Assignment-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dQR9tgUyq_By"
   },
   "source": [
    "**Aim-** Use TensorFlow to build our first Sequential model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "lTbEeJvFRQiv"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h2JON-_Oy79w"
   },
   "source": [
    "## Generate Dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 770
    },
    "id": "hgpG3WDuypfa",
    "outputId": "8e72ef08-2183-4d5d-f0c5-0461dcbc01c6"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.772861</td>\n",
       "      <td>-1.379698</td>\n",
       "      <td>-0.527355</td>\n",
       "      <td>1.593241</td>\n",
       "      <td>0.097285</td>\n",
       "      <td>0.624984</td>\n",
       "      <td>-0.778922</td>\n",
       "      <td>1.140979</td>\n",
       "      <td>1.259162</td>\n",
       "      <td>1.557611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.395608</td>\n",
       "      <td>0.849631</td>\n",
       "      <td>-0.941101</td>\n",
       "      <td>-1.346985</td>\n",
       "      <td>-1.603413</td>\n",
       "      <td>2.016347</td>\n",
       "      <td>-0.224818</td>\n",
       "      <td>0.249793</td>\n",
       "      <td>-1.353353</td>\n",
       "      <td>0.466200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.553373</td>\n",
       "      <td>-1.135040</td>\n",
       "      <td>0.073822</td>\n",
       "      <td>0.533735</td>\n",
       "      <td>1.083365</td>\n",
       "      <td>0.340763</td>\n",
       "      <td>0.106149</td>\n",
       "      <td>-0.703702</td>\n",
       "      <td>0.773991</td>\n",
       "      <td>0.814101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.334539</td>\n",
       "      <td>-0.172720</td>\n",
       "      <td>-0.276809</td>\n",
       "      <td>0.327939</td>\n",
       "      <td>0.898349</td>\n",
       "      <td>0.242673</td>\n",
       "      <td>1.133843</td>\n",
       "      <td>-0.386229</td>\n",
       "      <td>-0.019636</td>\n",
       "      <td>0.422848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.135120</td>\n",
       "      <td>-0.527784</td>\n",
       "      <td>0.738520</td>\n",
       "      <td>0.709233</td>\n",
       "      <td>-0.682314</td>\n",
       "      <td>-0.399219</td>\n",
       "      <td>0.015748</td>\n",
       "      <td>1.321061</td>\n",
       "      <td>-0.153580</td>\n",
       "      <td>0.768587</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.170377</td>\n",
       "      <td>0.075524</td>\n",
       "      <td>-0.240938</td>\n",
       "      <td>-1.161567</td>\n",
       "      <td>-0.759352</td>\n",
       "      <td>0.352380</td>\n",
       "      <td>-0.276121</td>\n",
       "      <td>-0.704457</td>\n",
       "      <td>-2.425453</td>\n",
       "      <td>-1.197688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>-0.606887</td>\n",
       "      <td>-0.087987</td>\n",
       "      <td>0.723342</td>\n",
       "      <td>1.546979</td>\n",
       "      <td>0.425072</td>\n",
       "      <td>-1.156816</td>\n",
       "      <td>0.046136</td>\n",
       "      <td>0.618670</td>\n",
       "      <td>0.332253</td>\n",
       "      <td>0.350997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.993684</td>\n",
       "      <td>-1.730338</td>\n",
       "      <td>0.065035</td>\n",
       "      <td>-1.082626</td>\n",
       "      <td>-0.207354</td>\n",
       "      <td>-0.212866</td>\n",
       "      <td>0.590163</td>\n",
       "      <td>0.501810</td>\n",
       "      <td>1.109175</td>\n",
       "      <td>-0.422517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>-0.079979</td>\n",
       "      <td>-1.104575</td>\n",
       "      <td>1.401277</td>\n",
       "      <td>0.649790</td>\n",
       "      <td>-0.291096</td>\n",
       "      <td>0.038635</td>\n",
       "      <td>1.555938</td>\n",
       "      <td>-0.852951</td>\n",
       "      <td>-0.890976</td>\n",
       "      <td>-0.480849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.030067</td>\n",
       "      <td>0.650753</td>\n",
       "      <td>0.081264</td>\n",
       "      <td>-0.336180</td>\n",
       "      <td>0.614741</td>\n",
       "      <td>1.578874</td>\n",
       "      <td>-0.240524</td>\n",
       "      <td>0.738247</td>\n",
       "      <td>-0.126569</td>\n",
       "      <td>-0.207182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0   0.772861 -1.379698 -0.527355  1.593241  0.097285  0.624984 -0.778922   \n",
       "1   0.395608  0.849631 -0.941101 -1.346985 -1.603413  2.016347 -0.224818   \n",
       "2  -0.553373 -1.135040  0.073822  0.533735  1.083365  0.340763  0.106149   \n",
       "3  -0.334539 -0.172720 -0.276809  0.327939  0.898349  0.242673  1.133843   \n",
       "4   1.135120 -0.527784  0.738520  0.709233 -0.682314 -0.399219  0.015748   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "75  0.170377  0.075524 -0.240938 -1.161567 -0.759352  0.352380 -0.276121   \n",
       "76 -0.606887 -0.087987  0.723342  1.546979  0.425072 -1.156816  0.046136   \n",
       "77  0.993684 -1.730338  0.065035 -1.082626 -0.207354 -0.212866  0.590163   \n",
       "78 -0.079979 -1.104575  1.401277  0.649790 -0.291096  0.038635  1.555938   \n",
       "79  0.030067  0.650753  0.081264 -0.336180  0.614741  1.578874 -0.240524   \n",
       "\n",
       "           7         8         9  \n",
       "0   1.140979  1.259162  1.557611  \n",
       "1   0.249793 -1.353353  0.466200  \n",
       "2  -0.703702  0.773991  0.814101  \n",
       "3  -0.386229 -0.019636  0.422848  \n",
       "4   1.321061 -0.153580  0.768587  \n",
       "..       ...       ...       ...  \n",
       "75 -0.704457 -2.425453 -1.197688  \n",
       "76  0.618670  0.332253  0.350997  \n",
       "77  0.501810  1.109175 -0.422517  \n",
       "78 -0.852951 -0.890976 -0.480849  \n",
       "79  0.738247 -0.126569 -0.207182  \n",
       "\n",
       "[80 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Generate the data\n",
    "x, y = datasets.make_regression(n_samples=100, n_features=10, noise=5, random_state=4)\n",
    "\n",
    "# Split the data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1234)\n",
    "dataset = pd.DataFrame(x)\n",
    "train_dataset = pd.DataFrame(x_train)\n",
    "train_dataset['Output']=y_train\n",
    "train_dataset_labels = train_dataset.pop('Output')\n",
    "test_dataset = pd.DataFrame(x_test)\n",
    "test_dataset['Output'] = y_test\n",
    "test_dataset_labels = test_dataset.pop('Output')\n",
    "dataset['Output'] = y\n",
    "train_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r6it-Rm7zD1Y"
   },
   "source": [
    "## Visualize Dataset\n",
    "This is the same code from Assignment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "UautPVj1yzaQ",
    "outputId": "eb5ca101-c828-4f04-a26c-fe5f1be19178",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# print(dataset[1])\n",
    "# print(x.shape,y.shape)\n",
    "for i in range(x.shape[1]):\n",
    "  plt.xlabel(i)\n",
    "  plt.ylabel('Output')\n",
    "  # plt.plot(dataset[i],dataset['Output'])\n",
    "  plt.scatter(dataset[i],dataset['Output'])\n",
    "  plt.show()\n",
    "# Your code here\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(dataset[[0,1,2,3,4,5,6,7,8,9,'Output']], diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rAaMRXyrrPBS"
   },
   "source": [
    "[Note: Use this Link for help](https://www.tensorflow.org/tutorials/keras/regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XMXb9lTyzGHE"
   },
   "source": [
    "## Model Definition\n",
    "\n",
    "Using TensorFlow, build a model with the following definition:\n",
    "> Input of shape 10 \\\\\n",
    "> Dense of shape 50 \\\\\n",
    "> Dense of shape 10 \\\\\n",
    "Dense of shape 5 \\\\\n",
    "> Dense of shape 1 \\\\\n",
    "\n",
    "Use Mean Square Error Loss and Stochaistic Gradient Descent (SGD) Optimizer\n",
    "\n",
    "Use Gradient Decay with appropriate parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "r32N1xK2ziOs"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "#normaliser = tf.keras.layers.Normalization(axis=-1)\n",
    "normaliser = tf.keras.layers.Normalization(axis=-1,mean=0,variance=1)\n",
