boston housing price-v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af262250",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#https://mp.weixin.qq.com/s/IoYSJA-ePTUKESgHtM0__Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "daa97a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入波士顿房价数据集\n",
    "rawdata = pd.read_table('housing.csv', sep = '\\s+', header=None)\n",
    "dataset = np.array(rawdata.values)\n",
    "#data = pd.read_csv(path, header=None, names=['Population', 'Profit'])\n",
    "#(train_x, train_y), (test_x, test_y) = housing.load_data()\n",
    "train_x = dataset[:, :-1].astype(float)\n",
    "train_y = dataset[:, -1]\n",
    "test_x = dataset[:, :-1]\n",
    "test_y = dataset[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d96288fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rawdata.head()\n",
    "#rawdata.describe()\n",
    "dataset.shape\n",
    "#print (rawdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0922166e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print (train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2810d368",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense\n",
    "from tensorflow import random\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#随机种子\n",
    "np.random.seed(0)\n",
    "random.set_seed(0)\n",
    "\n",
    "#单层线性层的网络结构（也就是线性回归）：无隐藏层，由于是数值回归预测，输出层没有用激活函数\n",
    "#model = Sequential()\n",
    "#model.add(Dense(1,use_bias=False))  \n",
    "\n",
    "\n",
    "#网络结构：输入层的特征维数为13，1层relu隐藏层，线性的输出层；\n",
    "model = Sequential()\n",
    "model.add(Dense(10, input_dim=13, activation='relu',use_bias=False))\n",
    "model.add(Dense(1,use_bias=False))  \n",
    "\n",
    "#回归预测损失mse\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f9d3142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 10)                130       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 10        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 140\n",
      "Trainable params: 140\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#训练模型\n",
    "model.fit (train_x, train_y, epochs=1000, verbose=False)\n",
    "model.summary()\n",
    "pred_y = model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4dd35cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确标签： [24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
      " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
      " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
      " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
      " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
      " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
      " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
      " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
      " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
      " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
      " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
      " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
      " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
      " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
      " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
      " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
      " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
      " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
      " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
      " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
      " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
      " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
      " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
      " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
      " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
      " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
      " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
      " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
      "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
      " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
      " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
      " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
      " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
      " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
      " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
      "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
      " 22.  11.9]\n",
      "模型预测： [[30.839005 ]\n",
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      " [21.136948 ]\n",
      " [29.571648 ]\n",
      " [21.339333 ]\n",
      " [23.80477  ]\n",
      " [25.555283 ]\n",
      " [28.944262 ]\n",
      " [28.277905 ]\n",
      " [24.399288 ]\n",
      " [27.957537 ]\n",
      " [23.866642 ]\n",
      " [22.258713 ]\n",
      " [25.234022 ]\n",
      " [44.48109  ]\n",
      " [14.797419 ]\n",
      " [17.457571 ]\n",
      " [ 8.388713 ]\n",
      " [24.026495 ]\n",
      " [35.484848 ]\n",
      " [37.866394 ]\n",
      " [26.578547 ]\n",
      " [28.538225 ]\n",
      " [ 7.8731766]\n",
      " [ 1.8974457]\n",
      " [29.572819 ]\n",
      " [21.12569  ]\n",
      " [23.89272  ]\n",
      " [19.411205 ]\n",
      " [20.053005 ]\n",
      " [19.916233 ]\n",
      " [22.080559 ]\n",
      " [16.460646 ]\n",
      " [16.045181 ]\n",
      " [ 4.463505 ]\n",
      " [11.040431 ]\n",
      " [ 8.570137 ]\n",
      " [ 8.496537 ]\n",
      " [ 8.836752 ]\n",
      " [17.082531 ]\n",
      " [20.421667 ]\n",
      " [20.86687  ]\n",
      " [12.573041 ]\n",
      " [23.757725 ]\n",
      " [21.268604 ]\n",
      " [24.108898 ]\n",
      " [23.023224 ]\n",
      " [19.347912 ]\n",
      " [10.0839   ]\n",
      " [13.753018 ]\n",
      " [15.67675  ]\n",
      " [21.621922 ]\n",
      " [21.902962 ]\n",
      " [16.000427 ]\n",
      " [10.525915 ]\n",
      " [12.368941 ]\n",
      " [ 8.5761595]\n",
      " [21.427881 ]\n",
      " [14.025244 ]\n",
      " [20.463776 ]\n",
      " [13.831787 ]\n",
      " [16.261415 ]\n",
      " [-0.8575115]\n",
      " [11.211728 ]\n",
      " [-3.847951 ]\n",
      " [10.650208 ]\n",
      " [14.534001 ]\n",
      " [ 7.563715 ]\n",
      " [ 7.653349 ]\n",
      " [16.776833 ]\n",
      " [23.345224 ]\n",
      " [21.37154  ]\n",
      " [19.15575  ]\n",
      " [12.103483 ]\n",
      " [11.81857  ]\n",
      " [10.149806 ]\n",
      " [14.50516  ]\n",
      " [14.986663 ]\n",
      " [15.285748 ]\n",
      " [14.0967245]\n",
      " [17.325916 ]\n",
      " [18.464043 ]\n",
      " [20.814941 ]\n",
      " [19.097939 ]\n",
      " [17.898636 ]\n",
      " [16.425121 ]\n",
      " [16.653025 ]\n",
      " [10.60607  ]\n",
      " [ 6.958995 ]\n",
      " [17.125153 ]\n",
      " [17.218641 ]\n",
      " [22.183126 ]\n",
      " [23.325214 ]\n",
      " [22.985905 ]\n",
      " [14.707156 ]\n",
      " [14.2530365]\n",
      " [21.853004 ]\n",
      " [22.880264 ]\n",
      " [21.769741 ]\n",
      " [20.935036 ]\n",
      " [18.185472 ]\n",
      " [23.797394 ]\n",
      " [22.805809 ]\n",
      " [27.93169  ]\n",
      " [17.146397 ]\n",
      " [17.672125 ]\n",
      " [13.7108755]\n",
      " [13.94137  ]\n",
      " [20.768108 ]\n",
      " [22.764576 ]\n",
      " [22.771368 ]\n",
      " [24.553253 ]\n",
      " [24.27389  ]\n",
      " [27.061996 ]\n",
      " [23.650238 ]\n",
      " [20.198582 ]\n",
      " [14.378108 ]\n",
      " [17.950035 ]\n",
      " [18.494125 ]\n",
      " [19.927166 ]\n",
      " [21.944824 ]\n",
      " [23.79319  ]\n",
      " [24.37241  ]\n",
      " [28.410034 ]\n",
      " [16.899834 ]\n",
      " [16.983093 ]\n",
      " [23.024097 ]\n",
      " [12.618494 ]\n",
      " [21.274174 ]\n",
      " [23.88971  ]\n",
      " [24.477238 ]\n",
      " [28.51928  ]\n",
      " [30.372536 ]\n",
      " [21.81625  ]\n",
      " [21.020369 ]\n",
      " [24.52227  ]\n",
      " [21.6731   ]\n",
      " [22.78846  ]\n",
      " [15.117399 ]\n",
      " [11.116817 ]\n",
      " [ 6.150714 ]\n",
      " [17.578537 ]\n",
      " [19.83819  ]\n",
      " [22.766972 ]\n",
      " [23.023178 ]\n",
      " [19.280983 ]\n",
      " [16.205856 ]\n",
      " [21.65095  ]\n",
      " [23.810335 ]\n",
      " [20.50656  ]\n",
      " [23.063175 ]\n",
      " [27.94305  ]\n",
      " [26.200846 ]\n",
      " [32.252697 ]\n",
      " [30.660376 ]\n",
      " [26.161205 ]]\n",
      "实际与预测值的差异： 28.227889299879543\n"
     ]
    }
   ],
   "source": [
