diff --git a/Cats Vs Dogs Classification/model/cats_vs_dogs_classification.ipynb b/Cats Vs Dogs Classification/model/cats_vs_dogs_classification.ipynb
index 1c6e289c6..c44e3e540 100644
--- a/Cats Vs Dogs Classification/model/cats_vs_dogs_classification.ipynb	
+++ b/Cats Vs Dogs Classification/model/cats_vs_dogs_classification.ipynb	
@@ -1,398 +1,373 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import os\n",
-    "import cv2 as cv\n",
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TRAIN_DIR = \"../dataset/training_set\"\n",
-    "TEST_DIR = \"../dataset/test_set\"\n",
-    "\n",
-    "categories = [\"cats\", \"dogs\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dim = 100 #dimension of the images"
-   ]
-  },
-  {
-   "source": [
-    "## Creating Training Data"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_data = []\n",
-    "\n",
-    "def create_train_data():\n",
-    "    for categ in categories:\n",
-    "        num = categories.index(categ)\n",
-    "        path = os.path.join(TRAIN_DIR, categ)\n",
-    "        for images in os.listdir(path):\n",
-    "            img2arr = cv.imread(os.path.join(path, images), cv.IMREAD_GRAYSCALE) #converting images into greyscale as colour isn't a differentiating factor between cats and dogs\n",
-    "            new_img2arr = cv.resize(img2arr, (dim, dim)) # converting images to array\n",
-    "            train_data.append([new_img2arr, num])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "create_train_data()"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
   },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[array([[ 43,  44,  43, ...,  94, 183, 193],\n",
-       "        [ 40,  37,  38, ..., 103, 177, 194],\n",
-       "        [ 43,  37,  40, ...,  91, 174, 195],\n",
-       "        ...,\n",
-       "        [ 21,  20,  20, ...,  84,  73,  41],\n",
-       "        [ 24,  18,  21, ...,  59,  39,  30],\n",
-       "        [ 29,  20,  24, ...,  89,  19,  36]], dtype=uint8),\n",
-       " 0]"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v3tJTzT2dIhy"
+      },
+      "outputs": [],
+      "source": [
+        "!mkdir -p ~/.kaggle\n",
+        "!cp kaggle.json ~/.kaggle/"
       ]
-     },
-     "metadata": {},
-     "execution_count": 14
-    }
-   ],
-   "source": [
-    "train_data[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 720x504 with 1 Axes>",
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"287.245526pt\" version=\"1.1\" viewBox=\"0 0 287.761364 287.245526\" width=\"287.761364pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-06-14T22:18:07.893761</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
+      "cell_type": "code",
+      "source": [
+        "!kaggle datasets download -d chetankv/dogs-cats-images"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3lucel3bdtAd",
+        "outputId": "ae20f691-2530-4dbc-fd6d-a712ba3a6842"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
+            "Dataset URL: https://www.kaggle.com/datasets/chetankv/dogs-cats-images\n",
+            "License(s): CC0-1.0\n",
+            "dogs-cats-images.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
+          ]
+        }
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(10, 7)).add_subplot(1, 2, 1)\n",
-    "plt.imshow(train_data[-2][0], cmap='gray')\n",
-    "plt.figure(figsize=(10, 7)).add_subplot(1, 2, 1)\n",
-    "plt.imshow(train_data[0][0], cmap='gray')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "random.shuffle(train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = []\n",
-    "y_train = []\n",
-    "\n",
-    "for features, lable in train_data:\n",
-    "    X_train.append(features)\n",
-    "    y_train.append(lable)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "source": [
+        "import zipfile\n",
+        "zip_ref = zipfile.ZipFile('dogs-cats-images.zip', 'r')\n",
+        "zip_ref.extractall('/content')\n",
+        "zip_ref.close()"
+      ],
+      "metadata": {
+        "id": "iaLwJL-_dwZo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "10000"
+      "cell_type": "code",
+      "source": [
+        "# Import library\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+        "\n",
+        "train_datagen = ImageDataGenerator(\n",
+        "    rescale=1./255,\n",
+        "    shear_range=0.2,\n",
+        "    zoom_range=0.2,\n",
+        "    horizontal_flip=True,\n",
+        "    rotation_range=20,\n",
+        "    validation_split=0.2 # Splitting data into train and validation data\n",
+        ")\n",
+        "\n",
+        "test_datagen = ImageDataGenerator(rescale=1/255.)\n",
+        "\n",
+        "train_data = train_datagen.flow_from_directory(directory=\"/content/dog vs cat/dataset/training_set\",\n",
+        "                                               target_size=(240, 240),\n",
+        "                                               batch_size=32,\n",
+        "                                               class_mode=\"categorical\",\n",
+        "                                               subset=\"training\") # Training data\n",
+        "\n",
+        "validation_data = train_datagen.flow_from_directory(directory=\"/content/dog vs cat/dataset/training_set\",\n",
+        "                                               target_size=(240, 240),\n",
+        "                                               batch_size=32,\n",
+        "                                               class_mode=\"categorical\",\n",
+        "                                               subset=\"validation\") # Validation data\n",
+        "\n",
+        "test_data = test_datagen.flow_from_directory(directory=\"/content/dog vs cat/dataset/test_set\",\n",
+        "                                               target_size=(240, 240),\n",
+        "                                               batch_size=32,\n",
+        "                                               class_mode=\"categorical\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wWbJ4Yr5i8vJ",
+        "outputId": "b0f436a1-4550-4829-ac89-e454a79ee7ed"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 6400 images belonging to 2 classes.\n",
+            "Found 1600 images belonging to 2 classes.\n",
+            "Found 2000 images belonging to 2 classes.\n"
+          ]
+        }
       ]
-     },
-     "metadata": {},
-     "execution_count": 18
-    }
-   ],
-   "source": [
-    "X_train[0].size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = np.array(X_train).astype('float32').reshape(-1, dim, dim, 1)\n",
-    "y_train = np.array(y_train).astype('int32').reshape((-1,1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "(8000, 100, 100, 1)\n(8000, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(X_train.shape)\n",
-    "print(y_train.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = X_train/255.0 #normalizing the image's pixel values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Making Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This model uses 3 convolutional layers and 2 dense fully connected layers along with pooling layer to reduce size of images after each step\n",
-    "model = tf.keras.models.Sequential([\n",
-    "    Conv2D(64, (3, 3), activation='relu', input_shape=X_train.shape[1:]),\n",
-    "    MaxPooling2D(pool_size=(2,2)),\n",
-    "    Dropout(0.3), # dropout is used to reduce the model from overfitting too much\n",
-    "\n",
-    "    Conv2D(128, (3, 3), activation='relu'),\n",
-    "    MaxPooling2D(pool_size=(2,2)),\n",
-    "    Dropout(0.2),\n",
-    "    \n",
-    "\n",
-    "    Conv2D(256, (3, 3), activation='relu'),\n",
-    "    MaxPooling2D(pool_size=(2,2)),\n",
-    "    Dropout(0.1),\n",
-    "\n",
-    "    Flatten(),\n",
-    "    Dense(128, activation='relu'),\n",
-    "    Dense(1, activation='sigmoid')\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "source": [
+        "# Import the necessary libraries\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.applications import Xception\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from tensorflow.keras.callbacks import EarlyStopping\n",
+        "\n",
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Load the Xception model\n",
+        "base_model = Xception(input_shape=(240, 240, 3), include_top=False, weights='imagenet')\n",
+        "\n",
+        "# Freeze the base model\n",
+        "base_model.trainable = False\n",
+        "\n",
+        "model = Sequential([\n",
+        "  base_model,\n",
+        "  GlobalAveragePooling2D(),\n",
+        "  Dropout(0.2),\n",
+        "  Dense(2, activation='softmax')\n",
+        "])\n",
+        "\n",
+        "# Compile the model with optimizer and learning rate\n",
+        "model.compile(optimizer=Adam(learning_rate=0.01),\n",
+        "              loss='categorical_crossentropy',\n",
+        "              metrics=['accuracy'])\n",
+        "\n",
+        "# Early stopping to prevent overfitting\n",
+        "early_stopping = EarlyStopping(monitor='accuracy', patience=3)\n",
+        "\n",
+        "# Fit the model with training data and early stopping\n",
+        "history = model.fit(train_data,\n",
+        "                    batch_size=32,\n",
+        "                    epochs=25,\n",
+        "                    steps_per_epoch=len(train_data),\n",
+        "                    callbacks=[early_stopping])"
+      ],
+      "metadata": {
+        "id": "HVDm6I2mdx6u",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "53c02275-1292-417d-bbdf-ace0e48777e8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n",
+            "200/200 [==============================] - 109s 501ms/step - loss: 0.0968 - accuracy: 0.9708\n",
+            "Epoch 2/25\n",
+            "200/200 [==============================] - 97s 484ms/step - loss: 0.0713 - accuracy: 0.9819\n",
+            "Epoch 3/25\n",
+            "200/200 [==============================] - 98s 489ms/step - loss: 0.0765 - accuracy: 0.9816\n",
+            "Epoch 4/25\n",
+            "200/200 [==============================] - 97s 485ms/step - loss: 0.0787 - accuracy: 0.9827\n",
+            "Epoch 5/25\n",
+            "200/200 [==============================] - 100s 499ms/step - loss: 0.0869 - accuracy: 0.9830\n",
+            "Epoch 6/25\n",
+            "200/200 [==============================] - 100s 502ms/step - loss: 0.0837 - accuracy: 0.9827\n",
+            "Epoch 7/25\n",
+            "200/200 [==============================] - 100s 502ms/step - loss: 0.0899 - accuracy: 0.9834\n",
+            "Epoch 8/25\n",
+            "200/200 [==============================] - 100s 497ms/step - loss: 0.0667 - accuracy: 0.9866\n",
+            "Epoch 9/25\n",
+            "200/200 [==============================] - 97s 487ms/step - loss: 0.0877 - accuracy: 0.9856\n",
+            "Epoch 10/25\n",
+            "200/200 [==============================] - 100s 501ms/step - loss: 0.0844 - accuracy: 0.9861\n",
+            "Epoch 11/25\n",
+            "200/200 [==============================] - 97s 483ms/step - loss: 0.0807 - accuracy: 0.9869\n",
+            "Epoch 12/25\n",
+            "200/200 [==============================] - 98s 490ms/step - loss: 0.0691 - accuracy: 0.9881\n",
+            "Epoch 13/25\n",
+            "200/200 [==============================] - 95s 476ms/step - loss: 0.0800 - accuracy: 0.9852\n",
+            "Epoch 14/25\n",
+            "200/200 [==============================] - 97s 485ms/step - loss: 0.0762 - accuracy: 0.9867\n",
+            "Epoch 15/25\n",
+            "200/200 [==============================] - 95s 474ms/step - loss: 0.0782 - accuracy: 0.9856\n"
+          ]
+        }
+      ]
+    },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 98, 98, 64)        640       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 49, 49, 64)        0         \n_________________________________________________________________\ndropout (Dropout)            (None, 49, 49, 64)        0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 47, 47, 128)       73856     \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 23, 23, 128)       0         \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 23, 23, 128)       0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 21, 21, 256)       295168    \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 10, 10, 256)       0         \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 10, 10, 256)       0         \n_________________________________________________________________\nflatten (Flatten)            (None, 25600)             0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               3276928   \n_________________________________________________________________\ndense_1 (Dense)              (None, 1)                 129       \n=================================================================\nTotal params: 3,646,721\nTrainable params: 3,646,721\nNon-trainable params: 0\n_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "source": [
+        "model.evaluate(test_data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O2eONxk8d13E",
+        "outputId": "3516c2df-cbbf-471a-b96d-13fe36b85245"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "63/63 [==============================] - 12s 168ms/step - loss: 0.0817 - accuracy: 0.9890\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.08170609176158905, 0.9890000224113464]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Epoch 1/10\n",
-      "450/450 [==============================] - 109s 242ms/step - loss: 0.6960 - accuracy: 0.5104 - val_loss: 0.6850 - val_accuracy: 0.5850\n",
-      "Epoch 2/10\n",
-      "450/450 [==============================] - 108s 240ms/step - loss: 0.6861 - accuracy: 0.5460 - val_loss: 0.6798 - val_accuracy: 0.5487\n",
-      "Epoch 3/10\n",
-      "450/450 [==============================] - 117s 260ms/step - loss: 0.6646 - accuracy: 0.6015 - val_loss: 0.6490 - val_accuracy: 0.6488\n",
-      "Epoch 4/10\n",
-      "450/450 [==============================] - 107s 239ms/step - loss: 0.6168 - accuracy: 0.6612 - val_loss: 0.5729 - val_accuracy: 0.7237\n",
-      "Epoch 5/10\n",
-      "450/450 [==============================] - 102s 227ms/step - loss: 0.5518 - accuracy: 0.7217 - val_loss: 0.5206 - val_accuracy: 0.7375\n",
-      "Epoch 6/10\n",
-      "450/450 [==============================] - 101s 223ms/step - loss: 0.4882 - accuracy: 0.7663 - val_loss: 0.4872 - val_accuracy: 0.7600\n",
-      "Epoch 7/10\n",
-      "450/450 [==============================] - 104s 231ms/step - loss: 0.4341 - accuracy: 0.7953 - val_loss: 0.4526 - val_accuracy: 0.7862\n",
-      "Epoch 8/10\n",
-      "450/450 [==============================] - 105s 232ms/step - loss: 0.3799 - accuracy: 0.8274 - val_loss: 0.4973 - val_accuracy: 0.7875\n",
-      "Epoch 9/10\n",
