model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import *\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "url_data = 'data - 10,000.csv'\n",
    "df = pd.read_csv(url_data, index_col=0)\n",
    "\n",
    "X = df.loc[:,'G1a':'G2b']\n",
    "y = df.loc[:, 'force']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=64, activation='relu', input_dim=5))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "model.compile(loss='mse', optimizer=SGD(lr=0.003))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'SGD',\n",
       " 'learning_rate': 0.003,\n",
       " 'decay': 0.0,\n",
       " 'momentum': 0.0,\n",
       " 'nesterov': False}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.optimizer.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5600 samples, validate on 1400 samples\n",
      "Epoch 1/100\n",
      "5600/5600 - 0s - loss: 3.5059 - val_loss: 2.3523\n",
      "Epoch 2/100\n",
      "5600/5600 - 0s - loss: 2.3123 - val_loss: 2.2196\n",
      "Epoch 3/100\n",
      "5600/5600 - 0s - loss: 2.1949 - val_loss: 2.1150\n",
      "Epoch 4/100\n",
      "5600/5600 - 0s - loss: 2.1018 - val_loss: 2.0293\n",
      "Epoch 5/100\n",
      "5600/5600 - 0s - loss: 2.0251 - val_loss: 1.9545\n",
      "Epoch 6/100\n",
      "5600/5600 - 0s - loss: 1.9568 - val_loss: 1.8901\n",
      "Epoch 7/100\n",
      "5600/5600 - 0s - loss: 1.8986 - val_loss: 1.8428\n",
      "Epoch 8/100\n",
      "5600/5600 - 0s - loss: 1.8465 - val_loss: 1.7867\n",
      "Epoch 9/100\n",
      "5600/5600 - 0s - loss: 1.7999 - val_loss: 1.7422\n",
      "Epoch 10/100\n",
      "5600/5600 - 0s - loss: 1.7580 - val_loss: 1.7022\n",
      "Epoch 11/100\n",
      "5600/5600 - 0s - loss: 1.7176 - val_loss: 1.6654\n",
      "Epoch 12/100\n",
      "5600/5600 - 0s - loss: 1.6805 - val_loss: 1.6306\n",
      "Epoch 13/100\n",
      "5600/5600 - 0s - loss: 1.6442 - val_loss: 1.6004\n",
      "Epoch 14/100\n",
      "5600/5600 - 0s - loss: 1.6108 - val_loss: 1.5692\n",
      "Epoch 15/100\n",
      "5600/5600 - 0s - loss: 1.5803 - val_loss: 1.5408\n",
      "Epoch 16/100\n",
      "5600/5600 - 0s - loss: 1.5509 - val_loss: 1.5136\n",
      "Epoch 17/100\n",
      "5600/5600 - 0s - loss: 1.5238 - val_loss: 1.4885\n",
      "Epoch 18/100\n",
      "5600/5600 - 0s - loss: 1.4985 - val_loss: 1.4675\n",
      "Epoch 19/100\n",
      "5600/5600 - 0s - loss: 1.4754 - val_loss: 1.4537\n",
      "Epoch 20/100\n",
      "5600/5600 - 0s - loss: 1.4545 - val_loss: 1.4262\n",
      "Epoch 21/100\n",
      "5600/5600 - 0s - loss: 1.4338 - val_loss: 1.4088\n",
      "Epoch 22/100\n",
      "5600/5600 - 0s - loss: 1.4135 - val_loss: 1.3929\n",
      "Epoch 23/100\n",
      "5600/5600 - 0s - loss: 1.3976 - val_loss: 1.3788\n",
      "Epoch 24/100\n",
      "5600/5600 - 0s - loss: 1.3824 - val_loss: 1.3829\n",
      "Epoch 25/100\n",
      "5600/5600 - 0s - loss: 1.3682 - val_loss: 1.3506\n",
      "Epoch 26/100\n",
      "5600/5600 - 0s - loss: 1.3531 - val_loss: 1.3528\n",
      "Epoch 27/100\n",
      "5600/5600 - 0s - loss: 1.3425 - val_loss: 1.3303\n",
      "Epoch 28/100\n",
      "5600/5600 - 0s - loss: 1.3308 - val_loss: 1.3277\n",
      "Epoch 29/100\n",
      "5600/5600 - 0s - loss: 1.3191 - val_loss: 1.3094\n",
      "Epoch 30/100\n",
      "5600/5600 - 0s - loss: 1.3109 - val_loss: 1.3031\n",
      "Epoch 31/100\n",
      "5600/5600 - 0s - loss: 1.3020 - val_loss: 1.3115\n",
      "Epoch 32/100\n",
