线性回归的从零实现.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "l### 设置屏幕宽度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.core.display import display, HTML\n",
    "# display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、导入tensorflow及相关模块，并设置 GPU 使用占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n",
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')] [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\n",
      "物理gpu个数:  2\n",
      "总的gpu个数，其中包括括虚拟Logical gpus:  4\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "print(tf.__version__)\n",
    "\n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0,1\"\n",
    "# 获取物理gpu,cpu对象\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "cpus = tf.config.experimental.list_physical_devices(device_type='CPU')\n",
    "print(gpus, cpus)\n",
    "# 设置当前程序的 物理可见设备范围\n",
    "tf.config.experimental.set_visible_devices(devices=gpus[0], device_type='GPU')\n",
    "\n",
    "#设置仅在需要时申请：\n",
    "# for gpu in gpus:\n",
    "#     tf.config.experimental.set_memory_growth(gpu, True)\n",
    "\n",
    "# 下面的方式是设置Tensorflow固定消耗GPU:0的2GB显存\n",
    "tf.config.experimental.set_virtual_device_configuration(\n",
    "    gpus[0],\n",
    "    [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)]\n",
    ")\n",
    "\n",
    "logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "\n",
    "print('物理gpu个数: ', len(gpus))\n",
    "print('总的gpu个数，其中包括括虚拟Logical gpus: ', len(logical_gpus))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、创建样本点和变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/job:localhost/replica:0/task:0/device:GPU:3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "num_examples = 1000\n",
    "\n",
    "with tf.device('/device:GPU:3'):\n",
    "    w = tf.constant([[2, -3.4]])\n",
    "    b = tf.constant([4.2])\n",
    "    x = tf.random.normal([num_examples, num_inputs], mean=0, stddev=10)\n",
    "    e = tf.random.normal([num_examples, num_inputs], mean=0, stddev=0.1)\n",
    "    W = tf.Variable(tf.constant([5, 1]))\n",
    "    B = tf.Variable(tf.constant([1]))\n",
    "w\n",
    "w.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "        [ 8.04902573e+01],\n",
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       "        [-1.02829838e+00],\n",
       "        [-8.87892723e-01],\n",
       "        [-3.41211662e+01],\n",
       "        [ 9.60390377e+00],\n",
       "        [-1.15959549e+01],\n",
       "        [ 9.57117558e+00],\n",
       "        [ 8.08290005e+00],\n",
       "        [ 2.34752007e+01],\n",
       "        [-2.02109566e+01],\n",
       "        [-1.41588030e+01],\n",
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       "        [ 9.52748394e+00],\n",
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       "        [-1.15805721e+00],\n",
       "        [ 1.34246721e+01],\n",
       "        [ 2.19253693e+01],\n",
       "        [ 9.44752121e+01],\n",
       "        [-2.85508499e+01],\n",
       "        [ 6.54397812e+01],\n",
       "        [ 3.54828377e+01],\n",
       "        [-2.16995583e+01],\n",
       "        [ 3.56944513e+00],\n",
       "        [ 5.05904350e+01],\n",
       "        [ 7.69861832e+01],\n",
       "        [ 8.92370129e+00],\n",
       "        [-8.14800110e+01],\n",
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       "        [-6.66644592e+01],\n",
       "        [-6.61096344e+01],\n",
       "        [-3.00949135e+01],\n",
       "        [ 1.63198586e+01],\n",
       "        [-6.31031895e+00],\n",
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       "        [ 1.31586771e+01],\n",
       "        [ 1.75056305e+01],\n",
       "        [-2.29651909e+01],\n",
       "        [ 4.83283577e+01],\n",
       "        [ 4.86760368e+01],\n",
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       "        [ 2.40872955e+01],\n",
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       "        [-3.94524422e+01],\n",
       "        [-4.96511002e+01],\n",
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       "        [-4.20378494e+01],\n",
       "        [-2.48542442e+01],\n",
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       "        [-2.50318031e+01],\n",
       "        [ 3.95536041e+00],\n",
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       "        [ 1.93379498e+00],\n",
       "        [ 4.03640480e+01],\n",
       "        [ 2.92563095e+01],\n",
       "        [ 2.39315147e+01],\n",
       "        [-2.85839272e+01],\n",
       "        [-5.35657272e+01],\n",
       "        [-1.56765451e+01],\n",
       "        [ 4.01958351e+01],\n",
       "        [ 1.40686598e+01],\n",
       "        [ 1.18769455e+00],\n",
       "        [-2.64712067e+01],\n",
       "        [ 6.70441151e+00],\n",
       "        [-4.79270554e+01],\n",
       "        [ 5.97875481e+01],\n",
       "        [ 2.08273849e+01],\n",
