nn test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "d = load_boston()\n",
    "\n",
    "std = StandardScaler()\n",
    "data = pd.DataFrame(std.fit_transform(d.data), columns=d.feature_names)\n",
    "target = std.fit_transform(d.target.reshape(-1,1))\n",
    "dd = data.iloc[:,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models, layers\n",
    "\n",
    "input_layer = layers.Input(shape=(data.shape[1],))\n",
    "\n",
    "layer1 = layers.Dense(32, activation='relu')(input_layer)\n",
    "layer2 = layers.Dense(16, activation='relu')(layer1)\n",
    "\n",
    "out_layer = layers.Dense(1, activation='linear')(layer2) ## 분류는 마지막 층에 activation이 sigmoid였지만 회귀에서는 설정하지 않는다(linear)\n",
    "\n",
    "model = models.Model(input_layer, out_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='mse',\n",
    "             metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "506/506 [==============================] - 0s 627us/step - loss: 0.9531 - mse: 0.9531\n",
      "Epoch 2/50\n",
      "506/506 [==============================] - 0s 149us/step - loss: 0.6236 - mse: 0.6236\n",
      "Epoch 3/50\n",
      "506/506 [==============================] - 0s 222us/step - loss: 0.4453 - mse: 0.4453\n",
      "Epoch 4/50\n",
      "506/506 [==============================] - 0s 247us/step - loss: 0.3274 - mse: 0.3274\n",
      "Epoch 5/50\n",
      "506/506 [==============================] - 0s 254us/step - loss: 0.2594 - mse: 0.2594\n",
      "Epoch 6/50\n",
      "506/506 [==============================] - 0s 244us/step - loss: 0.2230 - mse: 0.2230\n",
      "Epoch 7/50\n",
      "506/506 [==============================] - 0s 258us/step - loss: 0.2008 - mse: 0.2008\n",
      "Epoch 8/50\n",
      "506/506 [==============================] - 0s 246us/step - loss: 0.1864 - mse: 0.1864\n",
      "Epoch 9/50\n",
      "506/506 [==============================] - 0s 248us/step - loss: 0.1770 - mse: 0.1770\n",
      "Epoch 10/50\n",
      "506/506 [==============================] - 0s 262us/step - loss: 0.1672 - mse: 0.1672\n",
      "Epoch 11/50\n",
      "506/506 [==============================] - 0s 252us/step - loss: 0.1590 - mse: 0.1590\n",
      "Epoch 12/50\n",
      "506/506 [==============================] - 0s 162us/step - loss: 0.1570 - mse: 0.1570\n",
      "Epoch 13/50\n",
      "506/506 [==============================] - 0s 227us/step - loss: 0.1513 - mse: 0.1513\n",
      "Epoch 14/50\n",
      "506/506 [==============================] - 0s 233us/step - loss: 0.1430 - mse: 0.1430\n",
      "Epoch 15/50\n",
      "506/506 [==============================] - 0s 253us/step - loss: 0.1358 - mse: 0.1358\n",
      "Epoch 16/50\n",
      "506/506 [==============================] - 0s 187us/step - loss: 0.1308 - mse: 0.1308\n",
      "Epoch 17/50\n",
      "506/506 [==============================] - 0s 161us/step - loss: 0.1270 - mse: 0.1270\n",
      "Epoch 18/50\n",
      "506/506 [==============================] - 0s 138us/step - loss: 0.1232 - mse: 0.1232\n",
      "Epoch 19/50\n",
      "506/506 [==============================] - 0s 168us/step - loss: 0.1210 - mse: 0.1210\n",
      "Epoch 20/50\n",
      "506/506 [==============================] - 0s 160us/step - loss: 0.1159 - mse: 0.1159\n",
      "Epoch 21/50\n",
      "506/506 [==============================] - 0s 167us/step - loss: 0.1124 - mse: 0.1124\n",
      "Epoch 22/50\n",
      "506/506 [==============================] - 0s 203us/step - loss: 0.1121 - mse: 0.1121\n",
      "Epoch 23/50\n",
      "506/506 [==============================] - 0s 260us/step - loss: 0.1090 - mse: 0.1090\n",
      "Epoch 24/50\n",
      "506/506 [==============================] - 0s 244us/step - loss: 0.1055 - mse: 0.1055\n",
      "Epoch 25/50\n",
      "506/506 [==============================] - 0s 219us/step - loss: 0.1052 - mse: 0.1052\n",
      "Epoch 26/50\n",
      "506/506 [==============================] - 0s 242us/step - loss: 0.1014 - mse: 0.1014\n",
      "Epoch 27/50\n",
      "506/506 [==============================] - 0s 227us/step - loss: 0.0992 - mse: 0.0992\n",
      "Epoch 28/50\n",
      "506/506 [==============================] - 0s 256us/step - loss: 0.0989 - mse: 0.0989\n",
      "Epoch 29/50\n",
      "506/506 [==============================] - 0s 260us/step - loss: 0.0968 - mse: 0.0968\n",
      "Epoch 30/50\n",
      "506/506 [==============================] - 0s 258us/step - loss: 0.0959 - mse: 0.0959\n",
      "Epoch 31/50\n",
      "506/506 [==============================] - 0s 263us/step - loss: 0.0932 - mse: 0.0932\n",