      "5600/5600 - 0s - loss: 1.2950 - val_loss: 1.2863\n",
      "Epoch 33/100\n",
      "5600/5600 - 0s - loss: 1.2863 - val_loss: 1.2808\n",
      "Epoch 34/100\n",
      "5600/5600 - 0s - loss: 1.2817 - val_loss: 1.2744\n",
      "Epoch 35/100\n",
      "5600/5600 - 0s - loss: 1.2757 - val_loss: 1.2716\n",
      "Epoch 36/100\n",
      "5600/5600 - 0s - loss: 1.2699 - val_loss: 1.2704\n",
      "Epoch 37/100\n",
      "5600/5600 - 0s - loss: 1.2639 - val_loss: 1.2619\n",
      "Epoch 38/100\n",
      "5600/5600 - 0s - loss: 1.2600 - val_loss: 1.2848\n",
      "Epoch 39/100\n",
      "5600/5600 - 0s - loss: 1.2586 - val_loss: 1.2613\n",
      "Epoch 40/100\n",
      "5600/5600 - 0s - loss: 1.2536 - val_loss: 1.2579\n",
      "Epoch 41/100\n",
      "5600/5600 - 0s - loss: 1.2506 - val_loss: 1.2472\n",
      "Epoch 42/100\n",
      "5600/5600 - 0s - loss: 1.2484 - val_loss: 1.2450\n",
      "Epoch 43/100\n",
      "5600/5600 - 0s - loss: 1.2444 - val_loss: 1.2497\n",
      "Epoch 44/100\n",
      "5600/5600 - 0s - loss: 1.2436 - val_loss: 1.2474\n",
      "Epoch 45/100\n",
      "5600/5600 - 0s - loss: 1.2414 - val_loss: 1.2397\n",
      "Epoch 46/100\n",
      "5600/5600 - 0s - loss: 1.2400 - val_loss: 1.2377\n",
      "Epoch 47/100\n",
      "5600/5600 - 0s - loss: 1.2378 - val_loss: 1.2383\n",
      "Epoch 48/100\n",
      "5600/5600 - 0s - loss: 1.2365 - val_loss: 1.2342\n",
      "Epoch 49/100\n",
      "5600/5600 - 0s - loss: 1.2360 - val_loss: 1.2346\n",
      "Epoch 50/100\n",
      "5600/5600 - 0s - loss: 1.2353 - val_loss: 1.2323\n",
      "Epoch 51/100\n",
      "5600/5600 - 0s - loss: 1.2337 - val_loss: 1.2349\n",
      "Epoch 52/100\n",
      "5600/5600 - 0s - loss: 1.2345 - val_loss: 1.2317\n",
      "Epoch 53/100\n",
      "5600/5600 - 0s - loss: 1.2326 - val_loss: 1.2323\n",
      "Epoch 54/100\n",
      "5600/5600 - 0s - loss: 1.2302 - val_loss: 1.2436\n",
      "Epoch 55/100\n",
      "5600/5600 - 0s - loss: 1.2318 - val_loss: 1.2306\n",
      "Epoch 56/100\n",
      "5600/5600 - 0s - loss: 1.2294 - val_loss: 1.2301\n",
      "Epoch 57/100\n",
      "5600/5600 - 0s - loss: 1.2294 - val_loss: 1.2352\n",
      "Epoch 58/100\n",
      "5600/5600 - 0s - loss: 1.2278 - val_loss: 1.2274\n",
      "Epoch 59/100\n",
      "5600/5600 - 0s - loss: 1.2278 - val_loss: 1.2268\n",
      "Epoch 60/100\n",
      "5600/5600 - 0s - loss: 1.2267 - val_loss: 1.2493\n",
      "Epoch 61/100\n",
      "5600/5600 - 0s - loss: 1.2270 - val_loss: 1.2350\n",
      "Epoch 62/100\n",
      "5600/5600 - 0s - loss: 1.2266 - val_loss: 1.2293\n",
      "Epoch 63/100\n",
      "5600/5600 - 0s - loss: 1.2244 - val_loss: 1.2359\n",
      "Epoch 64/100\n",
      "5600/5600 - 0s - loss: 1.2246 - val_loss: 1.2393\n",
      "Epoch 65/100\n",
      "5600/5600 - 0s - loss: 1.2239 - val_loss: 1.2277\n",
      "Epoch 66/100\n",
      "5600/5600 - 0s - loss: 1.2246 - val_loss: 1.2253\n",
      "Epoch 67/100\n",
      "5600/5600 - 0s - loss: 1.2222 - val_loss: 1.2315\n",
      "Epoch 68/100\n",
      "5600/5600 - 0s - loss: 1.2216 - val_loss: 1.2267\n",
      "Epoch 69/100\n",
      "5600/5600 - 0s - loss: 1.2229 - val_loss: 1.2252\n",
      "Epoch 70/100\n",
      "5600/5600 - 0s - loss: 1.2221 - val_loss: 1.2260\n",
      "Epoch 71/100\n",
      "5600/5600 - 0s - loss: 1.2219 - val_loss: 1.2244\n",
      "Epoch 72/100\n",
      "5600/5600 - 0s - loss: 1.2196 - val_loss: 1.2275\n",
