Optimization_Project_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Task 1: Creating the Surrogate Model</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Input,Dense\n",
    "from keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>Loading the dataset and splitting it to 60% training and 40% validation</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv(\"./Desktop/ds_opt/dataset2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>size(MB)</th>\n",
       "      <th>time(Seconds)</th>\n",
       "      <th>PPI</th>\n",
       "      <th>PPI(Normalized)</th>\n",
       "      <th>Res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16x16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0</td>\n",
       "      <td>4.351</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42x11</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>0</td>\n",
       "      <td>8.350</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32x32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0.003072</td>\n",
       "      <td>0</td>\n",
       "      <td>8.703</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40x30</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0</td>\n",
       "      <td>9.615</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42x32</td>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>0.004032</td>\n",
       "      <td>0</td>\n",
       "      <td>10.154</td>\n",
       "      <td>0.001715</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>7680x4800</td>\n",
       "      <td>7680</td>\n",
       "      <td>4800</td>\n",
       "      <td>110.600000</td>\n",
       "      <td>1264</td>\n",
       "      <td>1741.700</td>\n",
       "      <td>0.513303</td>\n",
       "      <td>36864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>8192x4608</td>\n",
       "      <td>8192</td>\n",
       "      <td>4608</td>\n",
       "      <td>113.250000</td>\n",
       "      <td>1294</td>\n",
       "      <td>1807.500</td>\n",
       "      <td>0.532743</td>\n",
       "      <td>37748736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>10240x4320</td>\n",
       "      <td>10240</td>\n",
       "      <td>4320</td>\n",
       "      <td>132.700000</td>\n",
       "      <td>1516</td>\n",
       "      <td>2137.300</td>\n",
       "      <td>0.630183</td>\n",
       "      <td>44236800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>8192x8192</td>\n",
       "      <td>8192</td>\n",
       "      <td>8192</td>\n",
       "      <td>201.330000</td>\n",
       "      <td>2300</td>\n",
       "      <td>2228.000</td>\n",
       "      <td>0.656981</td>\n",
       "      <td>67108864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>15360x8640</td>\n",
       "      <td>15360</td>\n",
       "      <td>8640</td>\n",
       "      <td>398.100000</td>\n",
       "      <td>4549</td>\n",
       "      <td>3389.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>132710400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Resolution  Width  Height    size(MB)  time(Seconds)       PPI  \\\n",
       "0         16x16     16      16    0.000768              0     4.351   \n",
       "1         42x11     42      11    0.001386              0     8.350   \n",
       "2         32x32     32      32    0.003072              0     8.703   \n",
       "3         40x30     40      30    0.003600              0     9.615   \n",
       "4         42x32     42      32    0.004032              0    10.154   \n",
       "..          ...    ...     ...         ...            ...       ...   \n",
       "188   7680x4800   7680    4800  110.600000           1264  1741.700   \n",
       "189   8192x4608   8192    4608  113.250000           1294  1807.500   \n",
       "190  10240x4320  10240    4320  132.700000           1516  2137.300   \n",
       "191   8192x8192   8192    8192  201.330000           2300  2228.000   \n",
       "192  15360x8640  15360    8640  398.100000           4549  3389.000   \n",
       "\n",
       "     PPI(Normalized)        Res  \n",
       "0           0.000000        256  \n",
       "1           0.001182        462  \n",
       "2           0.001286       1024  \n",
       "3           0.001555       1200  \n",
       "4           0.001715       1344  \n",
       "..               ...        ...  \n",
       "188         0.513303   36864000  \n",
       "189         0.532743   37748736  \n",
       "190         0.630183   44236800  \n",
       "191         0.656981   67108864  \n",
       "192         1.000000  132710400  \n",
       "\n",
       "[193 rows x 8 columns]"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Res']=dataset['Width']*dataset['Height']\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=dataset.sample(frac=0.6,random_state=0)\n",
    "test_data=dataset.drop(train_data.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>Sanity check that train and test set are unique</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(test_data.merge(train_data).empty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>As we can see below, the time and quality are functions of Resolution</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x123e7ad72e0>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_data[['Res','time(Seconds)','PPI']],diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>Getting rid of useless features, because we are interested in the Width and Height only</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1           462\n",
       "3          1200\n",
       "6          2400\n",
       "9          4608\n",
       "11         4800\n",
       "         ...   \n",
       "177     9216000\n",
       "182    16384000\n",
       "184    20358144\n",
       "187    33177600\n",
       "191    67108864\n",
       "Name: Res, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.pop('PPI(Normalized)')\n",
    "train_data.pop('size(MB)')\n",
    "train_data.pop('Resolution')\n",
    "train_data.pop('Res')\n",
    "test_data.pop('PPI(Normalized)')\n",
    "test_data.pop('size(MB)')\n",
    "test_data.pop('Resolution')\n",