       "        [ 1.95401134e+01],\n",
       "        [-4.54463120e+01],\n",
       "        [ 1.72074966e+01],\n",
       "        [-5.33941364e+00],\n",
       "        [-4.09461136e+01],\n",
       "        [ 2.96171417e+01],\n",
       "        [-1.27602091e+01],\n",
       "        [-5.44326057e+01],\n",
       "        [-2.61975021e+01],\n",
       "        [-4.32773209e+01],\n",
       "        [-3.50403214e+01],\n",
       "        [ 4.56181412e+01],\n",
       "        [ 2.60917244e+01],\n",
       "        [-3.42954597e+01],\n",
       "        [-1.23626242e+01],\n",
       "        [ 4.19672050e+01],\n",
       "        [ 5.68947563e+01],\n",
       "        [-4.50797234e+01],\n",
       "        [-3.26290321e+00],\n",
       "        [-4.28235016e+01],\n",
       "        [ 7.00571632e+00],\n",
       "        [ 5.72163582e-01],\n",
       "        [-7.80153046e+01],\n",
       "        [ 2.72267647e+01],\n",
       "        [ 5.34331398e+01],\n",
       "        [ 8.78563118e+00],\n",
       "        [ 2.20167236e+01],\n",
       "        [-5.41542912e+00],\n",
       "        [-1.08470106e+01],\n",
       "        [ 1.80112762e+01],\n",
       "        [ 5.40471840e+01],\n",
       "        [ 3.38950691e+01],\n",
       "        [ 9.29517365e+01],\n",
       "        [ 2.34043427e+01],\n",
       "        [-1.20946722e+01],\n",
       "        [ 2.21463356e+01],\n",
       "        [ 6.15349722e+00],\n",
       "        [ 2.32220078e+01]], dtype=float32)>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "# 线性回归模型， y = \n",
    "# 生成数据,生成1000组数据\n",
    "\n",
    "\n",
    "true_y = tf.matmul(x, tf.transpose(w)) + b\n",
    "x, true_y\n",
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff2d80d0710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "\n",
    "set_figsize()\n",
    "plt.scatter(x[: ,1], true_y, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、创建线性模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mymodel():\n",
    "    def __init__(self):\n",
    "        self.w = tf.Variable([[2.3, 1.2]])\n",
    "        self.b = tf.Variable([8.0])\n",
    "    \n",
    "    def __call__(self, x):\n",
    "        self.y = tf.matmul(x , tf.transpose(self.w)) + self.b\n",
    "        return self.y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、数据处理及创建 batch_size 迭代器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(x, y):\n",
    "    return x, y\n",
    "    \n",
    "db = tf.data.Dataset.from_tensor_slices((x, true_y))\n",
    "db = db.shuffle(100)\n",
    "db2 = db.map(process)\n",
    "db_batch = db.batch(32)\n",
    "# db_iter = next(iter(db_batch))\n",
    "# print(db_iter[1].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、定义损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(predicted_y, desired_y):\n",
    "    return tf.reduce_mean(tf.square(predicted_y - tf.reshape(desired_y, predicted_y.shape)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6、定义一次损失计算及梯度更新训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, inputs, outputs, learning_rate):    # 传入模型，输入，输出，及学习率\n",
    "    with tf.GradientTape() as t:\n",
    "        current_loss = loss(model(inputs), outputs)\n",
    "        print('loss',current_loss)\n",
    "    dW, db = t.gradient(current_loss, [model.w, model.b])   # 梯度计算\n",
    "    # tf.assign_sub(ref, value, use_locking=None, name=None)，变量 ref 减去 value值，即 ref = ref - value\n",
    "    model.w.assign_sub(learning_rate * dW)\n",
    "    model.b.assign_sub(learning_rate * db)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7、迭代训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "loss tf.Tensor(1376.4008, shape=(), dtype=float32)\n",
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      "loss tf.Tensor(12.478985, shape=(), dtype=float32)\n",
      "w [[ 2.007623  -3.3770058]]\n",
      "b [7.7426805]\n",
      "loss tf.Tensor(12.263131, shape=(), dtype=float32)\n",
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      "loss tf.Tensor(11.193733, shape=(), dtype=float32)\n",
      "w [[ 1.9970254 -3.3513603]]\n",
      "b [7.522669]\n",
      "loss tf.Tensor(11.740538, shape=(), dtype=float32)\n",
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      "w [[ 2.0037441 -3.3875098]]\n",
      "b [7.316081]\n",
      "loss tf.Tensor(9.826916, shape=(), dtype=float32)\n",
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      "loss tf.Tensor(9.315684, shape=(), dtype=float32)\n",
      "w [[ 1.9906999 -3.412275 ]]\n",
      "b [7.122599]\n",
      "loss tf.Tensor(8.8379755, shape=(), dtype=float32)\n",
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      "loss tf.Tensor(7.5741367, shape=(), dtype=float32)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss tf.Tensor(8.112118, shape=(), dtype=float32)\n",
      "w [[ 1.9723355 -3.4121807]]\n",
      "b [6.9407787]\n",
      "loss tf.Tensor(7.725419, shape=(), dtype=float32)\n",
