diff --git "a/ResNETCNNModelV2.ipynb" "b/ResNETCNNModelV2.ipynb"
new file mode 100644--- /dev/null
+++ "b/ResNETCNNModelV2.ipynb"
@@ -0,0 +1,796 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "machine_shape": "hm",
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyNN7oI93+vCC6oQrcsHx6s2",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/MercyMoparthy/CNN-Project/blob/main/model_8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Model 6 on ResNet50V2\n",
+        "\n",
+        "This notebook goes through the following steps to create new model based on ResNet50V2\n",
+        "\n",
+        "\n",
+        "*   Loading ResNet50V2 model from Keras\n",
+        "*   Resizing the images to 128*128 size and learning rate set to 1e-5\n",
+        "*   Data Augementation with Zoom Range as 0.5\n",
+        "*   Using ResNet50V2, with Density 128 and dropout as 0.05\n",
+        "*   Metrics Included Accuracy, F1 Score, Recall and Precise\n",
+        "*   Added Confusion Matrix, changed batch size to 32\n",
+        "*   Added Transfer Learning to unfreezing the weights of the layers\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "0AwohuKl5G55"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Importing Data"
+      ],
+      "metadata": {
+        "id": "zdE7BoBKlbVV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ERRsEFnlT0S"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras.applications import ResNet50V2\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "from tensorflow.keras import layers, models, datasets, utils, optimizers, callbacks, Input\n",
+        "from tensorflow.keras.applications.resnet_v2 import preprocess_input\n",
+        "from tensorflow.keras.datasets import cifar10\n",
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
+        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Check if CUDA is available\n",
+        "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SfZ38e1xvcD5",
+        "outputId": "5cc2479c-aba2-472c-87a5-2d538dd950f9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Num GPUs Available:  1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Sample Data"
+      ],
+      "metadata": {
+        "id": "zXV2E57lnWZX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
+        "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()\n",
+        "# Function to plot sample images\n",
+        "def plot_sample_images(images, labels, class_names, num_images=8):\n",
+        "    plt.figure(figsize=(12, 4))\n",
+        "    for i in range(num_images):\n",
+        "        plt.subplot(1, num_images, i+1)\n",
+        "        plt.xticks([])\n",
+        "        plt.yticks([])\n",
+        "        plt.grid(False)\n",
+        "        plt.imshow(images[i])\n",
+        "        # Labels are arrays like [[6]], so extract integer\n",
+        "        plt.xlabel(class_names[labels[i][0]])\n",
+        "    plt.show()\n",
+        "\n",
+        "# Call the function to plot\n",
+        "plot_sample_images(train_images, train_labels, class_names)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 190
+        },
+        "id": "rQ-JLvckPgfZ",
+        "outputId": "a3636d8c-1e64-4578-9828-64ea0a54c8a6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 0us/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Data Loading and Processing"
+      ],
+      "metadata": {
+        "id": "pLkX0T77mH2L"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
+        "\n",
+        "# Resize images to the input shape expected by ResNet50 (224, 224)\n",
+        "IMG_HEIGHT, IMG_WIDTH = 128, 128\n",
+        "x_train = tf.image.resize(x_train, (IMG_HEIGHT, IMG_WIDTH)).numpy()\n",
+        "x_test = tf.image.resize(x_test, (IMG_HEIGHT, IMG_WIDTH)).numpy()\n",
+        "x_train = preprocess_input(x_train)\n",
+        "x_test = preprocess_input(x_test)\n",
+        "\n",
+        "y_train = tf.keras.utils.to_categorical(y_train, 10)\n",
+        "y_test = tf.keras.utils.to_categorical(y_test, 10)\n",
+        "#x_train = x_train.astype('float32') / 255\n",
+        "#x_test = x_test.astype('float32') / 255\n",
+        "print(\"X Train:\", x_train.shape)\n",
+        "print(\"Y Train:\", y_train.shape)\n",
+        "print(\"X Test:\", x_test.shape)\n",
+        "print(\"Y Test:\", y_test.shape)"
+      ],
+      "metadata": {
+        "id": "BB2dSW2OmJ6R",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "01d477b8-bea4-4edb-c5bc-f7a0bbee9699"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X Train: (50000, 128, 128, 3)\n",
+            "Y Train: (50000, 10)\n",
+            "X Test: (10000, 128, 128, 3)\n",
+            "Y Test: (10000, 10)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Data Augmentation"
+      ],
+      "metadata": {
+        "id": "CcZc7PfHM_yW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Data Augmentation\n",
+        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
+        "    rotation_range=15,\n",
+        "    width_shift_range=0.5,\n",
+        "    height_shift_range=0.5,\n",
+        "    horizontal_flip=True,\n",
+        "    zoom_range=0.5\n",
+        ")\n",
+        "datagen.fit(x_train)"
+      ],
+      "metadata": {
+        "id": "F-T2_iHzNBf5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ResNet Model Architectire"
+      ],
+      "metadata": {
+        "id": "1_wird40mfax"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras import regularizers\n",
+        "num_classes = 10\n",
+        "input_shape = (128, 128, 3)\n",
+        "\n",
+        "# Load ResNet50 base (without the top classification head)\n",
+        "base_model = ResNet50V2(\n",
+        "    weights='imagenet',\n",
+        "    include_top=False,\n",
+        "    input_shape=input_shape\n",
+        ")\n",
+        "base_model.trainable = True  # Freeze the base model\n",
+        "\n",
+        "# Build new model on top of ResNet50 base\n",
+        "model = models.Sequential([\n",
+        "    Input(shape=input_shape),\n",
+        "    layers.GaussianNoise(0.1),\n",
+        "    base_model,\n",
+        "    layers.GlobalAveragePooling2D(),\n",
+        "    layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l2(0.001)),\n",
+        "    layers.Dropout(0.05),\n",
+        "    layers.Dense(num_classes, activation='softmax')\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "Qm2wrSPqmhSJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fd32dd46-dc2d-4eba-88df-eeb7e8e12c15"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+            "\u001b[1m94668760/94668760\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Compile the Model"
+      ],
+      "metadata": {
+        "id": "bDzYRuF-nhGt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.optimizers import RMSprop\n",
+        "for layer in base_model.layers[:60]:  # adjust number based on experimentation\n",
+        "    layer.trainable = True\n",
+        "# Compile\n",
+        "model.compile(optimizer=RMSprop(learning_rate=1e-5),\n",
+        "              loss='categorical_crossentropy',\n",
+        "              metrics=['accuracy'])\n",
+        "\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "l9poYZTQb5JO",
+        "outputId": "c7397011-da6c-43a4-adfd-3f901008ba1f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ gaussian_noise (\u001b[38;5;33mGaussianNoise\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ resnet50v2 (\u001b[38;5;33mFunctional\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m2048\u001b[0m)     │    \u001b[38;5;34m23,564,800\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ global_average_pooling2d        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ gaussian_noise (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussianNoise</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ resnet50v2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)     │    <span style=\"color: #00af00; text-decoration-color: #00af00\">23,564,800</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ global_average_pooling2d        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m23,828,362\u001b[0m (90.90 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">23,828,362</span> (90.90 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m23,782,922\u001b[0m (90.72 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">23,782,922</span> (90.72 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m45,440\u001b[0m (177.50 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,440</span> (177.50 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Model Callbacks"
+      ],
+      "metadata": {
+        "id": "_p50pBtaMYQy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
+        "checkpoint = ModelCheckpoint('best_model.h5', monitor='val_accuracy', save_best_only=True)\n",
+        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, min_lr=1e-6, patience=2)"
+      ],
+      "metadata": {
+        "id": "1-Qov_FScQek"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Train the ResNet Model"
+      ],
+      "metadata": {
+        "id": "h5OjVRbpw5xl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train_new, x_val, y_train_new, y_val = train_test_split(\n",
+        "    x_train, y_train, test_size=0.1, random_state=42\n",
+        ")\n",
+        "\n",
+        "# Train using datagen on x_train_new\n",
+        "history = model.fit(\n",
+        "    datagen.flow(x_train_new, y_train_new, batch_size=32),\n",
+        "    validation_data=(x_val, y_val),\n",
+        "    epochs=20,\n",
+        "    callbacks=[early_stopping, reduce_lr],\n",
+        "    verbose=1\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SEAdG2eXcVRE",
+        "outputId": "5aa94d73-ebe1-4ed0-8a34-1695fcf7fb99"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+            "  self._warn_if_super_not_called()\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 150ms/step - accuracy: 0.2431 - loss: 2.3656 - val_accuracy: 0.0958 - val_loss: 3.9748 - learning_rate: 1.0000e-05\n",
+            "Epoch 2/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 133ms/step - accuracy: 0.5597 - loss: 1.5082 - val_accuracy: 0.0950 - val_loss: 3.1932 - learning_rate: 1.0000e-05\n",
+            "Epoch 3/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 131ms/step - accuracy: 0.6375 - loss: 1.2864 - val_accuracy: 0.1334 - val_loss: 3.1501 - learning_rate: 1.0000e-05\n",
+            "Epoch 4/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 133ms/step - accuracy: 0.6721 - loss: 1.1646 - val_accuracy: 0.3446 - val_loss: 2.2971 - learning_rate: 1.0000e-05\n",
+            "Epoch 5/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 133ms/step - accuracy: 0.7016 - loss: 1.0742 - val_accuracy: 0.5152 - val_loss: 1.7484 - learning_rate: 1.0000e-05\n",
+            "Epoch 6/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 132ms/step - accuracy: 0.7209 - loss: 1.0228 - val_accuracy: 0.6326 - val_loss: 1.4012 - learning_rate: 1.0000e-05\n",
+            "Epoch 7/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 133ms/step - accuracy: 0.7358 - loss: 0.9601 - val_accuracy: 0.7462 - val_loss: 0.9736 - learning_rate: 1.0000e-05\n",
+            "Epoch 8/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 132ms/step - accuracy: 0.7481 - loss: 0.9238 - val_accuracy: 0.7886 - val_loss: 0.8191 - learning_rate: 1.0000e-05\n",
+            "Epoch 9/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 131ms/step - accuracy: 0.7566 - loss: 0.8960 - val_accuracy: 0.8140 - val_loss: 0.7947 - learning_rate: 1.0000e-05\n",
+            "Epoch 10/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m189s\u001b[0m 134ms/step - accuracy: 0.7628 - loss: 0.8748 - val_accuracy: 0.8316 - val_loss: 0.7824 - learning_rate: 1.0000e-05\n",
+            "Epoch 11/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 132ms/step - accuracy: 0.7744 - loss: 0.8431 - val_accuracy: 0.8470 - val_loss: 0.7213 - learning_rate: 1.0000e-05\n",
+            "Epoch 12/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 130ms/step - accuracy: 0.7776 - loss: 0.8185 - val_accuracy: 0.8550 - val_loss: 0.6987 - learning_rate: 1.0000e-05\n",
+            "Epoch 13/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 135ms/step - accuracy: 0.7816 - loss: 0.8024 - val_accuracy: 0.8724 - val_loss: 0.6323 - learning_rate: 1.0000e-05\n",
+            "Epoch 14/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 138ms/step - accuracy: 0.7850 - loss: 0.7929 - val_accuracy: 0.8756 - val_loss: 0.6424 - learning_rate: 1.0000e-05\n",
+            "Epoch 15/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 132ms/step - accuracy: 0.7942 - loss: 0.7648 - val_accuracy: 0.8880 - val_loss: 0.5758 - learning_rate: 1.0000e-05\n",
+            "Epoch 16/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 134ms/step - accuracy: 0.7977 - loss: 0.7505 - val_accuracy: 0.8922 - val_loss: 0.6297 - learning_rate: 1.0000e-05\n",
+            "Epoch 17/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 130ms/step - accuracy: 0.8009 - loss: 0.7261 - val_accuracy: 0.9002 - val_loss: 0.8421 - learning_rate: 1.0000e-05\n",
+            "Epoch 18/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 131ms/step - accuracy: 0.8090 - loss: 0.7184 - val_accuracy: 0.8976 - val_loss: 0.8489 - learning_rate: 2.0000e-06\n",
+            "Epoch 19/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 132ms/step - accuracy: 0.8089 - loss: 0.7085 - val_accuracy: 0.9002 - val_loss: 0.8094 - learning_rate: 2.0000e-06\n",
+            "Epoch 20/20\n",
+            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 132ms/step - accuracy: 0.8118 - loss: 0.7036 - val_accuracy: 0.8992 - val_loss: 0.7665 - learning_rate: 1.0000e-06\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Evaluate the ResNet Model"
+      ],
+      "metadata": {
+        "id": "SrxEp8GgqI5Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(12,5))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(history.history['loss'], label='Train')\n",
+        "plt.plot(history.history['val_loss'], label='Validation')\n",
+        "plt.title('Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(history.history['accuracy'], label='Train')\n",
+        "plt.plot(history.history['val_accuracy'], label='Validation')\n",
+        "plt.title('Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "HsNDbaFlqLN_",
+        "outputId": "3329c454-4c32-4d06-8cc4-321ab96992c4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Sample Visualization with Predictions"
+      ],
+      "metadata": {
+        "id": "o5dpM8-nqRQI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+        "import numpy as np\n",
+        "\n",
+        "y_pred = model.predict(x_test)\n",
+        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+        "\n",
+        "# Convert y_test from one-hot encoding back to integer labels\n",