    "#normaliser.adapt(np.array(train_dataset))\n",
    "\n",
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First example: [[ 0.77 -1.38 -0.53  1.59  0.1   0.62 -0.78  1.14  1.26  1.56]]\n",
      "\n",
      "Normalized: [[ 0.77 -1.38 -0.53  1.59  0.1   0.62 -0.78  1.14  1.26  1.56]]\n"
     ]
    }
   ],
   "source": [
    "first = np.array(train_dataset[:1]) #[columns:rows]\n",
    "\n",
    "with np.printoptions(precision=2, suppress=True):\n",
    "  print('First example:', first)\n",
    "  print()\n",
    "  print('Normalized:', normaliser(first).numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization (Normalizatio  (None, 10)               0         \n",
      " n)                                                              \n",
      "                                                                 \n",
      " dense (Dense)               (None, 50)                550       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                510       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 5)                 55        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,121\n",
      "Trainable params: 1,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([normaliser,\n",
    "                           tf.keras.layers.Dense(units=50,activation='relu'),\n",
    "                           tf.keras.layers.Dense(units=10,activation='relu'),\n",
    "                             tf.keras.layers.Dense(units=5,activation='relu'),\n",
    "                            tf.keras.layers.Dense(units=1)])\n",
    "model.build(input_shape=(None, 10))\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),loss='mean_absolute_error')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "2/2 - 1s - loss: 125.7132 - val_loss: 133.6748 - 823ms/epoch - 412ms/step\n",
      "Epoch 2/500\n",
      "2/2 - 0s - loss: 125.7077 - val_loss: 133.6748 - 41ms/epoch - 21ms/step\n",
      "Epoch 3/500\n",
      "2/2 - 0s - loss: 125.6991 - val_loss: 133.6748 - 41ms/epoch - 21ms/step\n",
      "Epoch 4/500\n",
      "2/2 - 0s - loss: 125.6923 - val_loss: 133.6745 - 47ms/epoch - 24ms/step\n",
      "Epoch 5/500\n",
      "2/2 - 0s - loss: 125.6856 - val_loss: 133.6742 - 42ms/epoch - 21ms/step\n",
      "Epoch 6/500\n",
      "2/2 - 0s - loss: 125.6788 - val_loss: 133.6738 - 37ms/epoch - 19ms/step\n",
      "Epoch 7/500\n",
      "2/2 - 0s - loss: 125.6717 - val_loss: 133.6733 - 42ms/epoch - 21ms/step\n",
      "Epoch 8/500\n",
      "2/2 - 0s - loss: 125.6653 - val_loss: 133.6729 - 37ms/epoch - 19ms/step\n",
      "Epoch 9/500\n",
      "2/2 - 0s - loss: 125.6588 - val_loss: 133.6727 - 42ms/epoch - 21ms/step\n",
      "Epoch 10/500\n",
      "2/2 - 0s - loss: 125.6523 - val_loss: 133.6727 - 41ms/epoch - 21ms/step\n",
      "Epoch 11/500\n",
      "2/2 - 0s - loss: 125.6468 - val_loss: 133.6728 - 50ms/epoch - 25ms/step\n",
      "Epoch 12/500\n",
      "2/2 - 0s - loss: 125.6411 - val_loss: 133.6730 - 41ms/epoch - 21ms/step\n",
      "Epoch 13/500\n",
      "2/2 - 0s - loss: 125.6362 - val_loss: 133.6732 - 42ms/epoch - 21ms/step\n",
      "Epoch 14/500\n",
      "2/2 - 0s - loss: 125.6305 - val_loss: 133.6734 - 46ms/epoch - 23ms/step\n",
      "Epoch 15/500\n",
      "2/2 - 0s - loss: 125.6250 - val_loss: 133.6737 - 49ms/epoch - 25ms/step\n",
      "Epoch 16/500\n",
      "2/2 - 0s - loss: 125.6203 - val_loss: 133.6742 - 43ms/epoch - 22ms/step\n",
      "Epoch 17/500\n",
      "2/2 - 0s - loss: 125.6146 - val_loss: 133.6746 - 39ms/epoch - 20ms/step\n",
      "Epoch 18/500\n",
      "2/2 - 0s - loss: 125.6088 - val_loss: 133.6749 - 38ms/epoch - 19ms/step\n",
      "Epoch 19/500\n",
      "2/2 - 0s - loss: 125.6032 - val_loss: 133.6752 - 38ms/epoch - 19ms/step\n",
      "Epoch 20/500\n",
      "2/2 - 0s - loss: 125.5976 - val_loss: 133.6754 - 42ms/epoch - 21ms/step\n",
      "Epoch 21/500\n",
      "2/2 - 0s - loss: 125.5923 - val_loss: 133.6758 - 44ms/epoch - 22ms/step\n",
      "Epoch 22/500\n",
      "2/2 - 0s - loss: 125.5867 - val_loss: 133.6762 - 42ms/epoch - 21ms/step\n",
      "Epoch 23/500\n",
      "2/2 - 0s - loss: 125.5802 - val_loss: 133.6765 - 36ms/epoch - 18ms/step\n",
      "Epoch 24/500\n",
      "2/2 - 0s - loss: 125.5750 - val_loss: 133.6767 - 39ms/epoch - 20ms/step\n",
      "Epoch 25/500\n",
      "2/2 - 0s - loss: 125.5695 - val_loss: 133.6769 - 41ms/epoch - 21ms/step\n",
      "Epoch 26/500\n",
      "2/2 - 0s - loss: 125.5643 - val_loss: 133.6769 - 35ms/epoch - 18ms/step\n",
      "Epoch 27/500\n",
      "2/2 - 0s - loss: 125.5600 - val_loss: 133.6770 - 42ms/epoch - 21ms/step\n",
      "Epoch 28/500\n",
      "2/2 - 0s - loss: 125.5538 - val_loss: 133.6772 - 39ms/epoch - 20ms/step\n",
      "Epoch 29/500\n",
      "2/2 - 0s - loss: 125.5475 - val_loss: 133.6776 - 42ms/epoch - 21ms/step\n",
      "Epoch 30/500\n",
      "2/2 - 0s - loss: 125.5418 - val_loss: 133.6778 - 42ms/epoch - 21ms/step\n",
      "Epoch 31/500\n",
      "2/2 - 0s - loss: 125.5358 - val_loss: 133.6781 - 37ms/epoch - 19ms/step\n",
      "Epoch 32/500\n",
      "2/2 - 0s - loss: 125.5289 - val_loss: 133.6784 - 38ms/epoch - 19ms/step\n",
      "Epoch 33/500\n",
      "2/2 - 0s - loss: 125.5221 - val_loss: 133.6786 - 38ms/epoch - 19ms/step\n",
      "Epoch 34/500\n",
      "2/2 - 0s - loss: 125.5163 - val_loss: 133.6787 - 40ms/epoch - 20ms/step\n",
      "Epoch 35/500\n",
      "2/2 - 0s - loss: 125.5078 - val_loss: 133.6787 - 44ms/epoch - 22ms/step\n",
      "Epoch 36/500\n",
      "2/2 - 0s - loss: 125.5004 - val_loss: 133.6787 - 48ms/epoch - 24ms/step\n",
      "Epoch 37/500\n",
      "2/2 - 0s - loss: 125.4925 - val_loss: 133.6789 - 39ms/epoch - 20ms/step\n",
      "Epoch 38/500\n",
      "2/2 - 0s - loss: 125.4843 - val_loss: 133.6792 - 39ms/epoch - 20ms/step\n",
      "Epoch 39/500\n",
      "2/2 - 0s - loss: 125.4761 - val_loss: 133.6795 - 38ms/epoch - 19ms/step\n",
      "Epoch 40/500\n",
      "2/2 - 0s - loss: 125.4682 - val_loss: 133.6798 - 40ms/epoch - 20ms/step\n",
      "Epoch 41/500\n",
      "2/2 - 0s - loss: 125.4584 - val_loss: 133.6801 - 35ms/epoch - 18ms/step\n",
      "Epoch 42/500\n",
      "2/2 - 0s - loss: 125.4491 - val_loss: 133.6804 - 44ms/epoch - 22ms/step\n",
      "Epoch 43/500\n",
      "2/2 - 0s - loss: 125.4396 - val_loss: 133.6805 - 40ms/epoch - 20ms/step\n",
      "Epoch 44/500\n",
      "2/2 - 0s - loss: 125.4295 - val_loss: 133.6802 - 43ms/epoch - 22ms/step\n",
      "Epoch 45/500\n",
      "2/2 - 0s - loss: 125.4187 - val_loss: 133.6800 - 46ms/epoch - 23ms/step\n",
      "Epoch 46/500\n",
      "2/2 - 0s - loss: 125.4077 - val_loss: 133.6793 - 47ms/epoch - 24ms/step\n",
      "Epoch 47/500\n",
      "2/2 - 0s - loss: 125.3959 - val_loss: 133.6785 - 44ms/epoch - 22ms/step\n",
      "Epoch 48/500\n",
      "2/2 - 0s - loss: 125.3832 - val_loss: 133.6778 - 42ms/epoch - 21ms/step\n",
      "Epoch 49/500\n",
      "2/2 - 0s - loss: 125.3703 - val_loss: 133.6771 - 45ms/epoch - 23ms/step\n",
      "Epoch 50/500\n",
      "2/2 - 0s - loss: 125.3562 - val_loss: 133.6761 - 48ms/epoch - 24ms/step\n",
      "Epoch 51/500\n",
      "2/2 - 0s - loss: 125.3424 - val_loss: 133.6750 - 44ms/epoch - 22ms/step\n",
      "Epoch 52/500\n",
      "2/2 - 0s - loss: 125.3271 - val_loss: 133.6736 - 43ms/epoch - 22ms/step\n",
      "Epoch 53/500\n",
      "2/2 - 0s - loss: 125.3113 - val_loss: 133.6721 - 40ms/epoch - 20ms/step\n",
      "Epoch 54/500\n",
      "2/2 - 0s - loss: 125.2945 - val_loss: 133.6703 - 41ms/epoch - 21ms/step\n",
      "Epoch 55/500\n",
      "2/2 - 0s - loss: 125.2776 - val_loss: 133.6683 - 40ms/epoch - 20ms/step\n",
      "Epoch 56/500\n",
      "2/2 - 0s - loss: 125.2596 - val_loss: 133.6659 - 43ms/epoch - 22ms/step\n",
      "Epoch 57/500\n",
      "2/2 - 0s - loss: 125.2402 - val_loss: 133.6633 - 43ms/epoch - 22ms/step\n",
      "Epoch 58/500\n",
      "2/2 - 0s - loss: 125.2211 - val_loss: 133.6605 - 41ms/epoch - 21ms/step\n",
      "Epoch 59/500\n",
      "2/2 - 0s - loss: 125.1998 - val_loss: 133.6576 - 44ms/epoch - 22ms/step\n",
      "Epoch 60/500\n",
      "2/2 - 0s - loss: 125.1783 - val_loss: 133.6543 - 42ms/epoch - 21ms/step\n",
      "Epoch 61/500\n",
      "2/2 - 0s - loss: 125.1549 - val_loss: 133.6507 - 39ms/epoch - 20ms/step\n",