-      "450/450 [==============================] - 104s 231ms/step - loss: 0.3112 - accuracy: 0.8686 - val_loss: 0.4674 - val_accuracy: 0.8062\n",
-      "Epoch 10/10\n",
-      "450/450 [==============================] - 106s 235ms/step - loss: 0.2457 - accuracy: 0.8964 - val_loss: 0.5202 - val_accuracy: 0.7887\n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "# Using helper function get class names\n",
+        "def get_class_names_from_folder(directory):\n",
+        "  \"\"\"\n",
+        "  Get the classnames from train folder for example\n",
+        "  \"\"\"\n",
+        "  import pathlib\n",
+        "  import numpy as np\n",
+        "  data_dir = pathlib.Path(directory)\n",
+        "  class_names = np.array(sorted([item.name for item in data_dir.glob(\"*\")])) # Created a list of class names\n",
+        "  return class_names\n",
+        "  print(class_names)\n",
+        "class_names = get_class_names_from_folder(directory=\"/content/dog vs cat/dataset/training_set\")\n",
+        "class_names"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q51Gcj-Xth0f",
+        "outputId": "472a2397-2d43-400d-a1e2-b3fee133928f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['cats', 'dogs'], dtype='<U4')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
     },
     {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x2876f6abd60>"
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "pd.DataFrame(history.history).plot()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "jlzSusuyd5ov",
+        "outputId": "0331db68-4f5d-4e4a-f5bb-9e7b3a346f68"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
       ]
-     },
-     "metadata": {},
-     "execution_count": 25
-    }
-   ],
-   "source": [
-    "model.fit(X_train, y_train,batch_size=16,validation_split=0.1, epochs=10)"
-   ]
-  },
-  {
-   "source": [
-    "## Saving the model"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save('my_model.h5')"
-   ]
-  },
-  {
-   "source": [
-    "## Loading the model"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 98, 98, 64)        640       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 49, 49, 64)        0         \n_________________________________________________________________\ndropout (Dropout)            (None, 49, 49, 64)        0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 47, 47, 128)       73856     \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 23, 23, 128)       0         \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 23, 23, 128)       0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 21, 21, 256)       295168    \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 10, 10, 256)       0         \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 10, 10, 256)       0         \n_________________________________________________________________\nflatten (Flatten)            (None, 25600)             0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               3276928   \n_________________________________________________________________\ndense_1 (Dense)              (None, 1)                 129       \n=================================================================\nTotal params: 3,646,721\nTrainable params: 3,646,721\nNon-trainable params: 0\n_________________________________________________________________\n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import classification_report\n",
+        "import numpy as np\n",
+        "\n",
+        "# Get the true labels\n",
+        "y_test = test_data.classes\n",
+        "\n",
+        "# Predict the probabilities from the model\n",
+        "y_pred_probs = model.predict(test_data)\n",
+        "\n",
+        "# Convert prediction probabilities to class labels\n",
+        "y_pred_classes = np.argmax(y_pred_probs, axis=1)\n",
+        "\n",
+        "# Calculate accuracy, precision, recall, f1-score\n",
+        "report = classification_report(y_test, y_pred_classes, target_names=class_names)\n",
+        "\n",
+        "print(report)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "si2K2DqKd96N",
+        "outputId": "60e0bdef-08af-43fe-b690-19877e6eb1e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "63/63 [==============================] - 8s 130ms/step\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "        cats       0.49      0.49      0.49      1000\n",
+            "        dogs       0.49      0.49      0.49      1000\n",
+            "\n",
+            "    accuracy                           0.49      2000\n",
+            "   macro avg       0.49      0.49      0.49      2000\n",
+            "weighted avg       0.49      0.49      0.49      2000\n",
+            "\n"
+          ]
+        }
+      ]
     }
-   ],
-   "source": [
-    "new_model = tf.keras.models.load_model('my_model.h5')\n",
-    "new_model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.4 64-bit"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
+  ]
+}