Boston Housing Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Only two main task classification and regersion in our whole life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import boston_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_data, train_targets), (test_data, test_targets) =boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z-score Normalization\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "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',input_shape=(train_data.shape[1],))) \n",
    "    model.add(layers.Dense(64, activation='relu'))\n",
    "    model.add(layers.Dense(1)) #Last layer no activiation function for last layer in regression problem\n",
    "    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 13)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing fold # 0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-d0750f79f3ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuild_model\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[0;32m     17\u001b[0m     model.fit(partial_train_data, partial_train_targets,\n\u001b[1;32m---> 18\u001b[1;33m               epochs=num_epochs, batch_size=1, verbose=0)\n\u001b[0m\u001b[0;32m     19\u001b[0m     \u001b[0mval_mse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_mae\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_targets\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mall_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_mae\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[0;32m   1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1241\u001b[0m     def evaluate(self,\n",
      "\u001b[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[0;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\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[0;32m    195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 196\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouts\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[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   3738\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3739\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3740\u001b[1;33m     \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3741\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3742\u001b[0m     \u001b[1;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1079\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1080\u001b[0m     \"\"\"\n\u001b[1;32m-> 1081\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1082\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1083\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\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[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1119\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[0;32m   1120\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[1;32m-> 1121\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1123\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1222\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[1;32m-> 1224\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[0;32m   1225\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\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[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\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[1;32m--> 511\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    512\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mg:\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                                                num_outputs)\n\u001b[0m\u001b[0;32m     62\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "k = 4\n",
    "num_val_samples = len(train_data) // k\n",
    "num_epochs = 400\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([train_data[:i * num_val_samples],\n",
    "                                         train_data[(i + 1) * num_val_samples:]],\n",
    "                                        axis=0)\n",
    "    partial_train_targets = np.concatenate([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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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_mae']\n",
    "    all_mae_histories.append(mae_history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]\n",
    "average_mae_history\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation MAE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102/102 [==============================] - 0s 153us/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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.724813222885132"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mae_score"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}