update comment for ch06

ch06_optimizer/README.md

@@ -0,0 +1,8 @@
+
+|代码|说明|
+|---|---|
+|[pytorch_tutorial.ipynb](pytorch_tutorial.ipynb)| 张量的运算与基本操作 |
+|[gradient_descent.ipynb](gradient_descent.ipynb)| 利用PyTorch实现梯度下降法 |
+|[stochastic\_gradient_descent.ipynb](stochastic_gradient_descent.ipynb)| 利用PyTorch实现随机梯度下降法 |
+
+

ch06_optimizer/gradient_descent.ipynb

@@ -26,6 +26,7 @@
    "source": [
     "# 为了使用PyTorch的高层封装函数，我们通过继承Module类来定义函数\n",
     "class Linear(torch.nn.Module):\n",
+    "    \n",
     "    def __init__(self):\n",
     "        \"\"\"\n",
     "        定义线性回归模型的参数：a, b\n",

ch06_optimizer/pytorch_tutorial.ipynb

@@ -2,27 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: torch in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (2.0.1)\n",
-      "Requirement already satisfied: jinja2 in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from torch) (2.11.2)\n",
-      "Requirement already satisfied: filelock in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from torch) (3.0.12)\n",
-      "Requirement already satisfied: sympy in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from torch) (1.6.2)\n",
-      "Requirement already satisfied: typing-extensions in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from torch) (3.7.4.3)\n",
-      "Requirement already satisfied: networkx in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from torch) (2.5)\n",
-      "Requirement already satisfied: MarkupSafe>=0.23 in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from jinja2->torch) (1.1.1)\n",
-      "Requirement already satisfied: mpmath>=0.19 in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from sympy->torch) (1.1.0)\n",
-      "Requirement already satisfied: decorator>=4.3.0 in /Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages (from networkx->torch) (4.4.2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# 安装PyTorch\n",
+    "# 安装第三方库\n",
     "!pip install torch"
    ]
   },
@@ -48,8 +32,8 @@
    "source": [
     "import torch\n",
     "\n",
-    "# 创建tensor\n",
-    "## 使用tensor封装的函数创建tensor\n",
+    "# 创建张量（tensor）\n",
+    "## 使用封装的函数创建张量\n",
     "zeros = torch.zeros(2, 3)\n",
     "print(zeros)\n",
     "\n",
@@ -88,7 +72,7 @@
     }
    ],
    "source": [
-    "# 创建tensor\n",
+    "# 创建张量（tensor）\n",
     "## 从Python对象创建\n",
     "data = [[2, 3, 4], [1, 0, 1]]\n",
     "t_data = torch.tensor(data)\n",
@@ -101,7 +85,7 @@
     "tn_data = torch.from_numpy(n_data)\n",
     "print(tn_data)\n",
     "\n",
-    "## Numpy bridge，也就是对numpy对象的改变会传导到tensor\n",
+    "## Numpy bridge，也就是对numpy对象的改变会传导到张量\n",
     "n_data += 1\n",
     "torch.all(torch.from_numpy(n_data) == tn_data)"
    ]
@@ -124,8 +108,7 @@
     }
    ],
    "source": [
-    "# 变换tensor维度\n",
-    "\n",
+    "# 变换张量维度\n",
     "## 增加或减少数据的维度\n",
     "a = torch.rand(3, 4)\n",
     "print(a.shape)\n",
@@ -161,7 +144,7 @@
     }
    ],
    "source": [
-    "# 变换tensor形状\n",
+    "# 变换张量形状\n",
     "data = torch.tensor(range(0, 10))\n",
     "print(data, data.shape)\n",
     "view1 = data.view(2, 5)\n",
@@ -244,7 +227,7 @@
     }
    ],
    "source": [
-    "## tensor广播，tensor broadcasting\n",
+    "## 广播机制（tensor broadcasting）\n",
     "a = torch.tensor(range(1, 7)).view(2, 3)\n",
     "b = torch.tensor(range(1, 4)).view(   3)\n",
     "print(a)\n",
@@ -301,15 +284,15 @@
    ],
    "source": [
     "# 向量运算\n",
-    "# vector x vector\n",
+    "# 向量与向量\n",
     "vec1 = torch.randn(3)\n",
     "vec2 = torch.randn(3)\n",
     "print((vec1 @ vec2).shape)\n",
-    "# matrix x vector\n",
+    "# 矩阵与向量\n",
     "mat = torch.randn(3, 4)\n",
     "vec = torch.randn(4)\n",
     "print((mat @ vec).shape)\n",
-    "# batched matrix x broadcasted vector\n",
+    "# 张量与向量\n",
     "mat = torch.randn(10, 3, 4)\n",
     "vec = torch.randn(4)\n",
     "print((mat @ vec).shape)"