      "Epoch 62/500\n",
      "2/2 - 0s - loss: 125.1292 - val_loss: 133.6469 - 38ms/epoch - 19ms/step\n",
      "Epoch 63/500\n",
      "2/2 - 0s - loss: 125.1023 - val_loss: 133.6426 - 43ms/epoch - 22ms/step\n",
      "Epoch 64/500\n",
      "2/2 - 0s - loss: 125.0740 - val_loss: 133.6377 - 38ms/epoch - 19ms/step\n",
      "Epoch 65/500\n",
      "2/2 - 0s - loss: 125.0428 - val_loss: 133.6318 - 38ms/epoch - 19ms/step\n",
      "Epoch 66/500\n",
      "2/2 - 0s - loss: 125.0114 - val_loss: 133.6256 - 41ms/epoch - 21ms/step\n",
      "Epoch 67/500\n",
      "2/2 - 0s - loss: 124.9765 - val_loss: 133.6183 - 76ms/epoch - 38ms/step\n",
      "Epoch 68/500\n",
      "2/2 - 0s - loss: 124.9394 - val_loss: 133.6106 - 55ms/epoch - 28ms/step\n",
      "Epoch 69/500\n",
      "2/2 - 0s - loss: 124.8993 - val_loss: 133.6020 - 52ms/epoch - 26ms/step\n",
      "Epoch 70/500\n",
      "2/2 - 0s - loss: 124.8573 - val_loss: 133.5921 - 44ms/epoch - 22ms/step\n",
      "Epoch 71/500\n",
      "2/2 - 0s - loss: 124.8079 - val_loss: 133.5810 - 40ms/epoch - 20ms/step\n",
      "Epoch 72/500\n",
      "2/2 - 0s - loss: 124.7579 - val_loss: 133.5686 - 45ms/epoch - 23ms/step\n",
      "Epoch 73/500\n",
      "2/2 - 0s - loss: 124.7044 - val_loss: 133.5546 - 39ms/epoch - 20ms/step\n",
      "Epoch 74/500\n",
      "2/2 - 0s - loss: 124.6443 - val_loss: 133.5388 - 41ms/epoch - 21ms/step\n",
      "Epoch 75/500\n",
      "2/2 - 0s - loss: 124.5827 - val_loss: 133.5212 - 53ms/epoch - 27ms/step\n",
      "Epoch 76/500\n",
      "2/2 - 0s - loss: 124.5121 - val_loss: 133.5016 - 40ms/epoch - 20ms/step\n",
      "Epoch 77/500\n",
      "2/2 - 0s - loss: 124.4334 - val_loss: 133.4796 - 40ms/epoch - 20ms/step\n",
      "Epoch 78/500\n",
      "2/2 - 0s - loss: 124.3485 - val_loss: 133.4548 - 41ms/epoch - 20ms/step\n",
      "Epoch 79/500\n",
      "2/2 - 0s - loss: 124.2577 - val_loss: 133.4279 - 42ms/epoch - 21ms/step\n",
      "Epoch 80/500\n",
      "2/2 - 0s - loss: 124.1551 - val_loss: 133.3975 - 45ms/epoch - 22ms/step\n",
      "Epoch 81/500\n",
      "2/2 - 0s - loss: 124.0369 - val_loss: 133.3625 - 41ms/epoch - 21ms/step\n",
      "Epoch 82/500\n",
      "2/2 - 0s - loss: 123.9066 - val_loss: 133.3224 - 53ms/epoch - 27ms/step\n",
      "Epoch 83/500\n",
      "2/2 - 0s - loss: 123.7622 - val_loss: 133.2769 - 56ms/epoch - 28ms/step\n",
      "Epoch 84/500\n",
      "2/2 - 0s - loss: 123.6012 - val_loss: 133.2240 - 54ms/epoch - 27ms/step\n",
      "Epoch 85/500\n",
      "2/2 - 0s - loss: 123.4170 - val_loss: 133.1628 - 47ms/epoch - 24ms/step\n",
      "Epoch 86/500\n",
      "2/2 - 0s - loss: 123.2064 - val_loss: 133.0916 - 41ms/epoch - 21ms/step\n",
      "Epoch 87/500\n",
      "2/2 - 0s - loss: 122.9704 - val_loss: 133.0092 - 39ms/epoch - 19ms/step\n",
      "Epoch 88/500\n",
      "2/2 - 0s - loss: 122.6965 - val_loss: 132.9127 - 43ms/epoch - 22ms/step\n",
      "Epoch 89/500\n",
      "2/2 - 0s - loss: 122.3827 - val_loss: 132.7982 - 39ms/epoch - 19ms/step\n",
      "Epoch 90/500\n",
      "2/2 - 0s - loss: 122.0044 - val_loss: 132.6626 - 44ms/epoch - 22ms/step\n",
      "Epoch 91/500\n",
      "2/2 - 0s - loss: 121.5844 - val_loss: 132.4991 - 40ms/epoch - 20ms/step\n",
      "Epoch 92/500\n",
      "2/2 - 0s - loss: 121.0717 - val_loss: 132.2977 - 41ms/epoch - 21ms/step\n",
      "Epoch 93/500\n",
      "2/2 - 0s - loss: 120.4172 - val_loss: 132.0529 - 40ms/epoch - 20ms/step\n",
      "Epoch 94/500\n",
      "2/2 - 0s - loss: 119.6393 - val_loss: 131.7398 - 38ms/epoch - 19ms/step\n",
      "Epoch 95/500\n",
      "2/2 - 0s - loss: 118.6796 - val_loss: 131.3413 - 41ms/epoch - 21ms/step\n",
      "Epoch 96/500\n",
      "2/2 - 0s - loss: 117.4859 - val_loss: 130.8223 - 38ms/epoch - 19ms/step\n",
      "Epoch 97/500\n",
      "2/2 - 0s - loss: 115.9530 - val_loss: 130.1276 - 36ms/epoch - 18ms/step\n",
      "Epoch 98/500\n",
      "2/2 - 0s - loss: 114.1411 - val_loss: 129.2599 - 39ms/epoch - 20ms/step\n",
      "Epoch 99/500\n",
      "2/2 - 0s - loss: 111.9856 - val_loss: 128.1140 - 38ms/epoch - 19ms/step\n",
      "Epoch 100/500\n",
      "2/2 - 0s - loss: 109.2903 - val_loss: 126.6745 - 43ms/epoch - 22ms/step\n",
      "Epoch 101/500\n",
      "2/2 - 0s - loss: 105.9603 - val_loss: 124.6237 - 42ms/epoch - 21ms/step\n",
      "Epoch 102/500\n",
      "2/2 - 0s - loss: 101.4019 - val_loss: 121.5545 - 40ms/epoch - 20ms/step\n",
      "Epoch 103/500\n",
      "2/2 - 0s - loss: 95.2048 - val_loss: 117.4542 - 45ms/epoch - 23ms/step\n",
      "Epoch 104/500\n",
      "2/2 - 0s - loss: 88.6440 - val_loss: 112.3020 - 41ms/epoch - 21ms/step\n",
      "Epoch 105/500\n",
      "2/2 - 0s - loss: 80.5036 - val_loss: 107.2068 - 38ms/epoch - 19ms/step\n",
      "Epoch 106/500\n",
      "2/2 - 0s - loss: 73.6113 - val_loss: 105.0630 - 40ms/epoch - 20ms/step\n",
      "Epoch 107/500\n",
      "2/2 - 0s - loss: 68.3612 - val_loss: 102.4027 - 39ms/epoch - 20ms/step\n",
      "Epoch 108/500\n",
      "2/2 - 0s - loss: 65.2118 - val_loss: 102.1152 - 42ms/epoch - 21ms/step\n",
      "Epoch 109/500\n",
      "2/2 - 0s - loss: 60.1301 - val_loss: 98.8187 - 44ms/epoch - 22ms/step\n",
      "Epoch 110/500\n",
      "2/2 - 0s - loss: 55.5537 - val_loss: 94.7212 - 39ms/epoch - 20ms/step\n",
      "Epoch 111/500\n",
      "2/2 - 0s - loss: 52.3186 - val_loss: 92.2021 - 41ms/epoch - 21ms/step\n",
      "Epoch 112/500\n",
      "2/2 - 0s - loss: 52.2241 - val_loss: 89.3195 - 40ms/epoch - 20ms/step\n",
      "Epoch 113/500\n",
      "2/2 - 0s - loss: 47.2017 - val_loss: 86.0364 - 40ms/epoch - 20ms/step\n",
      "Epoch 114/500\n",
      "2/2 - 0s - loss: 50.7465 - val_loss: 88.9181 - 38ms/epoch - 19ms/step\n",
      "Epoch 115/500\n",
      "2/2 - 0s - loss: 49.7984 - val_loss: 81.5742 - 41ms/epoch - 21ms/step\n",
      "Epoch 116/500\n",
      "2/2 - 0s - loss: 51.9648 - val_loss: 81.0042 - 38ms/epoch - 19ms/step\n",
      "Epoch 117/500\n",
      "2/2 - 0s - loss: 54.2526 - val_loss: 79.1334 - 48ms/epoch - 24ms/step\n",
      "Epoch 118/500\n",
      "2/2 - 0s - loss: 44.6554 - val_loss: 83.8163 - 42ms/epoch - 21ms/step\n",
      "Epoch 119/500\n",
      "2/2 - 0s - loss: 44.9872 - val_loss: 91.7115 - 45ms/epoch - 23ms/step\n",
      "Epoch 120/500\n",
      "2/2 - 0s - loss: 53.6737 - val_loss: 82.4486 - 37ms/epoch - 19ms/step\n",
      "Epoch 121/500\n",
      "2/2 - 0s - loss: 55.6040 - val_loss: 85.9519 - 40ms/epoch - 20ms/step\n",
      "Epoch 122/500\n",
      "2/2 - 0s - loss: 52.1281 - val_loss: 82.6283 - 40ms/epoch - 20ms/step\n",
      "Epoch 123/500\n",
      "2/2 - 0s - loss: 52.4733 - val_loss: 84.8827 - 41ms/epoch - 20ms/step\n",
      "Epoch 124/500\n",
      "2/2 - 0s - loss: 51.7219 - val_loss: 79.2256 - 40ms/epoch - 20ms/step\n",
      "Epoch 125/500\n",
      "2/2 - 0s - loss: 48.0865 - val_loss: 87.4574 - 39ms/epoch - 20ms/step\n",
      "Epoch 126/500\n",
      "2/2 - 0s - loss: 52.1833 - val_loss: 85.7656 - 42ms/epoch - 21ms/step\n",
      "Epoch 127/500\n",
      "2/2 - 0s - loss: 53.2271 - val_loss: 88.3269 - 42ms/epoch - 21ms/step\n",
      "Epoch 128/500\n",
      "2/2 - 0s - loss: 51.0740 - val_loss: 82.5899 - 37ms/epoch - 19ms/step\n",
      "Epoch 129/500\n",
      "2/2 - 0s - loss: 46.1198 - val_loss: 84.3861 - 46ms/epoch - 23ms/step\n",
      "Epoch 130/500\n",
      "2/2 - 0s - loss: 51.2446 - val_loss: 88.1685 - 39ms/epoch - 20ms/step\n",
      "Epoch 131/500\n",
      "2/2 - 0s - loss: 50.5971 - val_loss: 79.9451 - 43ms/epoch - 22ms/step\n",
      "Epoch 132/500\n",
      "2/2 - 0s - loss: 44.4154 - val_loss: 90.1153 - 43ms/epoch - 22ms/step\n",
      "Epoch 133/500\n",
      "2/2 - 0s - loss: 52.1812 - val_loss: 82.6086 - 40ms/epoch - 20ms/step\n",
      "Epoch 134/500\n",
      "2/2 - 0s - loss: 53.0672 - val_loss: 78.5742 - 40ms/epoch - 20ms/step\n",
      "Epoch 135/500\n",
      "2/2 - 0s - loss: 45.9893 - val_loss: 84.7998 - 37ms/epoch - 19ms/step\n",
      "Epoch 136/500\n",
      "2/2 - 0s - loss: 47.9280 - val_loss: 88.3670 - 40ms/epoch - 20ms/step\n",
      "Epoch 137/500\n",
      "2/2 - 0s - loss: 52.8333 - val_loss: 85.1054 - 36ms/epoch - 18ms/step\n",
      "Epoch 138/500\n",
      "2/2 - 0s - loss: 47.5736 - val_loss: 90.0722 - 43ms/epoch - 22ms/step\n",
      "Epoch 139/500\n",