    "print(\"正确标签：\",test_y)\n",
    "print(\"模型预测：\",pred_y )\n",
    "print(\"实际与预测值的差异：\",mean_squared_error(test_y,pred_y ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b8471a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘图表示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "#设置图形大小\n",
    "plt.figure(figsize=(8, 4), dpi=80)\n",
    "plt.plot(range(len(test_y)), test_y, ls='-.',lw=2,c='r',label='真实值')\n",
    "plt.plot(range(len(pred_y)), pred_y, ls='-',lw=2,c='b',label='预测值')\n",
    "\n",
    "#绘制网格\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend()\n",
    "plt.xlabel('number')\n",
    "plt.ylabel('房价')\n",
    "\n",
    "#展示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37ed2933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-1.57584772e-01, -3.32815260e-01,  3.61518860e-02,\n",
       "          4.69033457e-02, -1.19137220e-01,  2.33699992e-01,\n",
       "          2.63848919e-02,  5.08842885e-01, -3.36589059e-03,\n",
       "         -3.82683814e-01],\n",
       "        [-8.47188607e-02,  2.72332523e-02,  7.38106370e-02,\n",
       "         -1.38155371e-01,  3.41768742e-01,  7.95739330e-03,\n",
       "          9.87585261e-02,  1.65123880e-01,  3.42757434e-01,\n",
       "         -4.24990147e-01],\n",
       "        [ 5.73996007e-02, -5.86508334e-01,  2.31242359e-01,\n",
       "          2.57235169e-01,  2.66490161e-01,  1.95373461e-01,\n",
       "          2.09063411e-01, -4.62312043e-01, -4.34202462e-01,\n",
       "          2.57157922e-01],\n",
       "        [-2.93998027e+00, -4.64683652e-01, -2.13113248e-01,\n",
       "         -3.98868412e-01,  1.61534548e+00, -2.18854547e+00,\n",
       "         -1.43100250e+00,  4.92376626e-01, -6.10267043e-01,\n",
       "         -3.66041660e-01],\n",
       "        [-1.75480008e+00, -2.79228017e-02, -3.29005420e-01,\n",
       "          5.37084602e-03,  1.47359586e+00, -1.54906988e+00,\n",
       "         -1.21433198e+00,  9.96393561e-02, -5.63926995e-01,\n",
       "          3.11635852e-01],\n",
       "        [-3.22572398e+00, -5.52612484e-01,  4.54473376e-01,\n",
       "         -2.35973164e-01,  2.29998136e+00, -3.25268698e+00,\n",
       "         -2.27060962e+00, -1.12882674e-01, -2.22420883e+00,\n",
       "         -4.67917651e-01],\n",
       "        [-4.86604907e-02,  2.26383388e-01, -3.27287316e-02,\n",
       "         -4.37628701e-02,  3.69521350e-01, -2.16102670e-03,\n",
       "          3.04759502e-01, -1.45263642e-01, -2.11758956e-01,\n",
       "          1.05431020e-01],\n",
       "        [ 1.10906911e+00, -3.08057871e-02,  3.77214491e-01,\n",
       "          3.11786950e-01, -7.25847125e-01,  7.46802390e-01,\n",
       "         -1.17425032e-01, -4.69254106e-01,  9.03367281e-01,\n",
       "         -3.36906940e-01],\n",
       "        [ 2.03226343e-01,  1.69352263e-01,  3.50567818e-01,\n",
       "          2.24814445e-01,  4.03631717e-01, -4.47625011e-01,\n",
       "          1.40113264e-01, -3.18175316e-01, -9.62988064e-02,\n",
       "         -4.71255064e-01],\n",
       "        [ 4.43828732e-01, -5.23660239e-03, -5.09843409e-01,\n",
       "         -4.15309258e-02,  6.13218606e-01,  3.77726704e-01,\n",
       "         -1.81471445e-02, -1.62141770e-01,  1.65425301e-01,\n",
       "         -4.50164437e-01],\n",
       "        [ 4.26461011e-01, -1.41432256e-01, -4.41796929e-01,\n",
       "          4.66728091e-01, -6.38122037e-02,  2.73290634e-01,\n",
       "         -2.98244208e-01,  2.35037446e-01,  5.63331664e-01,\n",
       "         -6.90571070e-02],\n",
       "        [-1.61946937e-01, -5.17872870e-02, -3.44893187e-01,\n",
       "         -9.24985707e-02,  2.62854487e-01, -1.46188915e-01,\n",
       "          2.93348610e-01, -1.90383673e-01, -2.10031844e-03,\n",
       "          9.05737281e-02],\n",
       "        [ 3.98277104e-01, -2.72720128e-01, -2.82776296e-01,\n",
       "          1.57037377e-01, -8.03791359e-02,  9.56747711e-01,\n",
       "          5.82956225e-02,  1.40436828e-01,  4.55669798e-02,\n",
       "          2.07556963e-01]], dtype=float32)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[0].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d16a145d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1、输入第一条样本x0:\n",
