{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入 TensorFlow\n",
    "import tensorflow as tf\n",
    "# 即显模式\n",
    "tf.enable_eager_execution()\n",
    "import plotly.plotly as py\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "plotly.offline.init_notebook_mode(connected=True)\n",
    "import numpy as np\n",
    "tf.set_random_seed(123)\n",
    "\n",
    "# 生成数据\n",
    "n_example = 100\n",
    "X = tf.random_normal([n_example])\n",
    "# 称重误差\n",
    "noise = tf.random_uniform([n_example], -0.5, 0.5)\n",
    "Y = X * 2 + noise\n",
    "\n",
    "# 训练集\n",
    "train_x = X[:80]\n",
    "train_y = Y[:80]\n",
    "\n",
    "# 测试集\n",
    "test_x = X[80:]\n",
    "test_y = Y[80:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22c7efd3f98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示训练集\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_x, train_y - noise[:80], color='green')\n",
    "plt.scatter(train_x, train_y)\n",
    "plt.legend(['Rule','Data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.contrib.eager as tfe\n",
    "# 定义模型类\n",
    "class Model(object):\n",
    "    def __init__(self):\n",
    "        # 参数\n",
    "        # 初始化：随便给 参数 W 和 b 两个值\n",
    "        self.W = tfe.Variable(0.1)\n",
    "        self.b = tfe.Variable(0.1)\n",
    "    \n",
    "    def __call__(self, x):\n",
    "        # 正向传递\n",
    "        # 如何利用 参数 W 和 b 运算\n",
    "        y = self.W * x + self.b\n",
    "        return y\n",
    "# 实例模型\n",
    "model = Model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练前"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test Loss: 2.763198\n",
      "[0.2 0.3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 误差\n",
    "def loss(prediction, label):\n",
    "    loss = tf.reduce_mean(tf.square(prediction - label))\n",
    "    return loss\n",
    "\n",
    "# 评估\n",
    "test_p = model(test_x)\n",
    "print(\"Final Test Loss: %s\" %loss(test_p, test_y).numpy())\n",
    "\n",
    "# 可视化\n",
    "plt.plot(test_x,test_y-noise[80:],color='red')\n",
    "plt.scatter(test_x,test_y)\n",
    "plt.scatter(test_x,test_p)\n",
    "plt.legend(['rule','real','predicted'])\n",
    "\n",
    "# 预测\n",
    "test_p = model([1,2])\n",
    "print(test_p.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 不同样本时，w 的梯度 （不训练）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "mode": "lines",
         "name": "e(w)",
         "type": "scatter",
         "uid": "03e64ae4-c19c-11e8-ad1f-00e04c6665ed",
         "visible": true,
         "x": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def change_example(i,v):\n",
    "    def ew(w):\n",
    "        return (w * train_x[i].numpy() + model.b.numpy() - train_y[i].numpy())**2\n",
    "    grad_ew = tfe.gradients_function(ew)\n",
    "    w = np.linspace(-5.0, 5.0, 100)\n",
    "    w0 = model.W.numpy()\n",
    "\n",
    "    trace1 = go.Scatter(x = w, y = ew(w),\n",
    "        visible = v,mode = 'lines', name = 'e(w)')\n",
    "\n",
    "    trace2 = go.Scatter(x = [w0,w0], y = [ew(w0),0],\n",
    "        visible = v,mode = 'lines+markers', name = 'points')\n",
    "\n",
    "    trace3 = go.Scatter(x = w, y = (grad_ew(w0)[0]*(w-w0)+ew(w0)).numpy(),\n",
    "        visible = v,mode = 'lines', name = \"slope at w = %.3f\" %w0)\n",
    "\n",
    "    trace4 = go.Scatter(x = [w0,w0-grad_ew(w0)[0].numpy()], y = [0,0],\n",
    "        visible = v,mode = 'lines', name = \"negative gradient\")\n",
    "    return [trace1,trace2,trace3,trace4]\n",
    "\n",
    "# 生成 10 组\n",
    "data = []\n",
    "s=10\n",
    "for i in range(s):\n",
    "    if i !=0:\n",
    "        v = False\n",
    "    else:\n",
    "        v = True\n",
    "    data+=change_example(i,v)\n",
    "    \n",
    "# 可视化\n",
    "steps = []\n",
    "for i in range(0,len(data),4):\n",
    "    step = dict(\n",
    "        method = 'restyle',  \n",
    "        args = ['visible', [False] * len(data)],\n",
    "    )\n",
    "    step['args'][1][i] = True # Toggle i'th trace to \"visible\"\n",