      "2/2 - 0s - loss: 50.4731 - val_loss: 78.2347 - 48ms/epoch - 24ms/step\n",
      "Epoch 140/500\n",
      "2/2 - 0s - loss: 48.8768 - val_loss: 86.3460 - 42ms/epoch - 21ms/step\n",
      "Epoch 141/500\n",
      "2/2 - 0s - loss: 49.9912 - val_loss: 85.6237 - 40ms/epoch - 20ms/step\n",
      "Epoch 142/500\n",
      "2/2 - 0s - loss: 53.2377 - val_loss: 79.2649 - 37ms/epoch - 19ms/step\n",
      "Epoch 143/500\n",
      "2/2 - 0s - loss: 46.5571 - val_loss: 90.8833 - 38ms/epoch - 19ms/step\n",
      "Epoch 144/500\n",
      "2/2 - 0s - loss: 52.6502 - val_loss: 81.1725 - 44ms/epoch - 22ms/step\n",
      "Epoch 145/500\n",
      "2/2 - 0s - loss: 48.7749 - val_loss: 79.2731 - 53ms/epoch - 27ms/step\n",
      "Epoch 146/500\n",
      "2/2 - 0s - loss: 50.4204 - val_loss: 81.7830 - 43ms/epoch - 22ms/step\n",
      "Epoch 147/500\n",
      "2/2 - 0s - loss: 51.9758 - val_loss: 81.4642 - 49ms/epoch - 25ms/step\n",
      "Epoch 148/500\n",
      "2/2 - 0s - loss: 47.3905 - val_loss: 80.9016 - 42ms/epoch - 21ms/step\n",
      "Epoch 149/500\n",
      "2/2 - 0s - loss: 51.3584 - val_loss: 79.2871 - 45ms/epoch - 23ms/step\n",
      "Epoch 150/500\n",
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      "Epoch 151/500\n",
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      "Epoch 152/500\n",
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      "Epoch 153/500\n",
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      "Epoch 154/500\n",
      "2/2 - 0s - loss: 51.9818 - val_loss: 78.5133 - 39ms/epoch - 20ms/step\n",
      "Epoch 155/500\n",
      "2/2 - 0s - loss: 41.6592 - val_loss: 89.6516 - 39ms/epoch - 20ms/step\n",
      "Epoch 156/500\n",
      "2/2 - 0s - loss: 53.0134 - val_loss: 85.4231 - 43ms/epoch - 22ms/step\n",
      "Epoch 157/500\n",
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      "Epoch 158/500\n",
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      "Epoch 159/500\n",
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      "Epoch 160/500\n",
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      "Epoch 161/500\n",
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      "Epoch 162/500\n",
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      "Epoch 163/500\n",
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      "Epoch 164/500\n",
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      "Epoch 165/500\n",
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      "Epoch 166/500\n",
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      "Epoch 167/500\n",
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      "Epoch 168/500\n",
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      "Epoch 169/500\n",
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      "Epoch 170/500\n",
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      "Epoch 171/500\n",
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      "Epoch 172/500\n",
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      "Epoch 173/500\n",
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      "Epoch 174/500\n",
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      "Epoch 175/500\n",
      "2/2 - 0s - loss: 46.0232 - val_loss: 83.8831 - 36ms/epoch - 18ms/step\n",
      "Epoch 176/500\n",
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      "Epoch 177/500\n",
      "2/2 - 0s - loss: 53.7630 - val_loss: 74.2196 - 42ms/epoch - 21ms/step\n",
      "Epoch 178/500\n",
      "2/2 - 0s - loss: 44.6134 - val_loss: 85.3161 - 50ms/epoch - 25ms/step\n",
      "Epoch 179/500\n",
      "2/2 - 0s - loss: 51.7740 - val_loss: 70.8602 - 47ms/epoch - 24ms/step\n",
      "Epoch 180/500\n",
      "2/2 - 0s - loss: 36.4463 - val_loss: 73.3793 - 46ms/epoch - 23ms/step\n",
      "Epoch 181/500\n",
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      "Epoch 182/500\n",
      "2/2 - 0s - loss: 44.4510 - val_loss: 74.9345 - 40ms/epoch - 20ms/step\n",
      "Epoch 183/500\n",
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      "Epoch 184/500\n",
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      "Epoch 185/500\n",
      "2/2 - 0s - loss: 41.6936 - val_loss: 73.1018 - 50ms/epoch - 25ms/step\n",
      "Epoch 186/500\n",
      "2/2 - 0s - loss: 43.1315 - val_loss: 66.1748 - 43ms/epoch - 22ms/step\n",
      "Epoch 187/500\n",
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      "Epoch 188/500\n",
      "2/2 - 0s - loss: 39.2434 - val_loss: 67.5385 - 36ms/epoch - 18ms/step\n",
      "Epoch 189/500\n",
      "2/2 - 0s - loss: 39.5546 - val_loss: 68.7423 - 45ms/epoch - 22ms/step\n",
      "Epoch 190/500\n",
      "2/2 - 0s - loss: 37.6313 - val_loss: 60.9657 - 40ms/epoch - 20ms/step\n",
      "Epoch 191/500\n",
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      "Epoch 192/500\n",
      "2/2 - 0s - loss: 43.5636 - val_loss: 51.7971 - 39ms/epoch - 20ms/step\n",
      "Epoch 193/500\n",
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      "Epoch 194/500\n",
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      "Epoch 195/500\n",
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      "Epoch 196/500\n",
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      "Epoch 197/500\n",
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      "Epoch 198/500\n",
      "2/2 - 0s - loss: 30.2600 - val_loss: 46.6557 - 41ms/epoch - 21ms/step\n",
      "Epoch 199/500\n",
      "2/2 - 0s - loss: 26.7483 - val_loss: 38.4721 - 39ms/epoch - 20ms/step\n",
      "Epoch 200/500\n",
      "2/2 - 0s - loss: 28.8611 - val_loss: 40.3315 - 47ms/epoch - 24ms/step\n",
      "Epoch 201/500\n",
      "2/2 - 0s - loss: 25.9565 - val_loss: 34.2807 - 43ms/epoch - 22ms/step\n",
      "Epoch 202/500\n",
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      "Epoch 203/500\n",
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      "Epoch 204/500\n",
      "2/2 - 0s - loss: 23.6546 - val_loss: 32.3315 - 41ms/epoch - 21ms/step\n",
      "Epoch 205/500\n",
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      "Epoch 206/500\n",
      "2/2 - 0s - loss: 23.4235 - val_loss: 28.6476 - 44ms/epoch - 22ms/step\n",
      "Epoch 207/500\n",
      "2/2 - 0s - loss: 16.2047 - val_loss: 26.2529 - 40ms/epoch - 20ms/step\n",
      "Epoch 208/500\n",
      "2/2 - 0s - loss: 17.5296 - val_loss: 29.3227 - 42ms/epoch - 21ms/step\n",
      "Epoch 209/500\n",
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      "Epoch 210/500\n",
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      "Epoch 211/500\n",
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      "Epoch 212/500\n",
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      "Epoch 213/500\n",
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      "Epoch 214/500\n",
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      "Epoch 215/500\n",
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      "Epoch 216/500\n",
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      "Epoch 217/500\n",
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      "Epoch 218/500\n",
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      "Epoch 219/500\n",
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      "Epoch 220/500\n",
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      "Epoch 221/500\n",
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      "Epoch 222/500\n",
      "2/2 - 0s - loss: 10.9933 - val_loss: 28.6370 - 39ms/epoch - 19ms/step\n",
      "Epoch 223/500\n",
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      "Epoch 224/500\n",
      "2/2 - 0s - loss: 16.6861 - val_loss: 27.0710 - 40ms/epoch - 20ms/step\n",
      "Epoch 225/500\n",
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      "Epoch 226/500\n",
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      "Epoch 227/500\n",
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      "Epoch 228/500\n",
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      "Epoch 229/500\n",
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      "Epoch 230/500\n",
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      "Epoch 231/500\n",
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      "Epoch 232/500\n",