      " [6.320e-03 1.800e+01 2.310e+00 0.000e+00 5.380e-01 6.575e+00 6.520e+01\n",
      " 4.090e+00 1.000e+00 2.960e+02 1.530e+01 3.969e+02 4.980e+00]\n",
      "2、第一层网络的权重参数w0:\n",
      " [[-1.57584772e-01 -3.32815260e-01  3.61518860e-02  4.69033457e-02\n",
      "  -1.19137220e-01  2.33699992e-01  2.63848919e-02  5.08842885e-01\n",
      "  -3.36589059e-03 -3.82683814e-01]\n",
      " [-8.47188607e-02  2.72332523e-02  7.38106370e-02 -1.38155371e-01\n",
      "   3.41768742e-01  7.95739330e-03  9.87585261e-02  1.65123880e-01\n",
      "   3.42757434e-01 -4.24990147e-01]\n",
      " [ 5.73996007e-02 -5.86508334e-01  2.31242359e-01  2.57235169e-01\n",
      "   2.66490161e-01  1.95373461e-01  2.09063411e-01 -4.62312043e-01\n",
      "  -4.34202462e-01  2.57157922e-01]\n",
      " [-2.93998027e+00 -4.64683652e-01 -2.13113248e-01 -3.98868412e-01\n",
      "   1.61534548e+00 -2.18854547e+00 -1.43100250e+00  4.92376626e-01\n",
      "  -6.10267043e-01 -3.66041660e-01]\n",
      " [-1.75480008e+00 -2.79228017e-02 -3.29005420e-01  5.37084602e-03\n",
      "   1.47359586e+00 -1.54906988e+00 -1.21433198e+00  9.96393561e-02\n",
      "  -5.63926995e-01  3.11635852e-01]\n",
      " [-3.22572398e+00 -5.52612484e-01  4.54473376e-01 -2.35973164e-01\n",
      "   2.29998136e+00 -3.25268698e+00 -2.27060962e+00 -1.12882674e-01\n",
      "  -2.22420883e+00 -4.67917651e-01]\n",
      " [-4.86604907e-02  2.26383388e-01 -3.27287316e-02 -4.37628701e-02\n",
      "   3.69521350e-01 -2.16102670e-03  3.04759502e-01 -1.45263642e-01\n",
      "  -2.11758956e-01  1.05431020e-01]\n",
      " [ 1.10906911e+00 -3.08057871e-02  3.77214491e-01  3.11786950e-01\n",
      "  -7.25847125e-01  7.46802390e-01 -1.17425032e-01 -4.69254106e-01\n",
      "   9.03367281e-01 -3.36906940e-01]\n",
      " [ 2.03226343e-01  1.69352263e-01  3.50567818e-01  2.24814445e-01\n",
      "   4.03631717e-01 -4.47625011e-01  1.40113264e-01 -3.18175316e-01\n",
      "  -9.62988064e-02 -4.71255064e-01]\n",
      " [ 4.43828732e-01 -5.23660239e-03 -5.09843409e-01 -4.15309258e-02\n",
      "   6.13218606e-01  3.77726704e-01 -1.81471445e-02 -1.62141770e-01\n",
      "   1.65425301e-01 -4.50164437e-01]\n",
      " [ 4.26461011e-01 -1.41432256e-01 -4.41796929e-01  4.66728091e-01\n",
      "  -6.38122037e-02  2.73290634e-01 -2.98244208e-01  2.35037446e-01\n",
      "   5.63331664e-01 -6.90571070e-02]\n",
      " [-1.61946937e-01 -5.17872870e-02 -3.44893187e-01 -9.24985707e-02\n",
      "   2.62854487e-01 -1.46188915e-01  2.93348610e-01 -1.90383673e-01\n",
      "  -2.10031844e-03  9.05737281e-02]\n",
      " [ 3.98277104e-01 -2.72720128e-01 -2.82776296e-01  1.57037377e-01\n",
      "  -8.03791359e-02  9.56747711e-01  5.82956225e-02  1.40436828e-01\n",
      "   4.55669798e-02  2.07556963e-01]]\n"
     ]
    }
   ],
   "source": [
    "x0=train_x[0]\n",
    "print(\"1、输入第一条样本x0:\\n\", x0)\n",
    " # 权重参数可以控制数据的特征表达再输出到下一层\n",
    "w0= model.layers[0].get_weights()[0] \n",
    "print(\"2、第一层网络的权重参数w0:\\n\", w0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6bc45b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3、经过第一层神经网络relu(w0*x0)后输出:\n",
      " [ 53.62493362   0.           0.           0.         328.67259583\n",
      "  43.57277848 112.99403682   0.          37.00899908   0.        ]\n",
      "4、第二层网络的权重参数w1:\n",
      " [[-0.34143656]\n",
      " [-0.3359894 ]\n",
      " [ 0.5390298 ]\n",
      " [ 0.388911  ]\n",
      " [ 0.43474424]\n",
      " [-0.30253264]\n",
      " [-0.6708156 ]\n",
      " [ 0.5517096 ]\n",
      " [-0.12860763]\n",
      " [-0.1673125 ]]\n",
      "5、经过第二层神经网络w1*ao后输出预测值:30.839015704375655,实际标签值为24.0\n"
     ]
    }
   ],
   "source": [
    "a0 = np.maximum(0,np.dot(w0.T, x0)) \n",
    "#a0可以视为第一层网络层交互出的新特征，但其特征含义是比较模糊的\n",
    "print(\"3、经过第一层神经网络relu(w0*x0)后输出:\\n\",a0) \n",
    "w1=model.layers[1].get_weights()[0] \n",
    "print(\"4、第二层网络的权重参数w1:\\n\", w1)  \n",
    "#预测结果为w1与ao加权求和\n",
    "a1 = np.dot(w1.T,a0)                                  \n",
    "print(\"5、经过第二层神经网络w1*ao后输出预测值:%s,实际标签值为%s\"%(a1[0],train_y[0]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fda30c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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