      "Epoch 73/100\n",
      "5600/5600 - 0s - loss: 1.2230 - val_loss: 1.2300\n",
      "Epoch 74/100\n",
      "5600/5600 - 0s - loss: 1.2218 - val_loss: 1.2239\n",
      "Epoch 75/100\n",
      "5600/5600 - 0s - loss: 1.2197 - val_loss: 1.2318\n",
      "Epoch 76/100\n",
      "5600/5600 - 0s - loss: 1.2210 - val_loss: 1.2368\n",
      "Epoch 77/100\n",
      "5600/5600 - 0s - loss: 1.2196 - val_loss: 1.2242\n",
      "Epoch 78/100\n",
      "5600/5600 - 0s - loss: 1.2209 - val_loss: 1.2234\n",
      "Epoch 79/100\n",
      "5600/5600 - 0s - loss: 1.2202 - val_loss: 1.2238\n",
      "Epoch 80/100\n",
      "5600/5600 - 0s - loss: 1.2203 - val_loss: 1.2297\n",
      "Epoch 81/100\n",
      "5600/5600 - 0s - loss: 1.2206 - val_loss: 1.2282\n",
      "Epoch 82/100\n",
      "5600/5600 - 0s - loss: 1.2188 - val_loss: 1.2243\n",
      "Epoch 83/100\n",
      "5600/5600 - 0s - loss: 1.2190 - val_loss: 1.2244\n",
      "Epoch 84/100\n",
      "5600/5600 - 0s - loss: 1.2189 - val_loss: 1.2324\n",
      "Epoch 85/100\n",
      "5600/5600 - 0s - loss: 1.2185 - val_loss: 1.2256\n",
      "Epoch 86/100\n",
      "5600/5600 - 0s - loss: 1.2199 - val_loss: 1.2234\n",
      "Epoch 87/100\n",
      "5600/5600 - 0s - loss: 1.2200 - val_loss: 1.2240\n",
      "Epoch 88/100\n",
      "5600/5600 - 0s - loss: 1.2199 - val_loss: 1.2244\n",
      "Epoch 89/100\n",
      "5600/5600 - 0s - loss: 1.2198 - val_loss: 1.2282\n",
      "Epoch 90/100\n",
      "5600/5600 - 0s - loss: 1.2199 - val_loss: 1.2268\n",
      "Epoch 91/100\n",
      "5600/5600 - 0s - loss: 1.2197 - val_loss: 1.2344\n",
      "Epoch 92/100\n",
      "5600/5600 - 0s - loss: 1.2191 - val_loss: 1.2227\n",
      "Epoch 93/100\n",
      "5600/5600 - 0s - loss: 1.2174 - val_loss: 1.2222\n",
      "Epoch 94/100\n",
      "5600/5600 - 0s - loss: 1.2175 - val_loss: 1.2270\n",
      "Epoch 95/100\n",
      "5600/5600 - 0s - loss: 1.2184 - val_loss: 1.2312\n",
      "Epoch 96/100\n",
      "5600/5600 - 0s - loss: 1.2162 - val_loss: 1.2231\n",
      "Epoch 97/100\n",
      "5600/5600 - 0s - loss: 1.2164 - val_loss: 1.2604\n",
      "Epoch 98/100\n",
      "5600/5600 - 0s - loss: 1.2184 - val_loss: 1.2220\n",
      "Epoch 99/100\n",
      "5600/5600 - 0s - loss: 1.2172 - val_loss: 1.2228\n",
      "Epoch 100/100\n",
      "5600/5600 - 0s - loss: 1.2168 - val_loss: 1.2225\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=X_train, y=y_train, validation_split=0.2, epochs=100, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['train', 'val'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000/3000 [==============================] - 0s 24us/sample - loss: 1.1587\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.1587189798355102"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.5755742],\n",
       "       [1.8691835],\n",
       "       [3.9365354],\n",
       "       ...,\n",
       "       [3.7540705],\n",
       "       [2.9579337],\n",
       "       [2.9604812]], dtype=float32)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9324    4.208496\n",
       "62      1.544645\n",
       "2559    3.472602\n",
       "2197    0.888309\n",
       "8176    3.475933\n",
       "          ...   \n",
       "3779    5.457604\n",
       "8864    4.496632\n",
       "3836    4.077334\n",
       "4297    2.367751\n",
       "4247    2.728932\n",
       "Name: force, Length: 3000, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}