Regression model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:72: UserWarning: h5py is running against HDF5 1.10.2 when it was built against 1.10.3, this may cause problems\n",
      "  '{0}.{1}.{2}'.format(*version.hdf5_built_version_tuple)\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/keras-datasets/boston_housing.npz\n",
      "57344/57026 [==============================] - 1s 14us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import boston_housing\n",
    "(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
       "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
       "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
       "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
       "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
       "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
       "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
       "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
       "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
       "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
       "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
       "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
       "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
       "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
       "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
       "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
       "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
       "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
       "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
       "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
       "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
       "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
       "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
       "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
       "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
       "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
       "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
       "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
       "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
       "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
       "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
       "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
       "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
       "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
       "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
       "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
       "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalizing data\n",
    "mean = train_data.mean(axis=0)\n",
    "train_data -= mean\n",
    "std = train_data.std(axis=0)\n",
    "train_data /= std\n",
    "test_data -= mean\n",
    "test_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model definition\n",
    "from keras import models\n",
    "from keras import layers\n",
    "def build_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Dense(64, activation='relu',\n",
    "    input_shape=(train_data.shape[1],)))\n",
    "    model.add(layers.Dense(64, activation='relu'))\n",
    "    model.add(layers.Dense(1))\n",
    "    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing fold # 0\n",
      "processing fold # 1\n",
      "processing fold # 2\n",
      "processing fold # 3\n"
     ]
    }
   ],
   "source": [
    "#K-fold validation\n",
    "import numpy as np\n",
    "k = 4\n",
    "num_val_samples = len(train_data) // k\n",
    "num_epochs = 100\n",
    "all_scores = []\n",
    "for i in range(k):\n",
    "    print('processing fold #', i)\n",
    "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    partial_train_data = np.concatenate(\n",
    "             [train_data[:i * num_val_samples],\n",
    "            train_data[(i + 1) * num_val_samples:]],\n",
    "            axis=0)\n",
    "    partial_train_targets = np.concatenate(\n",
    "     [train_targets[:i * num_val_samples],\n",
    "      train_targets[(i + 1) * num_val_samples:]],\n",
    "      axis=0)\n",
    "    model = build_model()\n",
    "    model.fit(partial_train_data, partial_train_targets,\n",
    "    epochs=num_epochs, batch_size=1, verbose=0)\n",
    "    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
    "    all_scores.append(val_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.0839788783894906, 2.231844699028695, 3.016034992614595, 2.3369768911068984]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4172088652849197"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(all_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing fold # 0\n",
      "processing fold # 1\n",
      "processing fold # 2\n",
      "processing fold # 3\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 500\n",
    "all_mae_histories = []\n",
    "for i in range(k):\n",
    "    print('processing fold #', i)\n",
    "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    partial_train_data = np.concatenate(\n",
    "    [train_data[:i * num_val_samples],\n",
    "    train_data[(i + 1) * num_val_samples:]],\n",
    "    axis=0)\n",
    "    partial_train_targets = np.concatenate(\n",
    "    [train_targets[:i * num_val_samples],\n",
    "    train_targets[(i + 1) * num_val_samples:]],\n",
    "    axis=0)\n",
    "    model = build_model()\n",
    "    history = model.fit(partial_train_data, partial_train_targets,\n",
    "    validation_data=(val_data, val_targets),\n",
    "    epochs=num_epochs, batch_size=1, verbose=0)\n",
    "    mae_history = history.history['val_mean_absolute_error']\n",
    "    all_mae_histories.append(mae_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_mae_history = [\n",
    "np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation MAE')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "def smooth_curve(points, factor=0.9):\n",
    "  smoothed_points = []\n",
    "  for point in points:\n",
    "    if smoothed_points:\n",
    "      previous = smoothed_points[-1]\n",
    "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
    "    else:\n",
    "      smoothed_points.append(point)\n",
    "  return smoothed_points\n",
    "smooth_mae_history = smooth_curve(average_mae_history[10:])\n",
    "\n",
    "plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation MAE')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102/102 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.fit(train_data, train_targets,\n",
    "epochs=80, batch_size=16, verbose=0)\n",
    "test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6795161471647373"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mae_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}