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      "Epoch 233/500\n",
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      "Epoch 234/500\n",
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      "Epoch 235/500\n",
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      "Epoch 236/500\n",
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      "Epoch 237/500\n",
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      "Epoch 238/500\n",
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      "Epoch 239/500\n",
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      "Epoch 240/500\n",
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      "Epoch 241/500\n",
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      "Epoch 242/500\n",
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      "Epoch 243/500\n",
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      "Epoch 244/500\n",
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      "Epoch 245/500\n",
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      "Epoch 246/500\n",
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      "Epoch 247/500\n",
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      "Epoch 248/500\n",
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      "Epoch 249/500\n",
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      "Epoch 250/500\n",
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      "Epoch 251/500\n",
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      "Epoch 252/500\n",
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      "Epoch 253/500\n",
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      "Epoch 254/500\n",
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      "Epoch 255/500\n",
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      "Epoch 256/500\n",
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      "Epoch 257/500\n",
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      "Epoch 258/500\n",
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      "Epoch 259/500\n",
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      "Epoch 260/500\n",
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      "Epoch 261/500\n",
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      "Epoch 262/500\n",
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      "Epoch 263/500\n",
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      "Epoch 264/500\n",
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      "Epoch 265/500\n",
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      "Epoch 266/500\n",
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      "Epoch 267/500\n",
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      "Epoch 268/500\n",
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      "Epoch 269/500\n",
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      "Epoch 270/500\n",
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      "Epoch 271/500\n",
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      "Epoch 272/500\n",
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      "Epoch 273/500\n",
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      "Epoch 274/500\n",
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      "Epoch 275/500\n",
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      "Epoch 276/500\n",
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      "Epoch 277/500\n",
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      "Epoch 278/500\n",
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      "Epoch 279/500\n",
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      "Epoch 280/500\n",
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      "Epoch 281/500\n",
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      "Epoch 282/500\n",
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      "Epoch 283/500\n",
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      "Epoch 284/500\n",
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      "Epoch 285/500\n",
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      "Epoch 286/500\n",
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      "Epoch 287/500\n",
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      "Epoch 288/500\n",
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      "Epoch 289/500\n",
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      "Epoch 290/500\n",
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      "Epoch 291/500\n",
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      "Epoch 292/500\n",
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      "Epoch 293/500\n",
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      "Epoch 294/500\n",
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      "Epoch 295/500\n",
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      "Epoch 296/500\n",
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      "Epoch 297/500\n",
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      "Epoch 298/500\n",
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      "Epoch 299/500\n",
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      "Epoch 300/500\n",
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      "Epoch 301/500\n",
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      "Epoch 302/500\n",
      "2/2 - 0s - loss: 18.6843 - val_loss: 19.2528 - 39ms/epoch - 20ms/step\n",
      "Epoch 303/500\n",
      "2/2 - 0s - loss: 15.7998 - val_loss: 28.1591 - 40ms/epoch - 20ms/step\n",
      "Epoch 304/500\n",
      "2/2 - 0s - loss: 24.3860 - val_loss: 19.5102 - 44ms/epoch - 22ms/step\n",
      "Epoch 305/500\n",
      "2/2 - 0s - loss: 14.2837 - val_loss: 23.3066 - 41ms/epoch - 21ms/step\n",
      "Epoch 306/500\n",
      "2/2 - 0s - loss: 14.5336 - val_loss: 17.1374 - 41ms/epoch - 20ms/step\n",
      "Epoch 307/500\n",
      "2/2 - 0s - loss: 13.5842 - val_loss: 22.9012 - 46ms/epoch - 23ms/step\n",
      "Epoch 308/500\n",
      "2/2 - 0s - loss: 12.0721 - val_loss: 30.5198 - 41ms/epoch - 21ms/step\n",
      "Epoch 309/500\n",
      "2/2 - 0s - loss: 12.8694 - val_loss: 25.9794 - 38ms/epoch - 19ms/step\n",
      "Epoch 310/500\n",
      "2/2 - 0s - loss: 14.3820 - val_loss: 22.9129 - 42ms/epoch - 21ms/step\n",
      "Epoch 311/500\n",
      "2/2 - 0s - loss: 14.1530 - val_loss: 23.1577 - 40ms/epoch - 20ms/step\n",
      "Epoch 312/500\n",
      "2/2 - 0s - loss: 17.4821 - val_loss: 21.0125 - 41ms/epoch - 20ms/step\n",
      "Epoch 313/500\n",
      "2/2 - 0s - loss: 10.5886 - val_loss: 28.6621 - 36ms/epoch - 18ms/step\n",
      "Epoch 314/500\n",
      "2/2 - 0s - loss: 14.8514 - val_loss: 21.6219 - 43ms/epoch - 22ms/step\n",
      "Epoch 315/500\n",
      "2/2 - 0s - loss: 16.5899 - val_loss: 15.8029 - 43ms/epoch - 21ms/step\n",
      "Epoch 316/500\n",
      "2/2 - 0s - loss: 10.5241 - val_loss: 33.3591 - 40ms/epoch - 20ms/step\n",
      "Epoch 317/500\n",
      "2/2 - 0s - loss: 24.5375 - val_loss: 14.7109 - 41ms/epoch - 21ms/step\n",
      "Epoch 318/500\n",
      "2/2 - 0s - loss: 17.5569 - val_loss: 15.5790 - 39ms/epoch - 20ms/step\n",
      "Epoch 319/500\n",
      "2/2 - 0s - loss: 11.8755 - val_loss: 26.2195 - 40ms/epoch - 20ms/step\n",
      "Epoch 320/500\n",
      "2/2 - 0s - loss: 17.2908 - val_loss: 30.9997 - 43ms/epoch - 22ms/step\n",
      "Epoch 321/500\n",
      "2/2 - 0s - loss: 16.1692 - val_loss: 11.3514 - 52ms/epoch - 26ms/step\n",
      "Epoch 322/500\n",
      "2/2 - 0s - loss: 14.6386 - val_loss: 12.6436 - 47ms/epoch - 24ms/step\n",
      "Epoch 323/500\n",
      "2/2 - 0s - loss: 11.6224 - val_loss: 18.1120 - 43ms/epoch - 22ms/step\n",
      "Epoch 324/500\n",
      "2/2 - 0s - loss: 18.7126 - val_loss: 13.6623 - 37ms/epoch - 19ms/step\n",
      "Epoch 325/500\n",
      "2/2 - 0s - loss: 11.3026 - val_loss: 23.8485 - 41ms/epoch - 21ms/step\n",
      "Epoch 326/500\n",
      "2/2 - 0s - loss: 20.8784 - val_loss: 18.4529 - 39ms/epoch - 20ms/step\n",
      "Epoch 327/500\n",
      "2/2 - 0s - loss: 16.9962 - val_loss: 17.9745 - 38ms/epoch - 19ms/step\n",
