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diff --git a/My Project/Project_1_DeepLearning_ImageCassification_CNN.ipynb b/My Project/Project_1_DeepLearning_ImageCassification_CNN.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r4rCKcndPybL"
+      },
+      "source": [
+        "# Proyect 1 : Image Classification using Convolutional Neural Networks\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9wkicuxZdrdq"
+      },
+      "source": [
+        "# **Selected dataset: CIFAR-10**\n",
+        "\n",
+        "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. More information about CIFAR-10 can be found [here](https://www.cs.toronto.edu/~kriz/cifar.html).\n",
+        "\n",
+        "In Keras, the CIFAR-10 dataset is also preloaded in the form of four Numpy arrays. x_train and y_train contain the training set, while x_test and y_test contain the test data. The images are encoded as Numpy arrays and their corresponding labels ranging from 0 to 9."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "id": "mPe-wVffoP33",
+        "outputId": "af3cd07b-2aa2-441f-cdee-c2682d8fac5b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training data shape: (50000, 32, 32, 3)\n",
+            "Test data shape: (10000, 32, 32, 3)\n",
+            "Training labels shape: (50000, 1)\n",
+            "y_train data shape after encoding: (50000, 10)\n",
+            "y_test data shape after encoding: (10000, 10)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1500x500 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Import libraries\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Load CIFAR-10 dataset from Keras\n",
+        "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
+        "\n",
+        "# Inspect data shapes\n",
+        "print(f\"Training data shape: {X_train.shape}\")\n",
+        "print(f\"Test data shape: {X_test.shape}\")\n",
+        "print(f\"Training labels shape: {y_train.shape}\")\n",
+        "\n",
+        "# Normalize images to [0,1]\n",
+        "X_train = X_train.astype('float32') / 255.0\n",
+        "X_test = X_test.astype('float32') / 255.0\n",
+        "\n",
+        "# Convert labels to one-hot encoding\n",
+        "y_train = to_categorical(y_train, num_classes=10)\n",
+        "y_test = to_categorical(y_test, num_classes=10)\n",
+        "\n",
+        "# Inspect data shapes\n",
+        "print(f\"y_train data shape after encoding: {y_train.shape}\")\n",
+        "print(f\"y_test data shape after encoding: {y_test.shape}\")\n",
+        "\n",
+        "# Visualize some examples\n",
+        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 5, figsize=(15, 5))\n",
+        "for i in range(5):\n",
+        "    ax = axes[i]\n",
+        "    idx = np.random.randint(0, X_train.shape[0])\n",
+        "    ax.imshow(X_train[idx])\n",
+        "    ax.set_title(class_names[np.argmax(y_train[idx])])\n",
+        "    ax.axis('off')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2ER5WlMNRydp"
+      },
+      "source": [
+        "## Initial Model definition:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WfWCHxh8HGhN"
+      },
+      "outputs": [],
+      "source": [
+        "# Import libraries\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iSN6riPISBMG",
+        "outputId": "2205a144-e67e-46af-9534-97cab40bc0aa"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\larry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define the model\n",
+        "model = Sequential()\n",
+        "\n",
+        "# First convolutional block\n",
+        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
+        "model.add(MaxPooling2D((2, 2)))\n",
+        "model.add(Dropout(0.25))\n",
+        "\n",
+        "# Second convolutional block\n",
+        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+        "model.add(MaxPooling2D((2, 2)))\n",
+        "model.add(Dropout(0.25))\n",
+        "\n",
+        "# Flatten and fully connected layers\n",
+        "model.add(Flatten()) # This ensures the output is 1D\n",
+        "model.add(Dense(128, activation='relu'))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(10, activation='softmax'))  # 10 output classes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 306
+        },
+        "id": "sHHy_SA71fFO",
+        "outputId": "b5291217-1dfa-488c-d8c8-3f3b0064193e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "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_1\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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",
+              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (<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\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (<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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<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\">295,040</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_5 (<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_3 (<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"
+            ],
+            "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",
+              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m295,040\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_5 (\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_3 (\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"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">315,722</span> (1.20 MB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m315,722\u001b[0m (1.20 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">315,722</span> (1.20 MB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m315,722\u001b[0m (1.20 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PGtivbQJT39U"
+      },
+      "source": [
+        "Compilation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hn8UzPBZugVp"
+      },
+      "outputs": [],
+      "source": [
+        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AYl58GOzoP37"
+      },
+      "source": [
+        "Training:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JVBfy9En1fFP",
+        "outputId": "c06420cc-8008-4fff-a571-5ada1dad3cf7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 36ms/step - accuracy: 0.6602 - loss: 0.9670 - val_accuracy: 0.7405 - val_loss: 0.7578\n",
+            "Epoch 2/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 31ms/step - accuracy: 0.6667 - loss: 0.9505 - val_accuracy: 0.7421 - val_loss: 0.7538\n",
+            "Epoch 3/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 31ms/step - accuracy: 0.6691 - loss: 0.9460 - val_accuracy: 0.7370 - val_loss: 0.7662\n",
+            "Epoch 4/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 32ms/step - accuracy: 0.6717 - loss: 0.9254 - val_accuracy: 0.7379 - val_loss: 0.7714\n",
+            "Epoch 5/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 32ms/step - accuracy: 0.6805 - loss: 0.9113 - val_accuracy: 0.7308 - val_loss: 0.7718\n",
+            "Epoch 6/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 32ms/step - accuracy: 0.6813 - loss: 0.8992 - val_accuracy: 0.7378 - val_loss: 0.7709\n",
+            "Epoch 7/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 32ms/step - accuracy: 0.6857 - loss: 0.8919 - val_accuracy: 0.7398 - val_loss: 0.7642\n",
+            "Epoch 8/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 33ms/step - accuracy: 0.6829 - loss: 0.8888 - val_accuracy: 0.7398 - val_loss: 0.7624\n",
+            "Epoch 9/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 32ms/step - accuracy: 0.6953 - loss: 0.8637 - val_accuracy: 0.7407 - val_loss: 0.7506\n",
+            "Epoch 10/10\n",
+            "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 32ms/step - accuracy: 0.6995 - loss: 0.8515 - val_accuracy: 0.7373 - val_loss: 0.7558\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Train\n",
+        "#history = model.fit(X_train, y_train, batch_size=32, epochs=10, validation_data=(X_test, y_test))\n",
+        "history = model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uS-PD075oP38"
+      },
+      "source": [
+        "**Metrics Calculation**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "i3pgR0KGoP38",
+        "outputId": "ae5eee67-e0c4-47c9-e962-57c757fa23a5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - accuracy: 0.7246 - loss: 0.8158\n",
+            "Test loss: 0.8197579979896545\n",
+            "Test accuracy: 0.7207000255584717\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Accuracy and Loss:\n",
+        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
+        "print('Test loss:', test_loss)\n",
+        "print('Test accuracy:', test_acc)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hhP2d8jboP38",
+        "outputId": "18e666d6-9a50-4db6-f98a-8544dd29eeb5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.77      0.76      0.76      1000\n",
+            "  automobile       0.87      0.83      0.85      1000\n",
+            "        bird       0.66      0.52      0.58      1000\n",
+            "         cat       0.54      0.49      0.51      1000\n",
+            "        deer       0.60      0.73      0.66      1000\n",
+            "         dog       0.64      0.61      0.63      1000\n",
+            "        frog       0.72      0.86      0.78      1000\n",
+            "       horse       0.82      0.72      0.77      1000\n",
+            "        ship       0.80      0.85      0.83      1000\n",
+            "       truck       0.80      0.83      0.81      1000\n",
+            "\n",
+            "    accuracy                           0.72     10000\n",
+            "   macro avg       0.72      0.72      0.72     10000\n",
+            "weighted avg       0.72      0.72      0.72     10000\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Confusion Matrix')"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+        "import numpy as np\n",
+        "\n",
+        "# Get predictions from the model\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "# Convert one-hot encoded predictions and labels to single class indices\n",
+        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+        "y_true_classes = np.argmax(y_test, axis=1)\n",
+        "\n",
+        "# Generate classification report\n",
+        "report = classification_report(y_true_classes, y_pred_classes, target_names=[\n",
+        "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
+        "])\n",
+        "\n",
+        "print(report)\n",
+        "\n",
+        "# Generate confusion matrix\n",
+        "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
+        "\n",
+        "# Display confusion matrix\n",
+        "cmd = ConfusionMatrixDisplay(cm, display_labels=[\n",
+        "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
+        "])\n",
+        "cmd.plot(cmap='viridis', xticks_rotation='vertical')\n",
+        "cmd.ax_.set_title(\"Confusion Matrix\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qMR0Ok071fFP"
+      },
+      "source": [
+        "Save Initial Trained Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KUIMh-nZoP39"
+      },
+      "outputs": [],
+      "source": [
+        "model.save('model_initial.keras')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G2mrWK5hSB_o"
+      },
+      "source": [
+        "## Defining Deeper Models:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "A80vLxW9FIek"
+      },
+      "outputs": [],
+      "source": [
+        "from keras.backend import clear_session\n",
+        "clear_session()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 734
+        },
+        "id": "-B27CnDkoP3-",
+        "outputId": "08747315-9432-4db1-efae-6f6b0cd2adbf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "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"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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",
+              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (<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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</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\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_2           │ (<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\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_2 (<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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,656</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (<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\">512</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\">5,130</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "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",
+              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m1,792\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
+              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
+              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m147,584\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m262,656\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\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;34m5,130\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">492,298</span> (1.88 MB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m492,298\u001b[0m (1.88 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">491,658</span> (1.88 MB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m491,658\u001b[0m (1.88 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">640</span> (2.50 KB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m640\u001b[0m (2.50 KB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Import libraries\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
+        "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
+        "\n",
+        "# Define the model\n",
+        "model_refined = Sequential()\n",
+        "\n",
+        "# First convolutional block\n",
+        "model_refined.add(Conv2D(64, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
+        "model_refined.add(BatchNormalization())\n",
+        "model_refined.add(MaxPooling2D((2, 2)))\n",
+        "model_refined.add(Dropout(0.25))\n",
+        "\n",
+        "# Second convolutional block\n",
+        "model_refined.add(Conv2D(128, (3, 3), activation='relu'))\n",
+        "model_refined.add(BatchNormalization())\n",
+        "model_refined.add(MaxPooling2D((2, 2)))\n",
+        "model_refined.add(Dropout(0.25))\n",
+        "\n",
+        "# Third convolutional block\n",
+        "model_refined.add(Conv2D(128, (3, 3), activation='relu'))\n",
+        "model_refined.add(BatchNormalization())\n",
+        "model_refined.add(MaxPooling2D((2, 2)))\n",
+        "model_refined.add(Dropout(0.4))\n",
+        "\n",
+        "# Flatten and fully connected layers\n",
+        "model_refined.add(Flatten()) # This ensures the output is 1D\n",
+        "model_refined.add(Dense(512, activation='relu'))\n",
+        "model_refined.add(Dropout(0.5))\n",
+        "model_refined.add(Dense(10, activation='softmax'))  # 10 output classes\n",
+        "\n",
+        "# Define optimizer with learning rate\n",
+        "learning_rate = 0.0005\n",
+        "optimizer = Adam(learning_rate=learning_rate)\n",
+        "\n",
+        "#Compilation:\n",
+        "model_refined.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Define ReduceLROnPlateau callback\n",
+        "# Parameters description: monitor=>Metric to monitor, factor=>Factor by which the learning rate will be reduced, patience=>Number of epochs with no improvement to wait, and min_lr=>Minimum learning rate.\n",
+        "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-5)\n",
+        "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
+        "\n",
+        "#Display Model Summary\n",
+        "model_refined.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a20oib29oP3-",
+        "outputId": "4c82107e-5f21-47b3-9a2d-1b43a5fc7311"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 150ms/step - accuracy: 0.2803 - loss: 2.2839 - val_accuracy: 0.3575 - val_loss: 2.2896 - learning_rate: 5.0000e-04\n",
+            "Epoch 2/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 137ms/step - accuracy: 0.4655 - loss: 1.4733 - val_accuracy: 0.4730 - val_loss: 1.6386 - learning_rate: 5.0000e-04\n",
+            "Epoch 3/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 136ms/step - accuracy: 0.5370 - loss: 1.2867 - val_accuracy: 0.5711 - val_loss: 1.2776 - learning_rate: 5.0000e-04\n",
+            "Epoch 4/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 138ms/step - accuracy: 0.5845 - loss: 1.1696 - val_accuracy: 0.6432 - val_loss: 1.0084 - learning_rate: 5.0000e-04\n",
+            "Epoch 5/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 138ms/step - accuracy: 0.6212 - loss: 1.0688 - val_accuracy: 0.5792 - val_loss: 1.2779 - learning_rate: 5.0000e-04\n",
+            "Epoch 6/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 137ms/step - accuracy: 0.6390 - loss: 1.0151 - val_accuracy: 0.6361 - val_loss: 1.0553 - learning_rate: 5.0000e-04\n",
+            "Epoch 7/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 138ms/step - accuracy: 0.6666 - loss: 0.9558 - val_accuracy: 0.6550 - val_loss: 1.0439 - learning_rate: 5.0000e-04\n",
+            "Epoch 8/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 140ms/step - accuracy: 0.6837 - loss: 0.8875 - val_accuracy: 0.7173 - val_loss: 0.8013 - learning_rate: 2.5000e-04\n",
+            "Epoch 9/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 141ms/step - accuracy: 0.7048 - loss: 0.8403 - val_accuracy: 0.7109 - val_loss: 0.8337 - learning_rate: 2.5000e-04\n",
+            "Epoch 10/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 141ms/step - accuracy: 0.7065 - loss: 0.8225 - val_accuracy: 0.7287 - val_loss: 0.7906 - learning_rate: 2.5000e-04\n",
+            "Epoch 11/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 141ms/step - accuracy: 0.7178 - loss: 0.7885 - val_accuracy: 0.7025 - val_loss: 0.8597 - learning_rate: 2.5000e-04\n",
+            "Epoch 12/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 140ms/step - accuracy: 0.7292 - loss: 0.7711 - val_accuracy: 0.7281 - val_loss: 0.7873 - learning_rate: 2.5000e-04\n",
+            "Epoch 13/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 136ms/step - accuracy: 0.7314 - loss: 0.7583 - val_accuracy: 0.7638 - val_loss: 0.6795 - learning_rate: 2.5000e-04\n",
+            "Epoch 14/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 136ms/step - accuracy: 0.7416 - loss: 0.7335 - val_accuracy: 0.7284 - val_loss: 0.7854 - learning_rate: 2.5000e-04\n",
+            "Epoch 15/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 138ms/step - accuracy: 0.7512 - loss: 0.7084 - val_accuracy: 0.7411 - val_loss: 0.7477 - learning_rate: 2.5000e-04\n",
+            "Epoch 16/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 136ms/step - accuracy: 0.7541 - loss: 0.6954 - val_accuracy: 0.7606 - val_loss: 0.6935 - learning_rate: 2.5000e-04\n",
+            "Epoch 17/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 137ms/step - accuracy: 0.7582 - loss: 0.6839 - val_accuracy: 0.7790 - val_loss: 0.6380 - learning_rate: 1.2500e-04\n",
+            "Epoch 18/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 135ms/step - accuracy: 0.7637 - loss: 0.6589 - val_accuracy: 0.7849 - val_loss: 0.6156 - learning_rate: 1.2500e-04\n",
+            "Epoch 19/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 138ms/step - accuracy: 0.7706 - loss: 0.6558 - val_accuracy: 0.7702 - val_loss: 0.6648 - learning_rate: 1.2500e-04\n",
+            "Epoch 20/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 137ms/step - accuracy: 0.7752 - loss: 0.6317 - val_accuracy: 0.7874 - val_loss: 0.6187 - learning_rate: 1.2500e-04\n",
+            "Epoch 21/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 136ms/step - accuracy: 0.7795 - loss: 0.6310 - val_accuracy: 0.7798 - val_loss: 0.6414 - learning_rate: 1.2500e-04\n",
+            "Epoch 22/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 137ms/step - accuracy: 0.7799 - loss: 0.6230 - val_accuracy: 0.7839 - val_loss: 0.6270 - learning_rate: 6.2500e-05\n",
+            "Epoch 23/100\n",
+            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 142ms/step - accuracy: 0.7851 - loss: 0.6096 - val_accuracy: 0.7835 - val_loss: 0.6399 - learning_rate: 6.2500e-05\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Train\n",
+        "history_refined = model_refined.fit(X_train, y_train, batch_size=64, epochs=100, validation_split=0.2, callbacks=[early_stopping, lr_scheduler])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vHeANqjdoP3-",
+        "outputId": "fa6912c1-2dc3-48f7-94f3-8921ef459ea2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 28ms/step - accuracy: 0.7841 - loss: 0.6279\n",
+            "Test loss: 0.6289911866188049\n",
+            "Test accuracy: 0.7825000286102295\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Accuracy and Loss:\n",
+        "test_loss, test_acc = model_refined.evaluate(X_test, y_test)\n",
+        "print('Test loss:', test_loss)\n",
+        "print('Test accuracy:', test_acc)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 866
+        },
+        "id": "5T1dS_ujoP3-",
+        "outputId": "8c896900-b5cc-4cbc-93ec-ae3831c0948c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.85      0.80      0.82      1000\n",
+            "  automobile       0.92      0.88      0.90      1000\n",
+            "        bird       0.75      0.71      0.73      1000\n",
+            "         cat       0.68      0.62      0.65      1000\n",
+            "        deer       0.75      0.83      0.79      1000\n",
+            "         dog       0.76      0.69      0.72      1000\n",
+            "        frog       0.82      0.89      0.85      1000\n",
+            "       horse       0.85      0.84      0.85      1000\n",
+            "        ship       0.83      0.92      0.87      1000\n",
+            "       truck       0.86      0.88      0.87      1000\n",
+            "\n",
+            "    accuracy                           0.81     10000\n",
+            "   macro avg       0.81      0.81      0.81     10000\n",
+            "weighted avg       0.81      0.81      0.81     10000\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Confusion Matrix')"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+        "import numpy as np\n",
+        "\n",
+        "# Get predictions from the model\n",
+        "y_pred = model_refined.predict(X_test)\n",
+        "\n",
+        "# Convert one-hot encoded predictions and labels to single class indices\n",
+        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+        "y_true_classes = np.argmax(y_test, axis=1)\n",
+        "\n",
+        "# Generate classification report\n",
+        "report = classification_report(y_true_classes, y_pred_classes, target_names=[\n",
+        "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
+        "])\n",
+        "\n",
+        "print(report)\n",
+        "\n",
+        "# Generate confusion matrix\n",
+        "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
+        "\n",
+        "# Display confusion matrix\n",
+        "cmd = ConfusionMatrixDisplay(cm, display_labels=[\n",
+        "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
+        "])\n",
+        "cmd.plot(cmap='viridis', xticks_rotation='vertical')\n",
+        "cmd.ax_.set_title(\"Confusion Matrix\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "GCjPGxl54Vjl",
+        "outputId": "4d921ea9-5532-4986-b1ee-377f977dae23"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.subplot(211)\n",
+        "plt.title('Cross Entropy Loss')\n",
+        "plt.plot(history_refined.history['loss'], color='blue', label='train')\n",
+        "plt.plot(history_refined.history['val_loss'], color='red', label='val')\n",
+        "\n",
+        "# plot accuracy\n",
+        "plt.subplot(212)\n",
+        "plt.title('Classification Accuracy')\n",
+        "plt.plot(history_refined.history['accuracy'], color='green', label='train')\n",
+        "plt.plot(history_refined.history['val_accuracy'], color='red', label='val')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "8QWtslcU4ILy"
+      },
+      "outputs": [],
+      "source": [
+        "model_refined.save('model_final.keras')"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.11.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/My Project/model_final.keras b/My Project/model_final.keras
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diff --git a/My Project/model_initial.keras b/My Project/model_initial.keras
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index 00000000..bbb0c696
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diff --git a/gitignore.txt b/gitignore.txt
new file mode 100644
index 00000000..5af31a19
--- /dev/null
+++ b/gitignore.txt
@@ -0,0 +1,5 @@
+images/
+myenv/
+image-prediction/
+processed/
+/uploads
diff --git a/image-classification-webapp/README.md b/image-classification-webapp/README.md
new file mode 100644
index 00000000..2c8da300
--- /dev/null
+++ b/image-classification-webapp/README.md
@@ -0,0 +1,93 @@
+# Image Classification Web App
+
+## Overview
+This web application allows users to classify images into predefined categories using a Convolutional Neural Network (CNN). Users can upload one or multiple images, view their preprocessed versions, and receive predictions with associated probabilities. The application integrates a custom-trained deep learning model hosted on Google Cloud's Vertex AI platform.
+
+## Features
+- **Image Upload**: Users can upload one or multiple images through a web interface.
+- **Predictions**: The app displays the predicted class labels and associated probabilities.
+- **Preprocessed Image Display**: Shows the preprocessed version of uploaded images to illustrate how the model interprets input data.
+
+## Technology Stack
+### Frontend
+- **HTML**: Provides the structure for web pages.
+- **HTMX**: Enables dynamic updates without full-page reloads.
+- **Tailwind CSS**: Ensures a clean and professional design.
+
+### Backend
+- **Flask (Python)**: Handles image uploads, preprocessing, and API requests.
+
+### Model API
+- **Google AI Platform**: Hosts the trained CNN model and provides an endpoint for making predictions.
+
+## Workflow
+1. **User Interaction**:
+    - Users visit the web application and upload images.
+2. **Image Preprocessing**:
+    - Images are resized to 32x32 pixels and normalized to match the model's input requirements.
+3. **Model Prediction**:
+    - Preprocessed images are sent to the Google AI Platform endpoint for prediction.
+    - Predictions include class labels and their probabilities.
+4. **Results Display**:
+    - The app displays predictions and preprocessed images.
+
+## Installation and Setup
+
+### Prerequisites
+- Python 3.11 or later
+- Google Cloud SDK (configured with service account credentials)
+- Docker (optional, for local TensorFlow Serving)
+
+### Steps
+1. **Clone the Repository**:
+   ```bash
+   git clone <repository-url>
+   cd image-classification-webapp
+   ```
+
+2. **Install Dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Set Environment Variables**:
+   Configure Google Cloud credentials:
+   ```bash
+   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-key.json"
+   ```
+
+4. **Run the Application**:
+   ```bash
+   flask run
+   ```
+   The application will be accessible at `http://127.0.0.1:5000`.
+
+## Usage
+1. Navigate to the homepage.
+2. Upload one or multiple images using the provided form.
+3. View predictions and preprocessed images on the results page.
+
+## Deployment
+The application can be deployed to any server or cloud platform that supports Flask. For production use:
+- Use a WSGI server like Gunicorn.
+- Configure HTTPS.
+
+## Folder Structure
+```
+image-classification-webapp/
+├── app.py                # Main application script
+├── static/               # Static files (CSS, JS, images)
+├── templates/            # HTML templates
+├── requirements.txt      # Python dependencies
+└── README.md             # Project documentation
+```
+
+## Acknowledgments
+- **TensorFlow**: For providing tools to preprocess data and train models.
+- **Google Cloud Vertex AI**: For hosting the model.
+- **Flask**: For powering the web application backend.
+- **Tailwind CSS**: For the frontend design.
+
+## License
+This project is licensed under the MIT License.
+
diff --git a/image-classification-webapp/app.py b/image-classification-webapp/app.py
new file mode 100644
index 00000000..ec3d3244
--- /dev/null
+++ b/image-classification-webapp/app.py
@@ -0,0 +1,103 @@
+import os
+import json
+import numpy as np
+from flask import Flask, request, render_template, jsonify, send_from_directory
+from werkzeug.utils import secure_filename
+from tensorflow.keras.preprocessing import image
+from google.auth import default
+from google.auth.transport.requests import Request
+import requests
+from PIL import Image
+
+# Flask app setup
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+app.config['PROCESSED_FOLDER'] = 'processed'
+
+# Ensure the upload and processed directories exist
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+os.makedirs(app.config['PROCESSED_FOLDER'], exist_ok=True)
+
+# Function to preprocess the image
+def preprocess_image(img_path, target_size=(32, 32)):
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)  # (32, 32, 3)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension -> (1, 32, 32, 3)
+    img_array /= 255.0  # Normalize to [0, 1] range
+
+    # Save the preprocessed image for visualization
+    processed_img_path = os.path.join(app.config['PROCESSED_FOLDER'], os.path.basename(img_path))
+    img_resized = Image.fromarray((img_array[0] * 255).astype(np.uint8))
+    img_resized.save(processed_img_path)
+
+    return img_array, processed_img_path
+
+# Function to send prediction request
+def predict_image(img_path):
+    # Preprocess the image
+    img_array, processed_img_path = preprocess_image(img_path)
+    input_data = img_array.tolist()
+
+    # Prepare the payload
+    data = json.dumps({"instances": input_data})
+    url = 'https://us-east1-aiplatform.googleapis.com/v1/projects/10609508497/locations/us-east1/endpoints/2868357556030406656:predict'
+
+    # Authenticate using service account credentials
+    credentials, project = default()
+    credentials.refresh(Request())
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {credentials.token}"
+    }
+
+    # Send the request
+    response = requests.post(url, headers=headers, data=data)
+    response_data = response.json()
+
+    # Parse predictions
+    if 'predictions' in response_data:
+        predictions = response_data['predictions'][0]
+        return predictions, processed_img_path
+    else:
+        return {"error": response_data.get("error", "Unknown error")}, processed_img_path
+
+# Class names
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/upload', methods=['POST'])
+def upload():
+    images = request.files.getlist('images')
+    results = []
+
+    for img in images:
+        filename = secure_filename(img.filename)
+        img_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        img.save(img_path)
+        predictions, processed_img_path = predict_image(img_path)
+
+        if isinstance(predictions, dict) and "error" in predictions:
+            results.append({"filename": filename, "error": predictions["error"]})
+        else:
+            probabilities = {class_names[i]: float(prob) for i, prob in enumerate(predictions)}
+            predicted_class = class_names[np.argmax(predictions)]
+            results.append({
+                "filename": filename,
+                "processed_image": os.path.basename(processed_img_path),  # Use basename here
+                "predicted_class": predicted_class,
+                "probabilities": probabilities
+            })
+
+    return render_template('results.html', results=results)
+
+
+@app.route('/processed/<filename>')
+def processed_image(filename):
+    return send_from_directory(app.config['PROCESSED_FOLDER'], filename)
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/image-classification-webapp/gitignore.txt b/image-classification-webapp/gitignore.txt
new file mode 100644
index 00000000..e04bb3b9
--- /dev/null
+++ b/image-classification-webapp/gitignore.txt
@@ -0,0 +1,2 @@
+processed/
+uploads/
\ No newline at end of file
diff --git a/image-classification-webapp/templates/base.html b/image-classification-webapp/templates/base.html
new file mode 100644
index 00000000..d086f062
--- /dev/null
+++ b/image-classification-webapp/templates/base.html
@@ -0,0 +1,18 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Image Classification</title>
+    <script src="https://cdn.jsdelivr.net/npm/htmx.org"></script>
+    <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
+</head>
+
+<body class="bg-gray-100">
+    <div class="container mx-auto p-5">
+        {% block content %}{% endblock %}
+    </div>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/image-classification-webapp/templates/index.html b/image-classification-webapp/templates/index.html
new file mode 100644
index 00000000..01cf16a9
--- /dev/null
+++ b/image-classification-webapp/templates/index.html
@@ -0,0 +1,11 @@
+{% extends "base.html" %}
+{% block content %}
+<div class="bg-white p-6 rounded shadow-md">
+    <h1 class="text-2xl font-bold mb-4">Upload Images for Classification</h1>
+    <form action="/upload" method="post" enctype="multipart/form-data" hx-post="/upload" hx-target="#results">
+        <input type="file" name="images" multiple accept="image/*" class="mb-4">
+        <button type="submit" class="bg-blue-500 text-white px-4 py-2 rounded">Upload</button>
+    </form>
+    <div id="results" class="mt-5"></div>
+</div>
+{% endblock %}
\ No newline at end of file
diff --git a/image-classification-webapp/templates/results.html b/image-classification-webapp/templates/results.html
new file mode 100644
index 00000000..b9b26518
--- /dev/null
+++ b/image-classification-webapp/templates/results.html
@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Results</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+</head>
+
+<body class="bg-gray-100">
+    <div class="container mx-auto p-4">
+        <h1 class="text-2xl font-bold text-center mb-4">Image Classification Results</h1>
+        <div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
+            {% for result in results %}
+            <div class="bg-white rounded-lg shadow p-4">
+                <h2 class="text-lg font-bold">{{ result.filename }}</h2>
+                {% if result.error %}
+                <p class="text-red-500">{{ result.error }}</p>
+                {% else %}
+                <img class="w-full h-32 object-cover rounded my-2" src="/processed/{{ result.processed_image }}"
+                    alt="Preprocessed Image">
+                <p class="text-green-500 font-semibold">Predicted Class: {{ result.predicted_class }}</p>
+                <h3 class="font-bold mt-2">Probabilities:</h3>
+                <ul>
+                    {% for class_name, prob in result.probabilities.items() %}
+                    <li>{{ class_name }}: {{ prob|round(3) }}</li>
+                    {% endfor %}
+                </ul>
+                {% endif %}
+            </div>
+            {% endfor %}
+        </div>
+        <a href="/" class="block text-center mt-4 text-blue-500 hover:underline">Upload more images</a>
+    </div>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/main-depricated.ipynb b/main-depricated.ipynb
new file mode 100644
index 00000000..84e9261a
--- /dev/null
+++ b/main-depricated.ipynb
@@ -0,0 +1,682 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.datasets import cifar10\n",
+    "\n",
+    "# Load dataset\n",
+    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+    "\n",
+    "# One-hot encode the labels\n",
+    "num_classes = 10\n",
+    "y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = tf.keras.utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "# Normalize the images\n",
+    "x_train = x_train.astype('float32') / 255.0\n",
+    "x_test = x_test.astype('float32') / 255.0\n",
+    "\n",
+    "# Define class names\n",
+    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
+    "\n",
+    "# Create a data augmentation pipeline\n",
+    "data_augmentation = tf.keras.Sequential([\n",
+    "    tf.keras.layers.RandomRotation(0.1),\n",
+    "    tf.keras.layers.RandomTranslation(0.1, 0.1),\n",
+    "    tf.keras.layers.RandomFlip(\"horizontal\"),\n",
+    "])\n",
+    "\n",
+    "\n",
+    "# Convert data to TensorFlow datasets\n",
+    "train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
+    "test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
+    "\n",
+    "# Apply augmentation to the training dataset\n",
+    "train_dataset = (\n",
+    "    train_dataset\n",
+    "    .shuffle(buffer_size=50000)  # Shuffle the dataset\n",
+    "    .map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=tf.data.AUTOTUNE)\n",
+    "    .batch(128)\n",
+    "    .prefetch(tf.data.AUTOTUNE)\n",
+    ")\n",
+    "\n",
+    "# Prepare the test dataset\n",
+    "test_dataset = (\n",
+    "    test_dataset\n",
+    "    .batch(128)\n",
+    "    .prefetch(tf.data.AUTOTUNE)\n",
+    ")\n",
+    "\n",
+    "# Visualize some augmented images\n",
+    "augmented_images, augmented_labels = next(iter(train_dataset))\n",
+    "\n",
+    "fig, axes = plt.subplots(6, 6, figsize=(5, 5))\n",
+    "axes = axes.ravel()\n",
+    "for i in np.arange(0, 36):\n",
+    "    axes[i].imshow(augmented_images[i].numpy())\n",
+    "    label_index = tf.argmax(augmented_labels[i]).numpy()  # Get the class index\n",
+    "    axes[i].set_title(class_names[label_index])\n",
+    "    axes[i].axis('off')\n",
+    "plt.subplots_adjust(wspace=0.5)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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_28\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_28\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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",
+       "│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_24          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_34 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_25          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_35 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</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\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_26          │ (<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\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_36 (<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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</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\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_27          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_37 (<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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_32 (<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\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,400</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_28          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_38 (<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\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_33 (<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\">2,570</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "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",
+       "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_28 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_24          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_34 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_29 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_25          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │           \u001b[38;5;34m256\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_35 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_30 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_26          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_36 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m295,168\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_31 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_27          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │         \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_37 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_8 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_32 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m262,400\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_28          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_38 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_33 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">656,330</span> (2.50 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m656,330\u001b[0m (2.50 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">654,858</span> (2.50 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m654,858\u001b[0m (2.50 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">1,472</span> (5.75 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,472\u001b[0m (5.75 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
+    "\n",
