linear_regression_gluon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:40.761222Z",
     "start_time": "2018-02-06T19:02:39.491082Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入mxnet\n",
    "import random\n",
    "import mxnet as mx\n",
    "\n",
    "# 导入mxnet的gluon, ndarray, autograd\n",
    "from mxnet import gluon\n",
    "from mxnet import autograd\n",
    "from mxnet import ndarray as nd\n",
    "\n",
    "# 设置随机种子\n",
    "mx.random.seed(1)\n",
    "random.seed(1)\n",
    "\n",
    "# 训练数据的维度\n",
    "num_inputs = 2\n",
    "\n",
    "# 训练数据的样本数量\n",
    "num_examples = 1000\n",
    "\n",
    "# 实际的权重w\n",
    "true_w = [2, -3.4]\n",
    "\n",
    "# 实际的偏置b\n",
    "true_b = 4.2\n",
    "\n",
    "# 随机生成均值为0, 方差为1, 服从正态分布的训练数据X, \n",
    "X = nd.random_normal(shape=(num_examples, num_inputs))\n",
    "\n",
    "# 根据X, w, b生成对应的输出y\n",
    "y = true_w[0] * X[:, 0] + true_w[1] * X[:, 1] + true_b \n",
    "\n",
    "# 给y加上随机噪声\n",
    "y += 0.01 * nd.random_normal(shape=y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.230757Z",
     "start_time": "2018-02-06T19:02:40.762933Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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P+b1kMfw2Y5+1GimzKUi9Q8FASBlIjzGIehMK8TCIKMOlAh+DGQubNl0jx48f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\nXl22tgYu+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109539210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制数据的散点图 \n",
    "plt.scatter(X[:, 1].asnumpy(), y.asnumpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.236814Z",
     "start_time": "2018-02-06T19:02:41.232498Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 训练时的批数据大小\n",
    "batch_size = 10\n",
    "\n",
    "# 创建数据集\n",
    "dataset = gluon.data.ArrayDataset(X, y)\n",
    "\n",
    "# 读取数据\n",
    "data_iter = gluon.data.DataLoader(dataset, batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.245468Z",
     "start_time": "2018-02-06T19:02:41.238256Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[[-2.11255503  0.61242002]\n",
      " [ 2.18546367 -0.48856559]\n",
      " [ 0.91085583  0.38985687]\n",
      " [-0.56097323  1.44421673]\n",
      " [ 0.31765923 -1.75729597]\n",
      " [-0.57738042  2.03963804]\n",
      " [-0.91808975  0.64181799]\n",
      " [-0.20269176  0.21012937]\n",
      " [-0.22549874  0.19895147]\n",
      " [ 1.42844415  0.06982213]]\n",
      "<NDArray 10x2 @cpu(0)> \n",
      "[ -2.11691356  10.22533131   4.70613146  -1.82755637  10.82125568\n",
      "  -3.88111711   0.17608714   3.07074499   3.06542921   6.82972908]\n",
      "<NDArray 10 @cpu(0)>\n"
     ]
    }
   ],
   "source": [
    "# 查看数据\n",
    "for data, label in data_iter:\n",
    "    print data, label\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.250556Z",
     "start_time": "2018-02-06T19:02:41.247275Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 定义一个空的模型\n",
    "net = gluon.nn.Sequential()\n",
    "\n",
    "# 加入一个Dense层\n",
    "net.add(gluon.nn.Dense(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.255395Z",
     "start_time": "2018-02-06T19:02:41.251992Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.260791Z",
     "start_time": "2018-02-06T19:02:41.257514Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "square_loss = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T18:12:42.920568Z",
     "start_time": "2018-02-06T18:12:42.917424Z"
    }
   },
   "source": [
    "## 优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.265893Z",
     "start_time": "2018-02-06T19:02:41.262365Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-06T19:02:41.867845Z",
     "start_time": "2018-02-06T19:02:41.267689Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, average loss: 7.403182\n",
      "Epoch 1, average loss: 0.854247\n",
      "Epoch 2, average loss: 0.099864\n",
      "Epoch 3, average loss: 0.011887\n",
      "Epoch 4, average loss: 0.001479\n"
     ]
    }
   ],
   "source": [
    "# 定义训练的迭代周期\n",
    "epochs = 5\n",
    "\n",
    "# 训练\n",
    "for epoch in xrange(epochs):\n",
    "    # 总的loss\n",
    "    total_loss = 0\n",
    "    for data, label in data_iter:\n",
    "        # 记录梯度\n",
    "        with autograd.record():\n",
    "            # 计算预测值\n",
    "            output = net(data)\n",
    "            # 计算loss\n",
    "            loss = square_loss(output, label)\n",
    "        # 根据loss进行反向传播计算梯度\n",
    "        loss.backward()\n",
    "        # 更新权重, batch_size用来进行梯度平均\n",
    "        trainer.step(batch_size)\n",
    "        # 计算总的loss\n",
    "        total_loss += nd.sum(loss).asscalar()\n",
    "        \n",
    "    print \"Epoch %d, average loss: %f\" % (epoch, total_loss/num_examples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考资料\n",
    "\n",
    "* ArrayDataset  \n",
    "[https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.ArrayDataset](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.ArrayDataset)  \n",
    "\n",
    "* DataLoader  \n",
    "[https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataload#mxnet.gluon.data.DataLoader](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataload#mxnet.gluon.data.DataLoader)  \n",
    "\n",
    "* Sequential  \n",
    "[https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=gluon.nn.sequential#mxnet.gluon.nn.Sequential](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=gluon.nn.sequential#mxnet.gluon.nn.Sequential)  \n",
    "\n",
    "* L2Loss  \n",
    "[https://mxnet.incubator.apache.org/api/python/gluon/loss.html?highlight=l2loss#mxnet.gluon.loss.L2Loss](https://mxnet.incubator.apache.org/api/python/gluon/loss.html?highlight=l2loss#mxnet.gluon.loss.L2Loss)  \n",
    "\n",
    "* Trainer  \n",
    "[https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=trainer#mxnet.gluon.Trainer](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=trainer#mxnet.gluon.Trainer)  "
   ]
  }
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