1. init

04.ipynb

@@ -2,16 +2,127 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-06-11T15:26:24.930498Z",
+     "start_time": "2025-06-11T15:26:24.925343Z"
+    }
    },
-   "outputs": [],
    "source": [
     "import torch\n",
-    "\n"
-   ]
+    "import numpy as np\n",
+    "\n",
+    "torch.__version__"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.2.1'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-11T15:30:24.600025Z",
+     "start_time": "2025-06-11T15:30:24.594879Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "a = torch.tensor(1)\n",
+    "b = a.item()\n",
+    "print(a)\n",
+    "print(b)"
+   ],
+   "id": "ec73dc2f6feeece4",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(1)\n",
+      "1\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-11T15:30:58.264992Z",
+     "start_time": "2025-06-11T15:30:58.260725Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "a = [1, 2, 3]\n",
+    "b = torch.tensor(a)\n",
+    "c = b.numpy().tolist()\n",
+    "print(c)"
+   ],
+   "id": "6c3e0063d8fcc299",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3]\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-11T15:33:27.945096Z",
+     "start_time": "2025-06-11T15:33:27.939574Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "a = torch.zeros(2, 3, 5)\n",
+    "print(a.shape)\n",
+    "\n",
+    "print(a.size())\n",
+    "\n",
+    "print(a.numel())\n"
+   ],
+   "id": "d04c60d3f01351c2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 3, 5])\n",
+      "torch.Size([2, 3, 5])\n",
+      "30\n"
+     ]
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "x = torch.rand(2, 3, 5)\n",
+    "print(x.shape)"
+   ],
+   "id": "774add1439f9aa94"
   }
  ],
  "metadata": {

main.py

@@ -0,0 +1,4 @@
+import torch
+import numpy as np
+
+torch.__version__