    "test_data.pop('Res')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>Separate features from labels in both Training and Test set</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features=train_data.copy()\n",
    "test_features=test_data.copy()\n",
    "train_labels1=train_features.pop('time(Seconds)').tolist()\n",
    "train_labels2=train_features.pop('PPI').tolist()\n",
    "test_labels1=test_features.pop('time(Seconds)').tolist()\n",
    "test_labels2=test_features.pop('PPI').tolist()\n",
    "\n",
    "train_labels=np.concatenate((np.array(train_labels1).reshape((len(train_features),1)),np.array(train_labels2).reshape(len(train_features),1)),axis=1)\n",
    "test_labels=np.concatenate((np.array(test_labels1).reshape((len(test_features),1)),np.array(test_labels2).reshape(len(test_features),1)),axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>Creating the model</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer = preprocessing.Normalization()\n",
    "normalizer.adapt(np.array(train_features))\n",
    "#Feature normalization layer that normalizes the training features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.Sequential([\n",
    "    normalizer,\n",
    "    Dense(256,activation='relu'),\n",
    "    Dense(512,activation='relu'),\n",
    "    Dense(2)\n",
    "])\n",
    "model.compile(optimizer='adam',loss='MeanAbsoluteError')\n",
    "early_stop=EarlyStopping(monitor='val_loss',mode='min',patience=100,restore_best_weights=True)\n",
    "early_stop2=EarlyStopping(monitor='loss',mode='min',patience=50,restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>Training the model using a callback function for early stopping to avoid overfitting, and using 20% of the training set as a cross-validation set</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 290.4792 - val_loss: 160.5719\n",
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      "Epoch 65/2000\n",
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      "Epoch 89/2000\n",
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      "Epoch 98/2000\n",
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      "Epoch 457/2000\n",
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      "Epoch 463/2000\n",
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      "Epoch 467/2000\n",
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      "Epoch 468/2000\n",
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      "Epoch 469/2000\n",
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      "Epoch 470/2000\n",
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      "Epoch 471/2000\n",
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      "Epoch 472/2000\n",
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      "Epoch 473/2000\n",
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      "Epoch 474/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.4730 - val_loss: 1.0902\n",
      "Epoch 475/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.4490 - val_loss: 1.2878\n",
      "Epoch 476/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.4882 - val_loss: 1.1842\n",
      "Epoch 477/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.3824 - val_loss: 1.1791\n",
      "Epoch 478/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.3094 - val_loss: 1.2881\n",
      "Epoch 479/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.2906 - val_loss: 1.2088\n",
      "Epoch 480/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.2930 - val_loss: 1.1113\n",
      "Epoch 481/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.3886 - val_loss: 1.2201\n",
      "Epoch 482/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.2231 - val_loss: 1.2850\n",
      "Epoch 483/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.5359 - val_loss: 1.5546\n",
      "Epoch 484/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.7174 - val_loss: 1.7354\n",
      "Epoch 485/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.7096 - val_loss: 1.5421\n",
      "Epoch 486/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.5436 - val_loss: 0.9068\n",
      "Epoch 487/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.4103 - val_loss: 1.6370\n",
      "Epoch 488/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.8512 - val_loss: 1.3696\n",
      "Epoch 489/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.2858 - val_loss: 1.4862\n",
      "Epoch 490/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.6269 - val_loss: 1.0470\n",
      "Epoch 491/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.5226 - val_loss: 1.4205\n",
      "Epoch 492/2000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 7.4151 - val_loss: 1.1664\n",
      "Epoch 493/2000\n",
      "3/3 [==============================] - 0s 6ms/step - loss: 7.3551 - val_loss: 1.4021\n"
     ]
    }
   ],
   "source": [
    "hist_train=model.fit(train_features,train_labels,validation_split=0.2,epochs=2000,callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 997us/step - loss: 3.5949\n"
     ]
    }
   ],
   "source": [
    "hist_acc=model.evaluate(test_features,test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5>The loss is acceptable, so we can now train the model on the test set so it can improve even more</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123e11934f0>]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(range(len(hist_train.history['val_loss'])),hist_train.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "2/2 [==============================] - 0s 22ms/step - loss: 1.0776 - val_loss: 13.2150\n",
      "Epoch 2/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0610 - val_loss: 13.2922\n",
      "Epoch 3/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0664 - val_loss: 13.4574\n",
      "Epoch 4/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0474 - val_loss: 13.5467\n",
      "Epoch 5/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0623 - val_loss: 13.5510\n",
      "Epoch 6/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0535 - val_loss: 13.6403\n",