      "Epoch 32/50\n",
      "506/506 [==============================] - 0s 254us/step - loss: 0.0922 - mse: 0.0922\n",
      "Epoch 33/50\n",
      "506/506 [==============================] - 0s 205us/step - loss: 0.0920 - mse: 0.0920\n",
      "Epoch 34/50\n",
      "506/506 [==============================] - 0s 224us/step - loss: 0.0913 - mse: 0.0913\n",
      "Epoch 35/50\n",
      "506/506 [==============================] - 0s 244us/step - loss: 0.0877 - mse: 0.0877\n",
      "Epoch 36/50\n",
      "506/506 [==============================] - 0s 221us/step - loss: 0.0872 - mse: 0.0872\n",
      "Epoch 37/50\n",
      "506/506 [==============================] - 0s 254us/step - loss: 0.0862 - mse: 0.0862\n",
      "Epoch 38/50\n",
      "506/506 [==============================] - 0s 227us/step - loss: 0.0848 - mse: 0.0848\n",
      "Epoch 39/50\n",
      "506/506 [==============================] - 0s 173us/step - loss: 0.0836 - mse: 0.0836\n",
      "Epoch 40/50\n",
      "506/506 [==============================] - 0s 210us/step - loss: 0.0846 - mse: 0.0846\n",
      "Epoch 41/50\n",
      "506/506 [==============================] - 0s 252us/step - loss: 0.0847 - mse: 0.0847\n",
      "Epoch 42/50\n",
      "506/506 [==============================] - 0s 237us/step - loss: 0.0803 - mse: 0.0803\n",
      "Epoch 43/50\n",
      "506/506 [==============================] - 0s 245us/step - loss: 0.0789 - mse: 0.0789\n",
      "Epoch 44/50\n",
      "506/506 [==============================] - 0s 245us/step - loss: 0.0778 - mse: 0.0778\n",
      "Epoch 45/50\n",
      "506/506 [==============================] - 0s 231us/step - loss: 0.0765 - mse: 0.0765\n",
      "Epoch 46/50\n",
      "506/506 [==============================] - 0s 250us/step - loss: 0.0769 - mse: 0.0769\n",
      "Epoch 47/50\n",
      "506/506 [==============================] - 0s 248us/step - loss: 0.0749 - mse: 0.0749\n",
      "Epoch 48/50\n",
      "506/506 [==============================] - 0s 179us/step - loss: 0.0742 - mse: 0.0742\n",
      "Epoch 49/50\n",
      "506/506 [==============================] - 0s 212us/step - loss: 0.0736 - mse: 0.0736\n",
      "Epoch 50/50\n",
      "506/506 [==============================] - 0s 239us/step - loss: 0.0739 - mse: 0.0739\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x2032241b248>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data, target, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.layers[1].get_weights()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(a):\n",
    "    return max(0, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls1 = model.layers[1].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = data.iloc[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "\n",
    "for i in range(len(ls1[0][0])):\n",
    "    temp = 0\n",
    "    for j in range(13):\n",
    "        temp += ls1[0][j][i] * d1[j]\n",
    "#     temp = relu(temp)\n",
    "    temp += ls1[0][1][i]\n",
    "    res.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.53393573])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(list(map(relu, d1.values @ l[0] + l[1]))) @ l[2] + l[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06076025, 0.63698067, 0.34921543, 0.14380016, 1.19569301,\n",
       "       0.30774345, 0.        , 0.        , 0.21937795, 0.17100938,\n",
       "       0.        , 0.        , 0.47213362, 0.        , 0.        ,\n",
       "       0.4706104 , 0.75636843, 1.2552416 , 0.55731434, 0.48246728,\n",
       "       0.        , 0.48963836, 0.        , 0.        , 0.        ,\n",
       "       0.03627886, 0.66644765, 0.41316772, 0.97149563, 0.        ,\n",
       "       0.468878  , 0.        ])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(list(map(relu, d1.values @ l[0] + l[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [-4.19644058e-01],\n",
       "       [-3.40095609e-01],\n",
       "       [-1.18138738e-01],\n",
       "       [-3.05645555e-01],\n",
       "       [ 1.49359941e-01],\n",
       "       [-5.06033152e-02],\n",
       "       [-3.40244323e-01]], dtype=float32)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.017624723339644627"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls1[1] @ res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
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      "        -0.1514905 ,  0.25688305, -0.28207955, -0.1392947 , -0.37758955,\n",
      "         0.15074076,  0.17919692,  0.00190188,  0.20315413,  0.16528755,\n",
