
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"\n",
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" d_w1 = np.dot(X.T, d_z1)\n",
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" d_b1 = np.sum(d_z1, axis=0, keepdims=True)\n",
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"\n",
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" # Mise à jour des poids\n",
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" w2 -= learning_rate * d_w2\n",
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" b2 -= learning_rate * d_b2\n",
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" w1 -= learning_rate * d_w1\n",
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" b1 -= learning_rate * d_b1"
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 474
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},
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"id": "m60PeuYnKoie",
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"outputId": "d5e217cf-a0b9-49a8-a01b-d9575b7f797f",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"plt.plot(loss_history)\n",
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"plt.title(\"Perte d'erreurs au fil des époques\")\n",
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"plt.xlabel(\"Époques\")\n",
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"plt.ylabel(\"Taux d'erreur moyen\")\n",
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"plt.grid(True)\n",
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"plt.show()"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "A2sN1_fHKrh8",
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"outputId": "fa906657-126c-4e13-cb6e-c6e7afd9bfd7",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"print(\"Sortie finale après entraînement :\")\n",
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"a1 = sigmoid(np.dot(X, w1) + b1)\n",
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"a2 = sigmoid(np.dot(a1, w2) + b2)\n",
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"for i in range(4):\n",
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" print(f\"Entrée : {X[i]} → Prédit : {a2[i][0]:.4f} → Arrondi : {round(a2[i][0])}\")"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -1,4 +1,4 @@
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# IA Training XOR
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[](https://mybinder.org/v2/git/http%3A%2F%2Fgitea.louisgallet.fr%2FCours-particulier%2FNotebooks/master?urlpath=%2Fdoc%2Ftree%2FNotebook_IA_Training.ipynb)
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[](https://mybinder.org/v2/git/https%3A%2F%2Fgitea.louisgallet.fr%2FCours-particulier%2FNotebooks/master?urlpath=%2Fdoc%2Ftree%2FIA_Training%2FNotebook_IA_Training.ipynb)
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This is a simple XOR neural network training script.
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