      "Epoch 7/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0681 - val_loss: 13.6380\n",
      "Epoch 8/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0306 - val_loss: 13.7710\n",
      "Epoch 9/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0446 - val_loss: 14.0974\n",
      "Epoch 10/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0642 - val_loss: 14.0715\n",
      "Epoch 11/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0090 - val_loss: 14.1127\n",
      "Epoch 12/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0460 - val_loss: 14.2931\n",
      "Epoch 13/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0342 - val_loss: 14.4825\n",
      "Epoch 14/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0007 - val_loss: 14.6867\n",
      "Epoch 15/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0320 - val_loss: 14.7587\n",
      "Epoch 16/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9958 - val_loss: 14.6859\n",
      "Epoch 17/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0550 - val_loss: 14.9548\n",
      "Epoch 18/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 1.0587 - val_loss: 15.3828\n",
      "Epoch 19/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 1.0981 - val_loss: 15.1449\n",
      "Epoch 20/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9881 - val_loss: 15.1707\n",
      "Epoch 21/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9824 - val_loss: 15.3460\n",
      "Epoch 22/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9729 - val_loss: 15.5160\n",
      "Epoch 23/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9709 - val_loss: 15.4643\n",
      "Epoch 24/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.9559 - val_loss: 15.3525\n",
      "Epoch 25/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9580 - val_loss: 15.5169\n",
      "Epoch 26/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9630 - val_loss: 15.8116\n",
      "Epoch 27/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9618 - val_loss: 15.6871\n",
      "Epoch 28/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9476 - val_loss: 15.5714\n",
      "Epoch 29/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9733 - val_loss: 15.7261\n",
      "Epoch 30/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9648 - val_loss: 16.1424\n",
      "Epoch 31/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9547 - val_loss: 16.1351\n",
      "Epoch 32/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.9326 - val_loss: 15.9440\n",
      "Epoch 33/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9796 - val_loss: 16.1883\n",
      "Epoch 34/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9724 - val_loss: 16.5379\n",
      "Epoch 35/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9756 - val_loss: 16.3551\n",
      "Epoch 36/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9311 - val_loss: 16.3393\n",
      "Epoch 37/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9793 - val_loss: 16.2769\n",
      "Epoch 38/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9512 - val_loss: 16.4070\n",
      "Epoch 39/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9513 - val_loss: 16.3334\n",
      "Epoch 40/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9477 - val_loss: 16.4558\n",
      "Epoch 41/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9301 - val_loss: 16.6785\n",
      "Epoch 42/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.9461 - val_loss: 16.6871\n",
      "Epoch 43/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9293 - val_loss: 16.6126\n",
      "Epoch 44/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9067 - val_loss: 16.7694\n",
      "Epoch 45/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9161 - val_loss: 16.7355\n",
      "Epoch 46/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9237 - val_loss: 16.9759\n",
      "Epoch 47/1000\n",
      "2/2 [==============================] - 0s 11ms/step - loss: 0.9525 - val_loss: 16.8581\n",
      "Epoch 48/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9658 - val_loss: 16.7221\n",
      "Epoch 49/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9944 - val_loss: 17.0146\n",
      "Epoch 50/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9512 - val_loss: 16.9087\n",
      "Epoch 51/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.9492 - val_loss: 16.9725\n",
      "Epoch 52/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9174 - val_loss: 16.8068\n",
      "Epoch 53/1000\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 0.9110 - val_loss: 16.6712\n",
      "Epoch 54/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9202 - val_loss: 17.0382\n",
      "Epoch 55/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9772 - val_loss: 17.1085\n",
      "Epoch 56/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9739 - val_loss: 16.5551\n",
      "Epoch 57/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9686 - val_loss: 16.8656\n",
      "Epoch 58/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9536 - val_loss: 17.0738\n",
      "Epoch 59/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8983 - val_loss: 17.1566\n",
      "Epoch 60/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8996 - val_loss: 17.1578\n",
      "Epoch 61/1000\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 0.8924 - val_loss: 17.1514\n",
      "Epoch 62/1000\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 0.9156 - val_loss: 17.1816\n",
      "Epoch 63/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9317 - val_loss: 16.9615\n",
      "Epoch 64/1000\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 0.9552 - val_loss: 17.0849\n",
      "Epoch 65/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9330 - val_loss: 17.0301\n",
      "Epoch 66/1000\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 0.9488 - val_loss: 17.0645\n",
      "Epoch 67/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9052 - val_loss: 16.8947\n",
      "Epoch 68/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9017 - val_loss: 17.0749\n",