      "Epoch 328/500\n",
      "2/2 - 0s - loss: 13.0098 - val_loss: 16.3905 - 38ms/epoch - 19ms/step\n",
      "Epoch 329/500\n",
      "2/2 - 0s - loss: 16.9722 - val_loss: 15.5190 - 47ms/epoch - 24ms/step\n",
      "Epoch 330/500\n",
      "2/2 - 0s - loss: 13.1329 - val_loss: 17.9754 - 41ms/epoch - 21ms/step\n",
      "Epoch 331/500\n",
      "2/2 - 0s - loss: 14.3220 - val_loss: 17.0660 - 39ms/epoch - 20ms/step\n",
      "Epoch 332/500\n",
      "2/2 - 0s - loss: 15.7500 - val_loss: 18.8324 - 40ms/epoch - 20ms/step\n",
      "Epoch 333/500\n",
      "2/2 - 0s - loss: 17.7528 - val_loss: 16.7226 - 35ms/epoch - 18ms/step\n",
      "Epoch 334/500\n",
      "2/2 - 0s - loss: 15.5956 - val_loss: 21.6350 - 43ms/epoch - 22ms/step\n",
      "Epoch 335/500\n",
      "2/2 - 0s - loss: 11.6066 - val_loss: 15.4308 - 43ms/epoch - 22ms/step\n",
      "Epoch 336/500\n",
      "2/2 - 0s - loss: 17.9601 - val_loss: 24.6153 - 45ms/epoch - 23ms/step\n",
      "Epoch 337/500\n",
      "2/2 - 0s - loss: 13.3011 - val_loss: 19.8217 - 41ms/epoch - 21ms/step\n",
      "Epoch 338/500\n",
      "2/2 - 0s - loss: 17.6137 - val_loss: 20.0994 - 43ms/epoch - 22ms/step\n",
      "Epoch 339/500\n",
      "2/2 - 0s - loss: 10.4575 - val_loss: 19.4587 - 41ms/epoch - 21ms/step\n",
      "Epoch 340/500\n",
      "2/2 - 0s - loss: 17.6781 - val_loss: 18.2234 - 38ms/epoch - 19ms/step\n",
      "Epoch 341/500\n",
      "2/2 - 0s - loss: 15.4132 - val_loss: 18.9002 - 37ms/epoch - 19ms/step\n",
      "Epoch 342/500\n",
      "2/2 - 0s - loss: 7.5451 - val_loss: 21.6468 - 44ms/epoch - 22ms/step\n",
      "Epoch 343/500\n",
      "2/2 - 0s - loss: 16.6398 - val_loss: 21.0964 - 39ms/epoch - 20ms/step\n",
      "Epoch 344/500\n",
      "2/2 - 0s - loss: 13.1623 - val_loss: 22.0571 - 42ms/epoch - 21ms/step\n",
      "Epoch 345/500\n",
      "2/2 - 0s - loss: 13.6167 - val_loss: 22.0525 - 52ms/epoch - 26ms/step\n",
      "Epoch 346/500\n",
      "2/2 - 0s - loss: 15.0709 - val_loss: 26.1158 - 47ms/epoch - 24ms/step\n",
      "Epoch 347/500\n",
      "2/2 - 0s - loss: 15.1038 - val_loss: 25.9754 - 37ms/epoch - 19ms/step\n",
      "Epoch 348/500\n",
      "2/2 - 0s - loss: 13.8847 - val_loss: 24.2452 - 42ms/epoch - 21ms/step\n",
      "Epoch 349/500\n",
      "2/2 - 0s - loss: 13.7258 - val_loss: 33.2189 - 39ms/epoch - 20ms/step\n",
      "Epoch 350/500\n",
      "2/2 - 0s - loss: 16.0914 - val_loss: 16.1989 - 42ms/epoch - 21ms/step\n",
      "Epoch 351/500\n",
      "2/2 - 0s - loss: 17.5566 - val_loss: 21.1979 - 43ms/epoch - 22ms/step\n",
      "Epoch 352/500\n",
      "2/2 - 0s - loss: 15.6042 - val_loss: 21.3190 - 39ms/epoch - 20ms/step\n",
      "Epoch 353/500\n",
      "2/2 - 0s - loss: 18.0531 - val_loss: 21.1829 - 42ms/epoch - 21ms/step\n",
      "Epoch 354/500\n",
      "2/2 - 0s - loss: 17.2686 - val_loss: 23.1956 - 37ms/epoch - 19ms/step\n",
      "Epoch 355/500\n",
      "2/2 - 0s - loss: 15.5398 - val_loss: 29.5927 - 42ms/epoch - 21ms/step\n",
      "Epoch 356/500\n",
      "2/2 - 0s - loss: 13.6772 - val_loss: 12.6678 - 45ms/epoch - 23ms/step\n",
      "Epoch 357/500\n",
      "2/2 - 0s - loss: 16.8470 - val_loss: 12.0629 - 39ms/epoch - 20ms/step\n",
      "Epoch 358/500\n",
      "2/2 - 0s - loss: 7.9303 - val_loss: 13.4729 - 40ms/epoch - 20ms/step\n",
      "Epoch 359/500\n",
      "2/2 - 0s - loss: 14.9826 - val_loss: 18.0324 - 37ms/epoch - 19ms/step\n",
      "Epoch 360/500\n",
      "2/2 - 0s - loss: 11.1502 - val_loss: 28.8794 - 64ms/epoch - 32ms/step\n",
      "Epoch 361/500\n",
      "2/2 - 0s - loss: 14.6715 - val_loss: 11.7242 - 44ms/epoch - 22ms/step\n",
      "Epoch 362/500\n",
      "2/2 - 0s - loss: 9.4130 - val_loss: 15.2027 - 43ms/epoch - 22ms/step\n",
      "Epoch 363/500\n",
      "2/2 - 0s - loss: 10.4742 - val_loss: 27.5634 - 43ms/epoch - 22ms/step\n",
      "Epoch 364/500\n",
      "2/2 - 0s - loss: 12.8561 - val_loss: 11.3408 - 41ms/epoch - 21ms/step\n",
      "Epoch 365/500\n",
      "2/2 - 0s - loss: 15.6786 - val_loss: 20.3274 - 44ms/epoch - 22ms/step\n",
      "Epoch 366/500\n",
      "2/2 - 0s - loss: 12.3042 - val_loss: 25.9915 - 46ms/epoch - 23ms/step\n",
      "Epoch 367/500\n",
      "2/2 - 0s - loss: 14.6185 - val_loss: 21.2152 - 43ms/epoch - 22ms/step\n",
      "Epoch 368/500\n",
      "2/2 - 0s - loss: 14.5929 - val_loss: 23.7061 - 47ms/epoch - 24ms/step\n",
      "Epoch 369/500\n",
      "2/2 - 0s - loss: 12.1281 - val_loss: 29.2908 - 45ms/epoch - 23ms/step\n",
      "Epoch 370/500\n",
      "2/2 - 0s - loss: 15.7635 - val_loss: 16.5457 - 40ms/epoch - 20ms/step\n",
      "Epoch 371/500\n",
      "2/2 - 0s - loss: 18.0258 - val_loss: 17.4939 - 40ms/epoch - 20ms/step\n",
      "Epoch 372/500\n",
      "2/2 - 0s - loss: 7.5640 - val_loss: 25.2606 - 37ms/epoch - 19ms/step\n",
      "Epoch 373/500\n",
      "2/2 - 0s - loss: 15.9692 - val_loss: 24.8931 - 41ms/epoch - 20ms/step\n",
      "Epoch 374/500\n",
      "2/2 - 0s - loss: 13.0946 - val_loss: 16.5902 - 42ms/epoch - 21ms/step\n",
      "Epoch 375/500\n",
      "2/2 - 0s - loss: 16.3953 - val_loss: 25.1234 - 47ms/epoch - 23ms/step\n",
      "Epoch 376/500\n",
      "2/2 - 0s - loss: 15.0323 - val_loss: 28.6560 - 40ms/epoch - 20ms/step\n",
      "Epoch 377/500\n",
      "2/2 - 0s - loss: 13.2325 - val_loss: 24.6094 - 40ms/epoch - 20ms/step\n",
      "Epoch 378/500\n",
      "2/2 - 0s - loss: 19.4988 - val_loss: 17.8398 - 42ms/epoch - 21ms/step\n",
      "Epoch 379/500\n",
      "2/2 - 0s - loss: 16.1467 - val_loss: 12.4738 - 41ms/epoch - 21ms/step\n",
      "Epoch 380/500\n",
      "2/2 - 0s - loss: 16.0569 - val_loss: 17.9168 - 46ms/epoch - 23ms/step\n",
      "Epoch 381/500\n",
      "2/2 - 0s - loss: 10.8552 - val_loss: 22.0056 - 43ms/epoch - 22ms/step\n",
      "Epoch 382/500\n",
      "2/2 - 0s - loss: 23.4775 - val_loss: 18.6899 - 48ms/epoch - 24ms/step\n",
      "Epoch 383/500\n",
      "2/2 - 0s - loss: 11.4427 - val_loss: 25.6730 - 45ms/epoch - 23ms/step\n",
      "Epoch 384/500\n",
      "2/2 - 0s - loss: 14.0232 - val_loss: 25.1603 - 43ms/epoch - 22ms/step\n",
      "Epoch 385/500\n",
      "2/2 - 0s - loss: 15.7291 - val_loss: 12.7286 - 41ms/epoch - 21ms/step\n",
      "Epoch 386/500\n",
      "2/2 - 0s - loss: 11.1238 - val_loss: 16.7675 - 42ms/epoch - 21ms/step\n",
      "Epoch 387/500\n",
      "2/2 - 0s - loss: 8.5938 - val_loss: 16.3042 - 43ms/epoch - 22ms/step\n",
      "Epoch 388/500\n",
      "2/2 - 0s - loss: 16.6482 - val_loss: 14.8815 - 43ms/epoch - 22ms/step\n",
      "Epoch 389/500\n",
      "2/2 - 0s - loss: 10.5948 - val_loss: 22.8699 - 43ms/epoch - 22ms/step\n",
      "Epoch 390/500\n",
      "2/2 - 0s - loss: 16.7130 - val_loss: 24.8571 - 49ms/epoch - 25ms/step\n",
      "Epoch 391/500\n",
      "2/2 - 0s - loss: 22.2301 - val_loss: 17.7704 - 37ms/epoch - 19ms/step\n",
      "Epoch 392/500\n",
      "2/2 - 0s - loss: 15.5893 - val_loss: 21.2136 - 43ms/epoch - 22ms/step\n",
      "Epoch 393/500\n",
      "2/2 - 0s - loss: 13.0326 - val_loss: 16.3245 - 44ms/epoch - 22ms/step\n",
      "Epoch 394/500\n",
      "2/2 - 0s - loss: 11.5907 - val_loss: 22.0953 - 41ms/epoch - 21ms/step\n",
      "Epoch 395/500\n",
      "2/2 - 0s - loss: 17.5125 - val_loss: 20.2318 - 40ms/epoch - 20ms/step\n",
      "Epoch 396/500\n",
      "2/2 - 0s - loss: 19.0165 - val_loss: 16.3669 - 41ms/epoch - 20ms/step\n",
      "Epoch 397/500\n",
      "2/2 - 0s - loss: 15.4453 - val_loss: 20.1987 - 42ms/epoch - 21ms/step\n",
      "Epoch 398/500\n",
      "2/2 - 0s - loss: 17.0637 - val_loss: 15.5144 - 43ms/epoch - 22ms/step\n",
      "Epoch 399/500\n",
      "2/2 - 0s - loss: 17.5441 - val_loss: 14.4011 - 45ms/epoch - 22ms/step\n",
      "Epoch 400/500\n",
      "2/2 - 0s - loss: 7.5575 - val_loss: 13.7699 - 40ms/epoch - 20ms/step\n",
      "Epoch 401/500\n",
      "2/2 - 0s - loss: 12.7049 - val_loss: 12.7599 - 41ms/epoch - 21ms/step\n",
      "Epoch 402/500\n",
      "2/2 - 0s - loss: 10.2148 - val_loss: 13.3455 - 39ms/epoch - 20ms/step\n",
      "Epoch 403/500\n",
      "2/2 - 0s - loss: 15.0061 - val_loss: 14.5000 - 46ms/epoch - 23ms/step\n",
      "Epoch 404/500\n",
      "2/2 - 0s - loss: 15.4099 - val_loss: 15.2341 - 38ms/epoch - 19ms/step\n",
      "Epoch 405/500\n",
      "2/2 - 0s - loss: 8.6783 - val_loss: 27.1455 - 41ms/epoch - 21ms/step\n",
