mirror of
https://github.com/ArthurDanjou/ArtStudies.git
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Arthur DANJOU
8cf328e18a
- Updated import order in Point_Fixe.ipynb for consistency. - Changed lambda functions to regular function definitions for clarity in Point_Fixe.ipynb. - Added numpy import in TP1_EDO_EulerExp.ipynb, TP2_Lokta_Volterra.ipynb, and TP3_Convergence.ipynb for better readability. - Modified for loops in TP1_EDO_EulerExp.ipynb and TP2_Lokta_Volterra.ipynb to include strict=False for compatibility with future Python versions.
104 lines
2.6 KiB
Plaintext
104 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c897654e0a140cbd",
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"metadata": {},
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"source": [
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"# Automatic Differentiation\n",
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"\n",
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"### Neural Network\n",
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"\n",
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"Loss function: softmax layer in $\\mathbb{R}^3$\n",
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"\n",
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"Architecture: FC/ReLU 4-5-7-3"
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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": 33,
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"id": "70a4eb1d928b10d0",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-24T15:16:27.015669Z",
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"start_time": "2025-03-24T15:16:23.856887Z"
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}
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},
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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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"Mean Accuracy: 94%\n",
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"STD Accuracy: 3%\n",
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"Max accuracy: 100%\n",
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"Min accuracy: 88%\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from sklearn.datasets import make_classification\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"\n",
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"accuracies = []\n",
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"\n",
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"for _ in range(10):\n",
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" X, y = make_classification(\n",
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" n_samples=1000, n_features=4, n_classes=3, n_clusters_per_class=1\n",
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" )\n",
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"\n",
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" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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" model = MLPClassifier(\n",
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" hidden_layer_sizes=(5, 7), activation=\"relu\", max_iter=10000, solver=\"adam\"\n",
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" )\n",
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" model.fit(X_train, y_train)\n",
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"\n",
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" y_pred = model.predict(X_test)\n",
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" accuracies.append(accuracy_score(y_test, y_pred))\n",
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"\n",
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"print(f\"Mean Accuracy: {np.mean(accuracies) * 100:.0f}%\")\n",
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"print(f\"STD Accuracy: {np.std(accuracies) * 100:.0f}%\")\n",
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"print(f\"Max accuracy: {np.max(accuracies) * 100:.0f}%\")\n",
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"print(f\"Min accuracy: {np.min(accuracies) * 100:.0f}%\")"
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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": "96b6d46883ed5570",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-24T14:37:53.507776Z",
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"start_time": "2025-03-24T14:37:53.505376Z"
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}
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},
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"outputs": [],
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"source": []
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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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