+        "y_true = np.argmax(y_test, axis=1)\n",
+        "\n",
+        "print(classification_report(y_true, y_pred_classes, target_names=[\n",
+        "    'airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck'\n",
+        "]))\n",
+        "\n",
+        "cm = confusion_matrix(y_true, y_pred_classes)\n",
+        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\n",
+        "    'airplane','auto','bird','cat','deer','dog','frog','horse','ship','truck'\n",
+        "])\n",
+        "disp.plot(cmap='Blues', xticks_rotation=45)\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 820
+        },
+        "id": "5nQjEr32qUXn",
+        "outputId": "94e932c9-be6e-492d-8159-40f63cc888ad"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 42ms/step\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.79      0.96      0.87      1000\n",
+            "  automobile       0.92      0.96      0.94      1000\n",
+            "        bird       0.86      0.91      0.89      1000\n",
+            "         cat       0.87      0.79      0.83      1000\n",
+            "        deer       0.95      0.82      0.88      1000\n",
+            "         dog       0.88      0.83      0.86      1000\n",
+            "        frog       0.94      0.95      0.95      1000\n",
+            "       horse       0.87      0.97      0.92      1000\n",
+            "        ship       0.96      0.79      0.86      1000\n",
+            "       truck       0.92      0.94      0.93      1000\n",
+            "\n",
+            "    accuracy                           0.89     10000\n",
+            "   macro avg       0.90      0.89      0.89     10000\n",
+            "weighted avg       0.90      0.89      0.89     10000\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Metrics Score"
+      ],
+      "metadata": {
+        "id": "UXLddjvykqFg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+        "# Overall Accuracy\n",
+        "overall_accuracy = accuracy_score(y_true, y_pred_classes)\n",
+        "overall_precision = precision_score(y_true, y_pred_classes, average='weighted')\n",
+        "overall_recall = recall_score(y_true, y_pred_classes, average='weighted')\n",
+        "overall_f1 = f1_score(y_true, y_pred_classes, average='weighted')\n",
+        "\n",
+        "\n",
+        "print(f\"Precision: {overall_precision}\")\n",
+        "print(f\"Recall: {overall_recall}\")\n",
+        "print(f\"F1-Score: {overall_f1}\")\n",
+        "print(f\"Accuracy Score: {overall_accuracy}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Mt_S_C0TkMsJ",
+        "outputId": "2de383b1-d7f1-4c66-af20-2647b51ec1e7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Precision: 0.896494216710947\n",
+            "Recall: 0.8923\n",
+            "F1-Score: 0.891532707362809\n",
+            "Accuracy Score: 0.8923\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## MODEL Architecture as PNG"
+      ],
+      "metadata": {
+        "id": "Ziz40sbKBehG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install pydotplus graphviz\n",
+        "from tensorflow.keras.utils import plot_model\n",
+        "import pydot # pydot is a dependency for plot_model\n",
+        "import graphviz # graphviz is another dependency\n",
+        "\n",
+        "# Ensure pydot and graphviz are installed\n",
+        "try:\n",
+        "    # This will try to import the necessary components\n",
+        "    pydot.graph_from_dot_data('digraph G {}')\n",
+        "    graphviz.Digraph().render('test_render', view=False, cleanup=True, format='png')\n",
+        "except (pydot.InvocationException, graphviz.backend.execute.ExecutableNotFound, graphviz.backend.execute.P2jError) as e:\n",
+        "    print(f\"Graph visualization utilities not fully installed. Please install graphviz and pydotplus.\")\n",
+        "    print(f\"You might need to install graphviz system package as well.\")\n",
+        "    print(f\"Error: {e}\")\n",
+        "\n",
+        "plot_model(model,\n",
+        "           to_file='model_architecture.png',\n",
+        "           show_shapes=True,\n",
+        "           show_layer_names=True,\n",
+        "           # show_layer_activations=True # Uncomment if using TF 2.9+ and want to see activations\n",
+        "          )\n",
+        "\n",
+        "print(\"Model architecture saved as 'model_architecture.png'\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ts62MalWiGB9",
+        "outputId": "f8755336-9ecc-4574-eeaf-bdf4d414a20b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pydotplus in /usr/local/lib/python3.11/dist-packages (2.0.2)\n",
+            "Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (0.20.3)\n",
+            "Requirement already satisfied: pyparsing>=2.0.1 in /usr/local/lib/python3.11/dist-packages (from pydotplus) (3.2.3)\n",
+            "Model architecture saved as 'model_architecture.png'\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file