      "Epoch 69/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9177 - val_loss: 17.3481\n",
      "Epoch 70/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9235 - val_loss: 17.2197\n",
      "Epoch 71/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.9088 - val_loss: 17.2988\n",
      "Epoch 72/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8839 - val_loss: 17.3211\n",
      "Epoch 73/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8852 - val_loss: 17.3285\n",
      "Epoch 74/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8900 - val_loss: 17.3202\n",
      "Epoch 75/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8903 - val_loss: 17.3007\n",
      "Epoch 76/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9633 - val_loss: 17.4063\n",
      "Epoch 77/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8744 - val_loss: 17.4385\n",
      "Epoch 78/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8844 - val_loss: 17.4695\n",
      "Epoch 79/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8858 - val_loss: 17.4196\n",
      "Epoch 80/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8809 - val_loss: 17.4576\n",
      "Epoch 81/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8995 - val_loss: 17.3430\n",
      "Epoch 82/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8839 - val_loss: 17.6795\n",
      "Epoch 83/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8766 - val_loss: 17.6858\n",
      "Epoch 84/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8636 - val_loss: 17.5141\n",
      "Epoch 85/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8880 - val_loss: 17.7044\n",
      "Epoch 86/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8756 - val_loss: 17.7923\n",
      "Epoch 87/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8802 - val_loss: 17.9198\n",
      "Epoch 88/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.9009 - val_loss: 17.5342\n",
      "Epoch 89/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8774 - val_loss: 17.6868\n",
      "Epoch 90/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8872 - val_loss: 17.7257\n",
      "Epoch 91/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8740 - val_loss: 17.6464\n",
      "Epoch 92/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8792 - val_loss: 17.5506\n",
      "Epoch 93/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8735 - val_loss: 17.4235\n",
      "Epoch 94/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.8621 - val_loss: 17.6694\n",
      "Epoch 95/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8564 - val_loss: 17.8209\n",
      "Epoch 96/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8798 - val_loss: 17.8184\n",
      "Epoch 97/1000\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.8880 - val_loss: 17.6796\n",
      "Epoch 98/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.8756 - val_loss: 17.8178\n",
      "Epoch 99/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9918 - val_loss: 17.8346\n",
      "Epoch 100/1000\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.9328 - val_loss: 17.8823\n",
      "Epoch 101/1000\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 0.9060 - val_loss: 17.7527\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x123e1c27be0>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(test_features,test_labels,epochs=1000,callbacks=[early_stop],validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Task 2: Finding the best trade-off (Multi-objective optimization)</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This cell was made using exhaustive brute force of the weight below on the PPI (Quality), and it was concluded that by\n",
    "#increasing the weight of the PPI objective function, it changes the resolution, but between -500,000 and -1,500,000\n",
    "#The minimum weighted sum remained the same, and though we achieved the best trade-off with no boundaries on either quality\n",
    "#nor time.\n",
    "#The method used is weighted sum that minimizes time and maximizes quality, so to make it simpler, the weighted sum was used to\n",
    "#minimize time and minimize the inverse of quality.\n",
    "\n",
    "list_time=[]\n",
    "list_ppi=[]\n",
    "list_res=[]\n",
    "min_sum=10000000\n",
    "min_i=0\n",
    "min_j=0\n",
    "k=0\n",
    "for i,j in zip(dataset['Width'],dataset['Height']):\n",
    "        arr=model.predict([[i,j]])\n",
    "        ppi_now=-1./arr[0][1].squeeze()\n",
    "        time_now=arr[0][0].squeeze()\n",
    "        list_time.append(max(0,time_now))\n",
    "        list_res.append((i,j))\n",
    "        list_ppi.append(ppi_now)\n",
    "        i_sum=(-1500000*ppi_now)+time_now\n",
    "        if i_sum<min_sum:\n",
    "            min_sum=i_sum\n",
    "            min_i=i\n",
    "            min_j=j\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123eb957a90>]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "list_ppi=-1./np.array(list_ppi)\n",
    "ind=list_res.index((min_i,min_j))\n",
    "plt.scatter(list_ppi,list_time)\n",
    "plt.plot(list_ppi[ind],list_time[ind],\"r*\")\n",
    "\n",
    "##A scatter plot of time and quality and the red dot represents the best tradeoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123eadc43a0>]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(range(len(list_ppi)),list_ppi)\n",
    "plt.plot(range(len(list_time)),list_time)\n",
    "plt.plot(ind,list_time[ind],\"r*\")\n",
    "plt.plot(ind,list_ppi[ind],\"r*\")\n",
    "##A plot of time and quality and the red dots represents the best tradeoff time and quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best trade-off resolution is: 5120x2160 that takes time= 381.56366 seconds and quality= 1058.0343 PPI(Pixels per inch)\n"
     ]
    }
   ],
   "source": [
    "arr=model.predict([[5120,2160]])\n",
    "time=arr[0][0].squeeze()\n",
    "qua=arr[0][1].squeeze()\n",
    "print(\"Best trade-off resolution is: \"+str(min_i)+\"x\"+str(min_j)+\" that takes time= \"+str(time)+\" seconds and quality= \"+str(qua)+\" PPI(Pixels per inch)\")\n"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}