fix linear regressions code in notebook

tutorials/notebook/linear_regression.ipynb

@@ -34,6 +34,44 @@
     "5. 执行训练"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 环境准备"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "系统：Ubuntu18.04\n",
+    "\n",
+    "MindSpore版本：GPU\n",
+    "\n",
+    "设置MindSpore运行配置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=context.PYNATIVE_MODE, device_target=\"GPU\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`PYNATIVE_MODE`：自定义调试模式。\n",
+    "\n",
+    "`device_target`：设置MindSpore的训练硬件为GPU。"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -47,22 +85,18 @@
    "source": [
     "### 定义数据集生成函数\n",
     "\n",
-    "`get_data`用于生成训练数据集和测试数据集。由于拟合的是线性数据，假定要拟合的目标函数为：$y=2x+3$，那么我们需要的训练数据集应随机分布于函数周边，这里采用了`y=2x+3+noise`的方式生成，其中`noise`为遵循标准正态分布规律的随机数值。"
+    "`get_data`用于生成训练数据集和测试数据集。由于拟合的是线性数据，假定要拟合的目标函数为：$y=2x+3$，那么我们需要的训练数据集应随机分布于函数周边，这里采用了$y=2x+3+noise$的方式生成，其中`noise`为遵循标准正态分布规律的随机数值。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import mindspore as ms\n",
     "from mindspore import Tensor\n",
-    "from mindspore import context\n",
-    "from mindspore.train import Model\n",
-    "\n",
-    "context.set_context(mode=context.PYNATIVE_MODE, device_target=\"GPU\")\n",
     " \n",
     "def get_data(num,w=2.0, b=3.0):\n",
     "    np_x = np.ones([num, 1])\n",
@@ -102,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -157,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -296,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -469,12 +503,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "在MindSpore中的所有要编入计算图的类都需要继承`nn.Cell`算子。MindSpore的梯度计算函数采用如下方式。"
+    "在MindSpore中的所有要编入计算图的类都需要继承`nn.Cell`算子，MindSpore的梯度计算函数采用如下方式。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,12 +546,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`nn.RMSProp`为完成权重更新的函数，更新方式大致为公式10，但是考虑的因素更多，具体信息请参考官网说明：<www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html?highlight=rmsprop#mindspore.nn.RMSProp>"
+    "`nn.RMSProp`为完成权重更新的函数，更新方式大致为公式10，但是考虑的因素更多，具体信息请参考[官网说明](www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html?highlight=rmsprop#mindspore.nn.RMSProp)。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -549,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -561,6 +595,7 @@
     "    plt.scatter(x1,y1,color=\"red\",s=5)\n",
     "    plt.scatter(data_x.asnumpy(), data_y.asnumpy(), color=\"black\", s=5)\n",
     "    plt.plot(x, y, \"blue\")\n",
+    "    plt.axis([-11, 11, -20, 25])\n",
     "    plt.show()\n",
     "    time.sleep(0.02)"
    ]
@@ -573,7 +608,7 @@
     "\n",
     "- `weight`：模型函数的权重，即$w$。\n",
     "\n",
-    "- `bias`：模型函数的权重，既$b$。\n",
+    "- `bias`：模型函数的权重，即$b$。\n",
     "\n",
     "- `data_x`：训练数据的x值。\n",
     "\n",
@@ -612,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },