CNN_model2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kE1VHHbpShac"
   },
   "source": [
    "### CNN Model for the Phishing Websites group project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "J-v-B2BBShad"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msn\n",
    "from scipy.io import arff\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
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    "id": "vKEfCmImShad",
    "outputId": "e5435b09-9058-44c5-d2ca-da77d0afcc34"
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   "outputs": [
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      "text/plain": [
       "  having_IP_Address URL_Length Shortining_Service having_At_Symbol  \\\n",
       "0                -1          1                  1                1   \n",
       "1                 1          1                  1                1   \n",
       "2                 1          0                  1                1   \n",
       "3                 1          0                  1                1   \n",
       "4                 1          0                 -1                1   \n",
       "\n",
       "  double_slash_redirecting Prefix_Suffix having_Sub_Domain SSLfinal_State  \\\n",
       "0                       -1            -1                -1             -1   \n",
       "1                        1            -1                 0              1   \n",
       "2                        1            -1                -1             -1   \n",
       "3                        1            -1                -1             -1   \n",
       "4                        1            -1                 1              1   \n",
       "\n",
       "  Domain_registeration_length Favicon  ... popUpWidnow Iframe age_of_domain  \\\n",
       "0                          -1       1  ...           1      1            -1   \n",
       "1                          -1       1  ...           1      1            -1   \n",
       "2                          -1       1  ...           1      1             1   \n",
       "3                           1       1  ...           1      1            -1   \n",
       "4                          -1       1  ...          -1      1            -1   \n",
       "\n",
       "  DNSRecord web_traffic Page_Rank Google_Index Links_pointing_to_page  \\\n",
       "0        -1          -1        -1            1                      1   \n",
       "1        -1           0        -1            1                      1   \n",
       "2        -1           1        -1            1                      0   \n",
       "3        -1           1        -1            1                     -1   \n",
       "4        -1           0        -1            1                      1   \n",
       "\n",
       "  Statistical_report Result  \n",
       "0                 -1     -1  \n",
       "1                  1     -1  \n",
       "2                 -1     -1  \n",
       "3                  1     -1  \n",
       "4                  1      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = arff.loadarff('/Training Dataset.arff')\n",
    "df = pd.DataFrame(data[0])\n",
    "df = df.apply(lambda col: col.str.decode('utf-8').str.strip(\"'\"))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "gb9LkAymShae"
   },
   "outputs": [],
   "source": [
    "X = df.drop(\"Result\", axis=1)\n",
    "Y = df['Result']\n",
    "map = {1:1, -1:0}\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train = X_train.astype(float)\n",
    "X_test = X_test.astype(float)\n",
    "Y_test = Y_test.astype(float)\n",
    "Y_train = Y_train.astype(float)\n",
    "Y_train = np.array([map[y] for y in Y_train]).astype(float)\n",
    "Y_test = np.array([map[y] for y in Y_test]).astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r_7GBbYfShae",
    "outputId": "2020e910-8240-4f02-d98a-4966b868e597"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "139/139 [==============================] - 2s 4ms/step - loss: 0.3131 - accuracy: 0.8589\n",
      "Epoch 2/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1922 - accuracy: 0.9244\n",
      "Epoch 3/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1703 - accuracy: 0.9307\n",
      "Epoch 4/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1573 - accuracy: 0.9352\n",
      "Epoch 5/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1525 - accuracy: 0.9351\n",
      "Epoch 6/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1428 - accuracy: 0.9393\n",
      "Epoch 7/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1358 - accuracy: 0.9428\n",
      "Epoch 8/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.1331 - accuracy: 0.9414\n",
      "Epoch 9/50\n",
      "139/139 [==============================] - 1s 5ms/step - loss: 0.1304 - accuracy: 0.9439\n",
      "Epoch 10/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.1196 - accuracy: 0.9488\n",
      "Epoch 11/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.1183 - accuracy: 0.9476\n",
      "Epoch 12/50\n",
      "139/139 [==============================] - 1s 7ms/step - loss: 0.1142 - accuracy: 0.9506\n",
      "Epoch 13/50\n",
      "139/139 [==============================] - 1s 8ms/step - loss: 0.1091 - accuracy: 0.9538\n",
      "Epoch 14/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.1112 - accuracy: 0.9522\n",
      "Epoch 15/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.1029 - accuracy: 0.9552\n",
      "Epoch 16/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.1016 - accuracy: 0.9548\n",
      "Epoch 17/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0981 - accuracy: 0.9584\n",
      "Epoch 18/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0977 - accuracy: 0.9587\n",
      "Epoch 19/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0940 - accuracy: 0.9587\n",
      "Epoch 20/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0908 - accuracy: 0.9618\n",
      "Epoch 21/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0875 - accuracy: 0.9626\n",
      "Epoch 22/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0870 - accuracy: 0.9629\n",
      "Epoch 23/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0850 - accuracy: 0.9633\n",
      "Epoch 24/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0886 - accuracy: 0.9634\n",
      "Epoch 25/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0827 - accuracy: 0.9642\n",
      "Epoch 26/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0782 - accuracy: 0.9668\n",
      "Epoch 27/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0791 - accuracy: 0.9656\n",
      "Epoch 28/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0754 - accuracy: 0.9682\n",
      "Epoch 29/50\n",
      "139/139 [==============================] - 1s 5ms/step - loss: 0.0730 - accuracy: 0.9697\n",
      "Epoch 30/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.0733 - accuracy: 0.9698\n",
      "Epoch 31/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.0728 - accuracy: 0.9679\n",
      "Epoch 32/50\n",
      "139/139 [==============================] - 1s 5ms/step - loss: 0.0714 - accuracy: 0.9716\n",
      "Epoch 33/50\n",
      "139/139 [==============================] - 1s 6ms/step - loss: 0.0688 - accuracy: 0.9708\n",
      "Epoch 34/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0691 - accuracy: 0.9711\n",
      "Epoch 35/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0699 - accuracy: 0.9706\n",
      "Epoch 36/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0671 - accuracy: 0.9722\n",
      "Epoch 37/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0665 - accuracy: 0.9722\n",
      "Epoch 38/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0618 - accuracy: 0.9742\n",
      "Epoch 39/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0650 - accuracy: 0.9730\n",
      "Epoch 40/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0634 - accuracy: 0.9741\n",
      "Epoch 41/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0604 - accuracy: 0.9760\n",
      "Epoch 42/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0591 - accuracy: 0.9757\n",
      "Epoch 43/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0605 - accuracy: 0.9743\n",
      "Epoch 44/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0622 - accuracy: 0.9746\n",
      "Epoch 45/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0593 - accuracy: 0.9746\n",
      "Epoch 46/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0573 - accuracy: 0.9780\n",
      "Epoch 47/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0588 - accuracy: 0.9764\n",
      "Epoch 48/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0554 - accuracy: 0.9774\n",
      "Epoch 49/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0566 - accuracy: 0.9751\n",
      "Epoch 50/50\n",
      "139/139 [==============================] - 1s 4ms/step - loss: 0.0550 - accuracy: 0.9761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7ad984247970>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "\n",
    "# Reshape the input data to be 3D\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
    "\n",
    "model = models.Sequential()\n",
    "\n",
    "model.add(layers.Conv1D(filters=16, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "\n",
    "model.add(layers.MaxPooling1D(pool_size=2))\n",
    "\n",
    "model.add(layers.Conv1D(filters=32, kernel_size=3, activation='relu'))\n",
    "\n",
    "\n",
    "model.add(layers.MaxPooling1D(pool_size=2))\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, Y_train, epochs=50, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yqwa4fu-Shae",
    "outputId": "ff631a3e-5958-4c7f-aedc-4242b3fac97d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 0s 4ms/step\n",
      "Accuracy: 0.9579375848032564\n",
      "Precision: 0.9580175219861597\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = model.predict(X_test)\n",
    "Y_test = np.array([map[y] for y in Y_test]).astype(np.float64)\n",
    "predictions_binary = np.round(predictions)\n",
    "accuracy = accuracy_score(Y_test, predictions_binary)\n",
    "precision = precision_score(Y_test, predictions_binary, average='macro')\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JpY2sVolShae"
   },
   "source": [
    "### Attempts at improving the score for the above base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "zATZetWbShae"
   },
   "outputs": [],
   "source": [
    "#Preprocessing data again to train a new model\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train = X_train.astype(float)\n",
    "X_test = X_test.astype(float)\n",
    "Y_train = Y_train.astype(float)\n",
    "Y_test = Y_test.astype(float)\n",
    "Y_train = np.array([map[y] for y in Y_train]).astype(float)\n",
    "Y_test = np.array([map[y] for y in Y_test]).astype(float)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
    "\n",
    "# New Model\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "model.add(layers.MaxPooling1D(pool_size=2))\n",