      "        -0.22287506,  0.13791206,  0.08259504, -0.32153478, -0.05108   ,\n",
      "        -0.15560332, -0.19913626],\n",
      "       [ 0.20584138, -0.17445567, -0.18636592,  0.25146422, -0.19236611,\n",
      "        -0.2503508 ,  0.37371507,  0.24541757, -0.21062753, -0.3967676 ,\n",
      "        -0.05725698,  0.16839693, -0.33290425,  0.33768618,  0.21822843,\n",
      "         0.01885382, -0.37047473, -0.38907957, -0.35121882,  0.17694008,\n",
      "         0.4027563 ,  0.06920934,  0.25132558,  0.2912266 ,  0.17007118,\n",
      "         0.14769287, -0.13506944, -0.34494102, -0.16679129,  0.07865939,\n",
      "        -0.18201068,  0.12216482],\n",
      "       [-0.15760593, -0.01915189, -0.12003136,  0.129328  , -0.16875127,\n",
      "         0.3826287 , -0.20236205,  0.4579967 ,  0.15959956, -0.0209367 ,\n",
      "         0.02091598,  0.01470805, -0.03833028,  0.10009498,  0.4090367 ,\n",
      "         0.3652844 , -0.3631639 ,  0.15983671,  0.17315227,  0.03829157,\n",
      "         0.28153905, -0.0397496 ,  0.1479865 ,  0.15568364, -0.2639131 ,\n",
      "        -0.23253156, -0.02950091, -0.16927837, -0.13161454, -0.19367266,\n",
      "        -0.0767014 , -0.23555158],\n",
      "       [-0.33300608,  0.19258   , -0.4490105 ,  0.1241808 , -0.25623614,\n",
      "         0.17400141, -0.02123243,  0.31593376, -0.4457466 ,  0.0895621 ,\n",
      "         0.05297121,  0.07509573,  0.0366272 , -0.23763685,  0.08162166,\n",
      "         0.38789123, -0.1155489 , -0.16894336,  0.03787807, -0.2617292 ,\n",
      "         0.30978373, -0.41876566, -0.19889961,  0.13462375, -0.06886385,\n",
      "        -0.14764589, -0.20324059, -0.18464828, -0.3095649 , -0.31088585,\n",
      "        -0.22008425, -0.2548679 ]], dtype=float32), array([-0.08756598, -0.07635763, -0.04562698, -0.02940609,  0.19258595,\n",
      "       -0.03304979,  0.0017613 ,  0.04302534, -0.00352251,  0.08317549,\n",
      "       -0.02019708,  0.04721763,  0.2162317 , -0.05565662,  0.06060786,\n",
      "        0.06624735,  0.07780442, -0.06198244, -0.06904175,  0.0514497 ,\n",
      "        0.04577361,  0.04148291, -0.04391668, -0.02269206, -0.00320614,\n",
      "        0.15132359, -0.07513593, -0.10884636, -0.11047958, -0.08090383,\n",
      "        0.05470213, -0.06322961], dtype=float32)]\n",
      "[array([[ 0.16258085],\n",
      "       [ 0.3010663 ],\n",
      "       [ 0.4156838 ],\n",
      "       [ 0.26464933],\n",
      "       [-0.44346634],\n",
      "       [-0.2131083 ],\n",
      "       [-0.37692988],\n",
      "       [-0.28804883],\n",
      "       [ 0.35213804],\n",
      "       [-0.33065724],\n",
      "       [ 0.14639007],\n",
      "       [-0.40132436],\n",
      "       [-0.05975613],\n",
      "       [ 0.20691532],\n",
      "       [-0.1838144 ],\n",
      "       [ 0.0630216 ],\n",
      "       [-0.36134738],\n",
      "       [ 0.26143694],\n",
      "       [ 0.34481755],\n",
      "       [-0.40309995],\n",
      "       [-0.09808388],\n",
      "       [ 0.33865693],\n",
      "       [ 0.42177618],\n",
      "       [ 0.34111547],\n",
      "       [-0.39996862],\n",
      "       [-0.06093715],\n",
      "       [ 0.37340835],\n",
      "       [ 0.13286436],\n",
      "       [ 0.10926429],\n",
      "       [ 0.43365175],\n",
      "       [ 0.34226352],\n",
      "       [ 0.17222574]], dtype=float32), array([-0.06378994], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "for l in model.layers:\n",
    "    print(l.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "l = model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = model.layers[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models, layers\n",
    "\n",
    "input_layer = layers.Input(shape=(data.shape[1],))\n",
    "\n",
    "layer1 = layers.Dense(32, activation='relu')(input_layer)\n",
    "\n",
    "out_layer = layers.Dense(1, activation='linear')(layer1) ## 분류는 마지막 층에 activation이 sigmoid였지만 회귀에서는 설정하지 않는다(linear)\n",
    "\n",
    "m = models.Model(input_layer, out_layer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m.layers[1].set_weights(model.layers[1].get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 0.16817665]], dtype=float32)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-185-38518b2c7788>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "m.layers[3].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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