      "Epoch 406/500\n",
      "2/2 - 0s - loss: 14.3906 - val_loss: 9.3672 - 43ms/epoch - 22ms/step\n",
      "Epoch 407/500\n",
      "2/2 - 0s - loss: 15.9817 - val_loss: 12.9715 - 41ms/epoch - 21ms/step\n",
      "Epoch 408/500\n",
      "2/2 - 0s - loss: 18.5132 - val_loss: 19.8899 - 39ms/epoch - 20ms/step\n",
      "Epoch 409/500\n",
      "2/2 - 0s - loss: 16.9978 - val_loss: 28.0735 - 40ms/epoch - 20ms/step\n",
      "Epoch 410/500\n",
      "2/2 - 0s - loss: 15.1884 - val_loss: 23.5040 - 41ms/epoch - 21ms/step\n",
      "Epoch 411/500\n",
      "2/2 - 0s - loss: 8.7196 - val_loss: 27.5755 - 43ms/epoch - 21ms/step\n",
      "Epoch 412/500\n",
      "2/2 - 0s - loss: 15.6335 - val_loss: 22.7766 - 43ms/epoch - 22ms/step\n",
      "Epoch 413/500\n",
      "2/2 - 0s - loss: 13.6107 - val_loss: 24.2187 - 51ms/epoch - 26ms/step\n",
      "Epoch 414/500\n",
      "2/2 - 0s - loss: 12.1300 - val_loss: 10.1632 - 46ms/epoch - 23ms/step\n",
      "Epoch 415/500\n",
      "2/2 - 0s - loss: 14.9957 - val_loss: 12.5308 - 45ms/epoch - 23ms/step\n",
      "Epoch 416/500\n",
      "2/2 - 0s - loss: 10.5415 - val_loss: 20.4856 - 39ms/epoch - 20ms/step\n",
      "Epoch 417/500\n",
      "2/2 - 0s - loss: 18.4217 - val_loss: 20.4634 - 43ms/epoch - 22ms/step\n",
      "Epoch 418/500\n",
      "2/2 - 0s - loss: 16.7287 - val_loss: 24.9352 - 37ms/epoch - 19ms/step\n",
      "Epoch 419/500\n",
      "2/2 - 0s - loss: 14.5352 - val_loss: 10.0334 - 43ms/epoch - 22ms/step\n",
      "Epoch 420/500\n",
      "2/2 - 0s - loss: 10.0196 - val_loss: 28.8969 - 41ms/epoch - 21ms/step\n",
      "Epoch 421/500\n",
      "2/2 - 0s - loss: 17.2371 - val_loss: 12.6153 - 40ms/epoch - 20ms/step\n",
      "Epoch 422/500\n",
      "2/2 - 0s - loss: 14.8795 - val_loss: 16.1992 - 42ms/epoch - 21ms/step\n",
      "Epoch 423/500\n",
      "2/2 - 0s - loss: 18.5504 - val_loss: 18.2755 - 45ms/epoch - 23ms/step\n",
      "Epoch 424/500\n",
      "2/2 - 0s - loss: 15.3906 - val_loss: 23.9769 - 51ms/epoch - 26ms/step\n",
      "Epoch 425/500\n",
      "2/2 - 0s - loss: 15.5412 - val_loss: 19.8486 - 37ms/epoch - 19ms/step\n",
      "Epoch 426/500\n",
      "2/2 - 0s - loss: 16.2770 - val_loss: 18.9365 - 45ms/epoch - 23ms/step\n",
      "Epoch 427/500\n",
      "2/2 - 0s - loss: 15.7006 - val_loss: 21.4281 - 45ms/epoch - 23ms/step\n",
      "Epoch 428/500\n",
      "2/2 - 0s - loss: 18.4949 - val_loss: 19.7214 - 43ms/epoch - 22ms/step\n",
      "Epoch 429/500\n",
      "2/2 - 0s - loss: 14.9845 - val_loss: 20.6825 - 43ms/epoch - 22ms/step\n",
      "Epoch 430/500\n",
      "2/2 - 0s - loss: 16.4392 - val_loss: 27.3086 - 41ms/epoch - 21ms/step\n",
      "Epoch 431/500\n",
      "2/2 - 0s - loss: 15.3561 - val_loss: 18.9188 - 40ms/epoch - 20ms/step\n",
      "Epoch 432/500\n",
      "2/2 - 0s - loss: 9.3305 - val_loss: 28.1364 - 42ms/epoch - 21ms/step\n",
      "Epoch 433/500\n",
      "2/2 - 0s - loss: 15.0100 - val_loss: 22.1711 - 38ms/epoch - 19ms/step\n",
      "Epoch 434/500\n",
      "2/2 - 0s - loss: 11.7486 - val_loss: 22.8994 - 38ms/epoch - 19ms/step\n",
      "Epoch 435/500\n",
      "2/2 - 0s - loss: 14.3320 - val_loss: 17.6738 - 43ms/epoch - 22ms/step\n",
      "Epoch 436/500\n",
      "2/2 - 0s - loss: 11.0487 - val_loss: 22.5627 - 44ms/epoch - 22ms/step\n",
      "Epoch 437/500\n",
      "2/2 - 0s - loss: 14.1366 - val_loss: 16.0220 - 50ms/epoch - 25ms/step\n",
      "Epoch 438/500\n",
      "2/2 - 0s - loss: 17.0141 - val_loss: 20.5755 - 42ms/epoch - 21ms/step\n",
      "Epoch 439/500\n",
      "2/2 - 0s - loss: 14.4617 - val_loss: 19.7261 - 39ms/epoch - 20ms/step\n",
      "Epoch 440/500\n",
      "2/2 - 0s - loss: 12.2639 - val_loss: 12.7349 - 37ms/epoch - 19ms/step\n",
      "Epoch 441/500\n",
      "2/2 - 0s - loss: 5.3515 - val_loss: 22.8823 - 41ms/epoch - 21ms/step\n",
      "Epoch 442/500\n",
      "2/2 - 0s - loss: 11.5020 - val_loss: 28.2728 - 44ms/epoch - 22ms/step\n",
      "Epoch 443/500\n",
      "2/2 - 0s - loss: 14.4461 - val_loss: 24.5148 - 47ms/epoch - 23ms/step\n",
      "Epoch 444/500\n",
      "2/2 - 0s - loss: 21.4610 - val_loss: 18.6770 - 65ms/epoch - 33ms/step\n",
      "Epoch 445/500\n",
      "2/2 - 0s - loss: 18.3890 - val_loss: 18.7495 - 46ms/epoch - 23ms/step\n",
      "Epoch 446/500\n",
      "2/2 - 0s - loss: 17.7942 - val_loss: 17.4369 - 45ms/epoch - 23ms/step\n",
      "Epoch 447/500\n",
      "2/2 - 0s - loss: 13.0105 - val_loss: 28.0040 - 45ms/epoch - 23ms/step\n",
      "Epoch 448/500\n",
      "2/2 - 0s - loss: 15.6719 - val_loss: 20.2471 - 39ms/epoch - 20ms/step\n",
      "Epoch 449/500\n",
      "2/2 - 0s - loss: 17.5905 - val_loss: 23.3724 - 41ms/epoch - 21ms/step\n",
      "Epoch 450/500\n",
      "2/2 - 0s - loss: 20.0076 - val_loss: 13.8168 - 39ms/epoch - 20ms/step\n",
      "Epoch 451/500\n",
      "2/2 - 0s - loss: 14.2534 - val_loss: 18.5741 - 40ms/epoch - 20ms/step\n",
      "Epoch 452/500\n",
      "2/2 - 0s - loss: 7.4351 - val_loss: 17.0824 - 41ms/epoch - 21ms/step\n",
      "Epoch 453/500\n",
      "2/2 - 0s - loss: 12.9406 - val_loss: 25.7226 - 39ms/epoch - 20ms/step\n",
      "Epoch 454/500\n",
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      "Epoch 455/500\n",
      "2/2 - 0s - loss: 18.1593 - val_loss: 24.8451 - 41ms/epoch - 21ms/step\n",
      "Epoch 456/500\n",
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      "Epoch 457/500\n",
      "2/2 - 0s - loss: 14.1201 - val_loss: 21.6025 - 52ms/epoch - 26ms/step\n",
      "Epoch 458/500\n",
      "2/2 - 0s - loss: 17.2214 - val_loss: 17.4617 - 43ms/epoch - 22ms/step\n",
      "Epoch 459/500\n",
      "2/2 - 0s - loss: 13.0399 - val_loss: 16.3553 - 41ms/epoch - 20ms/step\n",
      "Epoch 460/500\n",
      "2/2 - 0s - loss: 14.7097 - val_loss: 18.4253 - 42ms/epoch - 21ms/step\n",
      "Epoch 461/500\n",
      "2/2 - 0s - loss: 18.5586 - val_loss: 17.8251 - 39ms/epoch - 20ms/step\n",
      "Epoch 462/500\n",
      "2/2 - 0s - loss: 13.0173 - val_loss: 23.9441 - 43ms/epoch - 22ms/step\n",
      "Epoch 463/500\n",
      "2/2 - 0s - loss: 11.6669 - val_loss: 24.6380 - 38ms/epoch - 19ms/step\n",
      "Epoch 464/500\n",
      "2/2 - 0s - loss: 11.1710 - val_loss: 24.3598 - 43ms/epoch - 22ms/step\n",
      "Epoch 465/500\n",
      "2/2 - 0s - loss: 14.8347 - val_loss: 25.7054 - 41ms/epoch - 21ms/step\n",
      "Epoch 466/500\n",
      "2/2 - 0s - loss: 17.1894 - val_loss: 19.5389 - 37ms/epoch - 19ms/step\n",
      "Epoch 467/500\n",
      "2/2 - 0s - loss: 7.9569 - val_loss: 23.7303 - 43ms/epoch - 22ms/step\n",
      "Epoch 468/500\n",
      "2/2 - 0s - loss: 12.4230 - val_loss: 27.9118 - 41ms/epoch - 21ms/step\n",
      "Epoch 469/500\n",
      "2/2 - 0s - loss: 17.9424 - val_loss: 20.9348 - 40ms/epoch - 20ms/step\n",
      "Epoch 470/500\n",
      "2/2 - 0s - loss: 16.0952 - val_loss: 25.6279 - 40ms/epoch - 20ms/step\n",
      "Epoch 471/500\n",
      "2/2 - 0s - loss: 15.5683 - val_loss: 21.7777 - 41ms/epoch - 21ms/step\n",
      "Epoch 472/500\n",
      "2/2 - 0s - loss: 13.8198 - val_loss: 12.6009 - 39ms/epoch - 20ms/step\n",
      "Epoch 473/500\n",
      "2/2 - 0s - loss: 13.3117 - val_loss: 25.5916 - 40ms/epoch - 20ms/step\n",
      "Epoch 474/500\n",
      "2/2 - 0s - loss: 15.7859 - val_loss: 15.4870 - 39ms/epoch - 19ms/step\n",
      "Epoch 475/500\n",
      "2/2 - 0s - loss: 13.4067 - val_loss: 18.7979 - 41ms/epoch - 21ms/step\n",
      "Epoch 476/500\n",
      "2/2 - 0s - loss: 11.4216 - val_loss: 13.5229 - 39ms/epoch - 20ms/step\n",
      "Epoch 477/500\n",
      "2/2 - 0s - loss: 4.6853 - val_loss: 27.3657 - 44ms/epoch - 22ms/step\n",
      "Epoch 478/500\n",
      "2/2 - 0s - loss: 14.9429 - val_loss: 22.8547 - 45ms/epoch - 23ms/step\n",
      "Epoch 479/500\n",
      "2/2 - 0s - loss: 9.7522 - val_loss: 23.3265 - 42ms/epoch - 21ms/step\n",
      "Epoch 480/500\n",
      "2/2 - 0s - loss: 11.8420 - val_loss: 26.2415 - 43ms/epoch - 21ms/step\n",
      "Epoch 481/500\n",
      "2/2 - 0s - loss: 12.8830 - val_loss: 13.9441 - 43ms/epoch - 22ms/step\n",
      "Epoch 482/500\n",
      "2/2 - 0s - loss: 16.5731 - val_loss: 14.3453 - 37ms/epoch - 19ms/step\n",
      "Epoch 483/500\n",
      "2/2 - 0s - loss: 6.3313 - val_loss: 17.5558 - 41ms/epoch - 21ms/step\n",
      "Epoch 484/500\n",
