概要

Pythonで「図解！Jupyter Labを徹底解説！(インストール・使い方・拡張機能)」の動作を確認してみました。以下のページを参考にしました。

動作確認

以下のコマンドを実行しました。

$ pip install jupyterlab
$ jupyter lab

以下のファイルを作成しました。

file20231022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46e37bcb-b477-40b5-bcb7-14bb3150332e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8466c957-b9d4-467f-8d44-ed8872546c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 + 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e0ad92b-dc64-4e37-a4a1-1d7de6534a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "b = 3\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f337330-0366-4690-a943-47dbd9b20d16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "def relu(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "x = np.arange(-5.0, 5.0, 0.1)\n",
    "y = relu(x)\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c14fa1a-e187-4b8c-932e-59cc90ada756",
   "metadata": {},
   "source": [
    "# 第一章 活性化関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89b693fd-e51f-42ad-a12a-ac31e78fa38c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

実行画面は以下の通りです。

まとめ

何かの役に立てばと。