提交 3920a1df 编写于 作者: L lvmingfu

fix linear regressions code in notebook

上级 b29483a6
......@@ -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,$b$。\n",
"- `bias`:模型函数的权重,$b$。\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
},
......
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