      "2/2 - 0s - loss: 13.4880 - val_loss: 23.5318 - 38ms/epoch - 19ms/step\n",
      "Epoch 485/500\n",
      "2/2 - 0s - loss: 17.1747 - val_loss: 18.7214 - 42ms/epoch - 21ms/step\n",
      "Epoch 486/500\n",
      "2/2 - 0s - loss: 15.5234 - val_loss: 17.0587 - 42ms/epoch - 21ms/step\n",
      "Epoch 487/500\n",
      "2/2 - 0s - loss: 16.7025 - val_loss: 16.8860 - 43ms/epoch - 22ms/step\n",
      "Epoch 488/500\n",
      "2/2 - 0s - loss: 13.9657 - val_loss: 21.8404 - 40ms/epoch - 20ms/step\n",
      "Epoch 489/500\n",
      "2/2 - 0s - loss: 18.8398 - val_loss: 16.9895 - 39ms/epoch - 20ms/step\n",
      "Epoch 490/500\n",
      "2/2 - 0s - loss: 19.3717 - val_loss: 19.6569 - 42ms/epoch - 21ms/step\n",
      "Epoch 491/500\n",
      "2/2 - 0s - loss: 17.8527 - val_loss: 15.9251 - 45ms/epoch - 23ms/step\n",
      "Epoch 492/500\n",
      "2/2 - 0s - loss: 9.4580 - val_loss: 24.3129 - 46ms/epoch - 23ms/step\n",
      "Epoch 493/500\n",
      "2/2 - 0s - loss: 13.2394 - val_loss: 24.1906 - 42ms/epoch - 21ms/step\n",
      "Epoch 494/500\n",
      "2/2 - 0s - loss: 11.7210 - val_loss: 26.2587 - 40ms/epoch - 20ms/step\n",
      "Epoch 495/500\n",
      "2/2 - 0s - loss: 12.4924 - val_loss: 12.8441 - 41ms/epoch - 21ms/step\n",
      "Epoch 496/500\n",
      "2/2 - 0s - loss: 7.8899 - val_loss: 21.8356 - 37ms/epoch - 19ms/step\n",
      "Epoch 497/500\n",
      "2/2 - 0s - loss: 20.7398 - val_loss: 16.2406 - 35ms/epoch - 18ms/step\n",
      "Epoch 498/500\n",
      "2/2 - 0s - loss: 11.0308 - val_loss: 23.8106 - 41ms/epoch - 21ms/step\n",
      "Epoch 499/500\n",
      "2/2 - 0s - loss: 14.3434 - val_loss: 23.9337 - 43ms/epoch - 22ms/step\n",
      "Epoch 500/500\n",
      "2/2 - 0s - loss: 15.1480 - val_loss: 13.8666 - 39ms/epoch - 20ms/step\n",
      "CPU times: total: 9.92 s\n",
      "Wall time: 23.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history_dnn = model.fit(\n",
    "    train_dataset,\n",
    "    train_dataset_labels,\n",
    "    validation_split=0.2,\n",
    "    verbose=2, epochs=500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "history_dnn.history\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jmeP6vt3z0oA"
   },
   "source": [
    "## Plot Loss\n",
    "\n",
    "Using matplotlib visualise how the loss (both validation and training) is changing, use this information to retrain the model with appropriate parameters.<br>We ideally want the loss to be constant over the last few iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "RQTNqPHm0mOi"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_loss(history):\n",
    "  plt.plot(history.history['loss'], label='loss')\n",
    "  plt.plot(history.history['val_loss'], label='val_loss')\n",
    "#   plt.ylim([100,200])\n",
    "#   plt.xlim([100,200])\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Error')\n",
    "  plt.legend()\n",
    "  plt.grid(True)\n",
    "plot_loss(history_dnn)\n",
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IVrR_vXA7kOt"
   },
   "source": [
    "## Evaluation Metrics\n",
    "Use the R2 Score function implemented in the first assignment to evaluate the performance of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 131ms/step\n",
      "[ 222.779 -199.175  155.224  -63.1    342.113  -27.181  -71.758  -81.969\n",
      "   51.259  163.616  -30.011 -122.573   70.134 -335.154 -119.977  182.56\n",
      " -107.238 -141.438 -133.793  143.729]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(test_dataset).flatten()\n",
    "print(test_predictions)\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_dataset_labels, test_predictions)\n",
    "plt.xlabel('True Values ')\n",
    "plt.ylabel('Predictions ')\n",
    "# lims = [300, ]\n",
    "# plt.xlim(lims)\n",
    "# plt.ylim(lims)\n",
    "_ = plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "-lOHpD8-7ggm"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.05363065348077%\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>real</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>222.841343</td>\n",
       "      <td>222.779221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-167.552578</td>\n",
       "      <td>-199.175400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173.478511</td>\n",
       "      <td>155.223938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-46.300805</td>\n",
       "      <td>-63.100182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>347.883140</td>\n",
       "      <td>342.113037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-39.573033</td>\n",
       "      <td>-27.181314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-90.567807</td>\n",
       "      <td>-71.757988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-110.252556</td>\n",
       "      <td>-81.968773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.237675</td>\n",
       "      <td>51.258656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>159.059644</td>\n",
       "      <td>163.615692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-40.865913</td>\n",
       "      <td>-30.010990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-150.615394</td>\n",
       "      <td>-122.572929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>57.235840</td>\n",
       "      <td>70.134117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-341.664377</td>\n",
       "      <td>-335.154327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-99.756088</td>\n",
       "      <td>-119.976830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>185.366822</td>\n",
       "      <td>182.559738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-90.058453</td>\n",
       "      <td>-107.237762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-151.555027</td>\n",
       "      <td>-141.438171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-135.335612</td>\n",
       "      <td>-133.792557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>137.100908</td>\n",
       "      <td>143.728775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          real   predicted\n",
       "0   222.841343  222.779221\n",
       "1  -167.552578 -199.175400\n",
       "2   173.478511  155.223938\n",
       "3   -46.300805  -63.100182\n",
       "4   347.883140  342.113037\n",
       "5   -39.573033  -27.181314\n",
       "6   -90.567807  -71.757988\n",
       "7  -110.252556  -81.968773\n",
       "8    45.237675   51.258656\n",
       "9   159.059644  163.615692\n",
       "10  -40.865913  -30.010990\n",
       "11 -150.615394 -122.572929\n",
       "12   57.235840   70.134117\n",
       "13 -341.664377 -335.154327\n",
       "14  -99.756088 -119.976830\n",
       "15  185.366822  182.559738\n",
       "16  -90.058453 -107.237762\n",
       "17 -151.555027 -141.438171\n",
       "18 -135.335612 -133.792557\n",
       "19  137.100908  143.728775"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert the function for R2 Score\n",
    "# def mean_squared_error(y_true, y_pred):\n",
    "#     mean_se = (1/regressor.get_n_samples())*((np.subtract(y_test,y_predicted))**2)\n",
    "\n",
    "#     return np.sum(mean_se)\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "def r2_score(y_true, y_pred):\n",
    "    ssr= np.sum((np.subtract(y_true,y_pred))**2)\n",
    "    mean = np.mean(y_true)\n",
    "    sst = np.sum((np.subtract(mean,y_pred))**2)\n",
    "    denom = ssr/sst\n",
    "    r_score = 1-denom\n",
    "    return r_score\n",
    "    # return the r2 score\n",
    "       \n",
    "\n",
    "# mse =  mean_squared_error(test_dataset_labels,test_predictions)\n",
    "# print(\"MSE:\", mse)\n",
    "\n",
    "accu = r2_score(test_dataset_labels,test_predictions)\n",
    "print(f\"Accuracy: {accu*100}%\")\n",
    "\n",
    "output = pd.DataFrame({'real':test_dataset_labels,'predicted':test_predictions})\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dnn_model': 12.968597412109375}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results={}\n",
    "test_results['dnn_model'] = model.evaluate(test_dataset, test_dataset_labels, verbose=0)\n",
    "test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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