    "    step['args'][1][i+1] = True\n",
    "    step['args'][1][i+2] = True\n",
    "    step['args'][1][i+3] = True\n",
    "    \n",
    "    steps.append(step)\n",
    "\n",
    "sliders = [dict(\n",
    "    active = 10,\n",
    "    currentvalue = {\"prefix\": \"train: \"},\n",
    "    pad = {\"t\": 10},\n",
    "    steps = steps\n",
    ")]\n",
    "\n",
    "layout = dict(sliders=sliders,\n",
    "    xaxis={\n",
    "        'range': [-5,5]\n",
    "    },\n",
    "    yaxis={\n",
    "        'range': [-2, 10]\n",
    "    },)\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "plotly.offline.iplot(fig)\n",
    "#py.iplot(fig, filename='gradient_of_w_using_different_examples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 同一个样本，训练十次后，w 的梯度变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\"method\": \"restyle\"}]}], \"xaxis\": {\"range\": [-5, 5]}, \"yaxis\": {\"range\": [-2, 10]}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train_one_example(i,v):\n",
    "    data=change_example(i,v)\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss_value = loss(model(train_x[i]), train_y[i])\n",
    "        dW, db = tape.gradient(loss_value, [model.W, model.b])\n",
    "    model.W.assign_sub(dW * 0.1)\n",
    "    model.b.assign_sub(db * 0.1)\n",
    "    return data\n",
    "# 生成 10 组\n",
    "data = []\n",
    "s=10\n",
    "for i in range(s):\n",
    "    if i !=0:\n",
    "        v = False\n",
    "    else:\n",
    "        v = True\n",
    "    data+=train_one_example(0,v)\n",
    "# 可视化\n",
    "steps = []\n",
    "for i in range(0,len(data),4):\n",
    "    step = dict(\n",
    "        method = 'restyle',  \n",
    "        args = ['visible', [False] * len(data)],\n",
    "    )\n",
    "    step['args'][1][i] = True # Toggle i'th trace to \"visible\"\n",
    "    step['args'][1][i+1] = True\n",
    "    step['args'][1][i+2] = True\n",
    "    step['args'][1][i+3] = True\n",
    "    \n",
    "    steps.append(step)\n",
    "\n",
    "sliders = [dict(\n",
    "    active = 10,\n",
    "    currentvalue = {\"prefix\": \"train: \"},\n",
    "    pad = {\"t\": 10},\n",
    "    steps = steps\n",
    ")]\n",
    "\n",
    "layout = dict(sliders=sliders,\n",
    "    xaxis={\n",
    "        'range': [-5,5]\n",
    "    },\n",
    "    yaxis={\n",
    "        'range': [-2, 10]\n",
    "    },)\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "plotly.offline.iplot(fig)\n",
    "#py.iplot(fig, filename='train_one_example_10times')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 不同样本时，w 的梯度 （训练）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true]], \"method\": \"restyle\"}]}], \"xaxis\": {\"range\": [-5, 5]}, \"yaxis\": {\"range\": [-2, 10]}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练 50 次\n",
    "data = []\n",
    "s=50\n",
    "for i in range(s):\n",
    "    if i !=0:\n",
    "        v = False\n",
    "    else:\n",
    "        v = True\n",
    "    data+=train_one_example(i,v)\n",
    "steps = []\n",
    "for i in range(0,len(data),4):\n",
    "    step = dict(\n",
    "        method = 'restyle',  \n",
    "        args = ['visible', [False] * len(data)],\n",
    "    )\n",
    "    step['args'][1][i] = True # Toggle i'th trace to \"visible\"\n",
    "    step['args'][1][i+1] = True\n",
    "    step['args'][1][i+2] = True\n",
    "    step['args'][1][i+3] = True\n",
    "    \n",
    "    steps.append(step)\n",
    "\n",
    "sliders = [dict(\n",
    "    active = 10,\n",
    "    currentvalue = {\"prefix\": \"train: \"},\n",
    "    pad = {\"t\": 10},\n",
    "    steps = steps\n",
    ")]\n",
    "\n",
    "layout = dict(sliders=sliders,\n",
    "    xaxis={\n",
    "        'range': [-5,5]\n",
    "    },\n",
    "    yaxis={\n",
    "        'range': [-2, 10]\n",
    "    },)\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "plotly.offline.iplot(fig)\n",
    "#py.iplot(fig, filename='train_one_example_10times')"
   ]
  }
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