+    "# Define the model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# Convolutional Block 1\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Convolutional Block 2\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "# Convolutional Block 3\n",
+    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.3))\n",
+    "\n",
+    "# Convolutional Block 4\n",
+    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.3))\n",
+    "\n",
+    "# Flatten and Fully Connected Block\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(256, activation='relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Output Layer\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 109ms/step - accuracy: 0.2750 - loss: 2.3320 - val_accuracy: 0.1860 - val_loss: 2.5377\n",
+      "Epoch 2/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 105ms/step - accuracy: 0.4349 - loss: 1.5664 - val_accuracy: 0.4381 - val_loss: 1.7754\n",
+      "Epoch 3/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 102ms/step - accuracy: 0.4979 - loss: 1.3859 - val_accuracy: 0.5213 - val_loss: 1.4164\n",
+      "Epoch 4/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 104ms/step - accuracy: 0.5320 - loss: 1.3011 - val_accuracy: 0.4876 - val_loss: 1.5114\n",
+      "Epoch 5/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 104ms/step - accuracy: 0.5549 - loss: 1.2440 - val_accuracy: 0.5901 - val_loss: 1.1926\n",
+      "Epoch 6/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 106ms/step - accuracy: 0.5827 - loss: 1.1770 - val_accuracy: 0.5996 - val_loss: 1.1406\n",
+      "Epoch 7/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 126ms/step - accuracy: 0.5964 - loss: 1.1383 - val_accuracy: 0.5874 - val_loss: 1.1952\n",
+      "Epoch 8/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 131ms/step - accuracy: 0.6061 - loss: 1.1177 - val_accuracy: 0.6836 - val_loss: 0.9101\n",
+      "Epoch 9/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 129ms/step - accuracy: 0.6176 - loss: 1.0801 - val_accuracy: 0.5959 - val_loss: 1.1954\n",
+      "Epoch 10/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 136ms/step - accuracy: 0.6282 - loss: 1.0502 - val_accuracy: 0.5901 - val_loss: 1.2348\n",
+      "Epoch 11/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 124ms/step - accuracy: 0.6356 - loss: 1.0358 - val_accuracy: 0.6691 - val_loss: 0.9411\n",
+      "Epoch 12/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 124ms/step - accuracy: 0.6439 - loss: 1.0051 - val_accuracy: 0.6260 - val_loss: 1.0759\n",
+      "Epoch 13/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 120ms/step - accuracy: 0.6494 - loss: 0.9878 - val_accuracy: 0.7085 - val_loss: 0.8370\n",
+      "Epoch 14/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 123ms/step - accuracy: 0.6577 - loss: 0.9761 - val_accuracy: 0.6243 - val_loss: 1.1534\n",
+      "Epoch 15/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 121ms/step - accuracy: 0.6606 - loss: 0.9635 - val_accuracy: 0.7076 - val_loss: 0.8348\n",
+      "Epoch 16/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 124ms/step - accuracy: 0.6658 - loss: 0.9573 - val_accuracy: 0.6770 - val_loss: 0.9472\n",
+      "Epoch 17/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 132ms/step - accuracy: 0.6705 - loss: 0.9413 - val_accuracy: 0.6376 - val_loss: 1.1004\n",
+      "Epoch 18/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 143ms/step - accuracy: 0.6736 - loss: 0.9336 - val_accuracy: 0.6325 - val_loss: 1.1296\n",
+      "Epoch 19/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 146ms/step - accuracy: 0.6751 - loss: 0.9249 - val_accuracy: 0.7528 - val_loss: 0.7116\n",
+      "Epoch 20/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 139ms/step - accuracy: 0.6776 - loss: 0.9196 - val_accuracy: 0.7212 - val_loss: 0.8105\n",
+      "Epoch 21/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 135ms/step - accuracy: 0.6855 - loss: 0.9025 - val_accuracy: 0.7109 - val_loss: 0.8327\n",
+      "Epoch 22/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 136ms/step - accuracy: 0.6854 - loss: 0.9010 - val_accuracy: 0.7364 - val_loss: 0.7678\n",
+      "Epoch 23/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 120ms/step - accuracy: 0.6867 - loss: 0.8908 - val_accuracy: 0.7211 - val_loss: 0.8059\n",
+      "Epoch 24/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 116ms/step - accuracy: 0.6916 - loss: 0.8813 - val_accuracy: 0.7521 - val_loss: 0.7147\n",
+      "Epoch 25/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 128ms/step - accuracy: 0.6928 - loss: 0.8724 - val_accuracy: 0.7701 - val_loss: 0.6726\n",
+      "Epoch 26/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 119ms/step - accuracy: 0.6979 - loss: 0.8697 - val_accuracy: 0.7375 - val_loss: 0.7558\n",
+      "Epoch 27/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 119ms/step - accuracy: 0.6992 - loss: 0.8607 - val_accuracy: 0.6877 - val_loss: 0.9058\n",
+      "Epoch 28/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 120ms/step - accuracy: 0.7022 - loss: 0.8546 - val_accuracy: 0.7210 - val_loss: 0.8290\n",
+      "Epoch 29/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 115ms/step - accuracy: 0.7013 - loss: 0.8505 - val_accuracy: 0.7022 - val_loss: 0.8599\n",
+      "Epoch 30/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 113ms/step - accuracy: 0.7113 - loss: 0.8364 - val_accuracy: 0.7751 - val_loss: 0.6595\n",
+      "Epoch 31/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 113ms/step - accuracy: 0.7075 - loss: 0.8442 - val_accuracy: 0.7639 - val_loss: 0.6749\n",
+      "Epoch 32/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 113ms/step - accuracy: 0.7102 - loss: 0.8349 - val_accuracy: 0.7646 - val_loss: 0.6770\n",
+      "Epoch 33/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 114ms/step - accuracy: 0.7120 - loss: 0.8207 - val_accuracy: 0.7568 - val_loss: 0.6989\n",
+      "Epoch 34/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 109ms/step - accuracy: 0.7182 - loss: 0.8123 - val_accuracy: 0.7503 - val_loss: 0.7176\n",
+      "Epoch 35/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 112ms/step - accuracy: 0.7178 - loss: 0.8116 - val_accuracy: 0.7564 - val_loss: 0.7059\n",
+      "Epoch 36/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 109ms/step - accuracy: 0.7176 - loss: 0.8023 - val_accuracy: 0.7795 - val_loss: 0.6289\n",
+      "Epoch 37/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 109ms/step - accuracy: 0.7183 - loss: 0.8019 - val_accuracy: 0.7555 - val_loss: 0.7089\n",
+      "Epoch 38/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 107ms/step - accuracy: 0.7240 - loss: 0.8030 - val_accuracy: 0.7732 - val_loss: 0.6476\n",