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      "w [[ 2.0144372 -3.3942707]]\n",
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      "w [[ 1.9987007 -3.3731375]]\n",
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      "w [[ 1.999751  -3.3949742]]\n",
      "b [6.4610777]\n",
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      "w [[ 2.0247467 -3.3904061]]\n",
      "b [6.3209424]\n",
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      "loss tf.Tensor(1.3333423, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.291249, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2569911, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2404885, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2785597, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2538574, shape=(), dtype=float32)\n",
      "w [[ 2.009376  -3.3976715]]\n",
      "b [5.317856]\n",
      "loss tf.Tensor(1.2007595, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.198533, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2634257, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2174051, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2653873, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2313917, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2369998, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2080035, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2118706, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1952832, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1865659, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2297317, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1967511, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1504173, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1858113, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2447511, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.208166, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.2202102, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1645017, shape=(), dtype=float32)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss tf.Tensor(1.1453468, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1592574, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1616294, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.185883, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1412241, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.148283, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1654972, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1469151, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1491536, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1291233, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1336288, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.1053963, shape=(), dtype=float32)\n",
      "loss tf.Tensor(1.0494366, shape=(), dtype=float32)\n",
      "w [[ 2.0119646 -3.392818 ]]\n",
      "b [5.248531]\n"
     ]
    }
   ],
   "source": [
    "model = Mymodel()\n",
    "\n",
    "Ws, bs = [], []\n",
    "epoches = 20\n",
    "\n",
    "steps = int(1000/32)\n",
    "for i in range(epoches):\n",
    "    for d in iter(db_batch):\n",
    "#         d = next(iter(db_batch))\n",
    "        it = d[0]\n",
    "#         print(it)\n",
    "        ot = d[1]\n",
    "#         print(ot)\n",
    "#         current_loss = loss(model(it), ot)\n",
    "#         print(current_loss)\n",
    "\n",
    "        train(model, it, ot, learning_rate=0.001)\n",
    "\n",
    "    Ws.append(model.w.numpy())\n",
    "    bs.append(model.b.numpy())\n",
    "    print('w',(Ws[-1]))\n",
    "    print('b',(bs[-1]))\n",
    "#         print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %\n",
    "#               (epoch, Ws[-1], bs[-1], current_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x4320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#     w = tf.constant([[2, -3.4]])\n",
    "#     b = tf.constant([4.2])\n",
    "# print(bs)\n",
    "# plt.plot(epochs, Ws, 'r',\n",
    "#          epochs, bs, 'b')\n",
    "# plt.plot([TRUE_W] * len(epochs), 'r--',\n",
    "#          [TRUE_b] * len(epochs), 'b--')\n",
    "# plt.legend(['W', 'b', 'true W', 'true_b'])\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(100, 60))\n",
    "plt.plot(range(epoches), [i[0] for i in Ws], 'r',\n",
    "         range(epoches), [i[0] for i in bs], 'b', linewidth=2)\n",
    "plt.plot([2] * epoches, 'r--',\n",
    "         [4.2] * epoches, 'b--', linewidth=2)\n",
    "\n",
    "plt.xlabel('epoches(time)')\n",
    "plt.ylabel(\"w/b\")\n",
    "plt.grid(True)\n",
    "plt.title(\"Python chart\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.legend(['W', 'b', 'true W', 'true_b'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (tf2-1)",
   "language": "python",
   "name": "tf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}