    "model.add(layers.Conv1D(filters=16, kernel_size=3, activation='relu', input_shape=(32, 1)))\n",
    "model.add(layers.MaxPooling1D(pool_size=2))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
    "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Aj7sQDVMShae",
    "outputId": "834b4e66-9d60-4577-cfef-5e828883ec3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "111/111 [==============================] - 2s 8ms/step - loss: 0.4310 - accuracy: 0.8004 - val_loss: 0.2277 - val_accuracy: 0.9158\n",
      "Epoch 2/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.2365 - accuracy: 0.9063 - val_loss: 0.1831 - val_accuracy: 0.9327\n",
      "Epoch 3/50\n",
      "111/111 [==============================] - 1s 8ms/step - loss: 0.2059 - accuracy: 0.9225 - val_loss: 0.1887 - val_accuracy: 0.9293\n",
      "Epoch 4/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1923 - accuracy: 0.9255 - val_loss: 0.1698 - val_accuracy: 0.9373\n",
      "Epoch 5/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1791 - accuracy: 0.9282 - val_loss: 0.1619 - val_accuracy: 0.9384\n",
      "Epoch 6/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1742 - accuracy: 0.9340 - val_loss: 0.1583 - val_accuracy: 0.9384\n",
      "Epoch 7/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1675 - accuracy: 0.9317 - val_loss: 0.1545 - val_accuracy: 0.9333\n",
      "Epoch 8/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1567 - accuracy: 0.9387 - val_loss: 0.1483 - val_accuracy: 0.9339\n",
      "Epoch 9/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1540 - accuracy: 0.9387 - val_loss: 0.1497 - val_accuracy: 0.9310\n",
      "Epoch 10/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1478 - accuracy: 0.9408 - val_loss: 0.1454 - val_accuracy: 0.9356\n",
      "Epoch 11/50\n",
      "111/111 [==============================] - 1s 6ms/step - loss: 0.1458 - accuracy: 0.9395 - val_loss: 0.1648 - val_accuracy: 0.9373\n",
      "Epoch 12/50\n",
      "111/111 [==============================] - 1s 6ms/step - loss: 0.1445 - accuracy: 0.9412 - val_loss: 0.1409 - val_accuracy: 0.9378\n",
      "Epoch 13/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1365 - accuracy: 0.9415 - val_loss: 0.1546 - val_accuracy: 0.9293\n",
      "Epoch 14/50\n",
      "111/111 [==============================] - 1s 6ms/step - loss: 0.1371 - accuracy: 0.9405 - val_loss: 0.1359 - val_accuracy: 0.9418\n",
      "Epoch 15/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1303 - accuracy: 0.9460 - val_loss: 0.1437 - val_accuracy: 0.9395\n",
      "Epoch 16/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1310 - accuracy: 0.9450 - val_loss: 0.1346 - val_accuracy: 0.9429\n",
      "Epoch 17/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1255 - accuracy: 0.9452 - val_loss: 0.1294 - val_accuracy: 0.9446\n",
      "Epoch 18/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1227 - accuracy: 0.9474 - val_loss: 0.1316 - val_accuracy: 0.9440\n",
      "Epoch 19/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1222 - accuracy: 0.9483 - val_loss: 0.1251 - val_accuracy: 0.9469\n",
      "Epoch 20/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1171 - accuracy: 0.9487 - val_loss: 0.1262 - val_accuracy: 0.9452\n",
      "Epoch 21/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1159 - accuracy: 0.9508 - val_loss: 0.1277 - val_accuracy: 0.9469\n",
      "Epoch 22/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1146 - accuracy: 0.9512 - val_loss: 0.1231 - val_accuracy: 0.9463\n",
      "Epoch 23/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1104 - accuracy: 0.9531 - val_loss: 0.1378 - val_accuracy: 0.9429\n",
      "Epoch 24/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1120 - accuracy: 0.9525 - val_loss: 0.1225 - val_accuracy: 0.9429\n",
      "Epoch 25/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1093 - accuracy: 0.9542 - val_loss: 0.1260 - val_accuracy: 0.9463\n",
      "Epoch 26/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1075 - accuracy: 0.9543 - val_loss: 0.1183 - val_accuracy: 0.9480\n",
      "Epoch 27/50\n",
      "111/111 [==============================] - 1s 7ms/step - loss: 0.1055 - accuracy: 0.9565 - val_loss: 0.1220 - val_accuracy: 0.9474\n",
      "Epoch 28/50\n",
      "111/111 [==============================] - 1s 5ms/step - loss: 0.1023 - accuracy: 0.9553 - val_loss: 0.1218 - val_accuracy: 0.9429\n",
      "Epoch 29/50\n",
      "111/111 [==============================] - 1s 6ms/step - loss: 0.1013 - accuracy: 0.9577 - val_loss: 0.1216 - val_accuracy: 0.9452\n",
      "70/70 [==============================] - 0s 2ms/step\n",
      "Accuracy: 0.9479873360470376\n",
      "Precision: 0.9486275145300753\n"
     ]
    }
   ],
   "source": [
    "# Train the model with early stopping\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
    "history = model.fit(X_train, Y_train, epochs=50, batch_size=64, validation_split=0.2, callbacks=[early_stopping])\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "predictions_binary = np.round(predictions)\n",
    "accuracy = accuracy_score(Y_test, predictions_binary)\n",
    "precision = precision_score(Y_test, predictions_binary, average='macro')\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "OYE-Rqu8Shae",
    "outputId": "7c7f2ba7-dd3c-4694-a9f4-95ccc5eba17b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training history\n",
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "cca8a382e7318d44f733ed6c078b368704121a8a201af092ba77c4564379a2b3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}