+      "Epoch 39/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 105ms/step - accuracy: 0.7180 - loss: 0.8054 - val_accuracy: 0.7865 - val_loss: 0.6146\n",
+      "Epoch 40/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 106ms/step - accuracy: 0.7208 - loss: 0.7992 - val_accuracy: 0.7485 - val_loss: 0.7284\n",
+      "Epoch 41/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 104ms/step - accuracy: 0.7235 - loss: 0.7846 - val_accuracy: 0.7531 - val_loss: 0.7161\n",
+      "Epoch 42/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 107ms/step - accuracy: 0.7259 - loss: 0.7901 - val_accuracy: 0.7498 - val_loss: 0.7270\n",
+      "Epoch 43/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 101ms/step - accuracy: 0.7271 - loss: 0.7854 - val_accuracy: 0.7354 - val_loss: 0.7723\n",
+      "Epoch 44/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 103ms/step - accuracy: 0.7278 - loss: 0.7817 - val_accuracy: 0.7797 - val_loss: 0.6304\n",
+      "Epoch 45/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 99ms/step - accuracy: 0.7274 - loss: 0.7809 - val_accuracy: 0.7907 - val_loss: 0.6137\n",
+      "Epoch 46/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 104ms/step - accuracy: 0.7325 - loss: 0.7684 - val_accuracy: 0.7840 - val_loss: 0.6292\n",
+      "Epoch 47/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 113ms/step - accuracy: 0.7278 - loss: 0.7804 - val_accuracy: 0.7609 - val_loss: 0.7123\n",
+      "Epoch 48/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 120ms/step - accuracy: 0.7321 - loss: 0.7728 - val_accuracy: 0.7363 - val_loss: 0.7887\n",
+      "Epoch 49/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 127ms/step - accuracy: 0.7338 - loss: 0.7659 - val_accuracy: 0.7461 - val_loss: 0.7468\n",
+      "Epoch 50/50\n",
+      "\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 110ms/step - accuracy: 0.7373 - loss: 0.7634 - val_accuracy: 0.7508 - val_loss: 0.7330\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "\n",
+    "# Early stopping\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
+    "\n",
+    "# Train the model\n",
+    "history = model.fit(\n",
+    "    train_dataset,  # Use the tf.data.Dataset pipeline for training. This is new to me I hope it works\n",
+    "    epochs=50,\n",
+    "    validation_data= test_dataset,  # Use the test_dataset for validation\n",
+    "    callbacks=[early_stopping]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 0.7330184578895569\n",
+      "Test Accuracy: 0.7508000135421753\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Evaluate the model on the test dataset\n",
+    "test_loss, test_acc = model.evaluate(test_dataset, verbose=0)\n",
+    "print(f\"Test Loss: {test_loss}\")\n",
+    "print(f\"Test Accuracy: {test_acc}\")\n",
+    "\n",
+    "# Predict the classes using the test dataset\n",
+    "y_pred = []\n",
+    "y_true = []\n",
+    "\n",
+    "# converting dataset, I need to learn more about this\n",
+    "for images, labels in test_dataset:\n",
+    "    predictions = model.predict(images, verbose=0)\n",
+    "    y_pred.extend(np.argmax(predictions, axis=1))  # Convert predictions to class indices\n",
+    "    y_true.extend(np.argmax(labels.numpy(), axis=1))  # Convert one-hot labels to class indices\n",
+    "\n",
+    "\n",
+    "# Confusion matrix\n",
+    "cm = confusion_matrix(y_true, y_pred)\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
+    "plt.ylabel('Actual')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m228s\u001b[0m 287ms/step - accuracy: 0.3086 - loss: 1.9215 - val_accuracy: 0.4960 - val_loss: 1.4439\n",
+      "Epoch 2/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 274ms/step - accuracy: 0.4457 - loss: 1.5668 - val_accuracy: 0.5244 - val_loss: 1.3586\n",
+      "Epoch 3/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m220s\u001b[0m 281ms/step - accuracy: 0.4673 - loss: 1.5153 - val_accuracy: 0.5306 - val_loss: 1.3201\n",
+      "Epoch 4/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m233s\u001b[0m 297ms/step - accuracy: 0.4728 - loss: 1.4845 - val_accuracy: 0.5413 - val_loss: 1.3040\n",
+      "Epoch 5/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 288ms/step - accuracy: 0.4817 - loss: 1.4688 - val_accuracy: 0.5409 - val_loss: 1.2964\n",
+      "Epoch 6/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m213s\u001b[0m 273ms/step - accuracy: 0.4875 - loss: 1.4512 - val_accuracy: 0.5508 - val_loss: 1.2767\n",
+      "Epoch 7/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 273ms/step - accuracy: 0.4955 - loss: 1.4399 - val_accuracy: 0.5455 - val_loss: 1.2826\n",
+      "Epoch 8/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 273ms/step - accuracy: 0.5007 - loss: 1.4228 - val_accuracy: 0.5459 - val_loss: 1.2748\n",
+      "Epoch 9/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 271ms/step - accuracy: 0.4953 - loss: 1.4282 - val_accuracy: 0.5503 - val_loss: 1.2630\n",
+      "Epoch 10/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 278ms/step - accuracy: 0.5012 - loss: 1.4214 - val_accuracy: 0.5479 - val_loss: 1.2646\n",
+      "Epoch 11/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 274ms/step - accuracy: 0.5032 - loss: 1.4113 - val_accuracy: 0.5576 - val_loss: 1.2454\n",
+      "Epoch 12/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 272ms/step - accuracy: 0.5048 - loss: 1.4059 - val_accuracy: 0.5603 - val_loss: 1.2495\n",
+      "Epoch 13/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 271ms/step - accuracy: 0.5070 - loss: 1.3994 - val_accuracy: 0.5630 - val_loss: 1.2441\n",
+      "Epoch 14/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 271ms/step - accuracy: 0.5058 - loss: 1.4010 - val_accuracy: 0.5651 - val_loss: 1.2347\n",
+      "Epoch 15/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m211s\u001b[0m 270ms/step - accuracy: 0.5098 - loss: 1.4020 - val_accuracy: 0.5664 - val_loss: 1.2373\n",
+      "Epoch 16/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 273ms/step - accuracy: 0.5120 - loss: 1.3861 - val_accuracy: 0.5530 - val_loss: 1.2571\n",
+      "Epoch 17/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m213s\u001b[0m 272ms/step - accuracy: 0.5107 - loss: 1.3847 - val_accuracy: 0.5637 - val_loss: 1.2361\n",
+      "Epoch 18/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m211s\u001b[0m 270ms/step - accuracy: 0.5099 - loss: 1.3937 - val_accuracy: 0.5631 - val_loss: 1.2410\n",
+      "Epoch 19/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 271ms/step - accuracy: 0.5107 - loss: 1.3856 - val_accuracy: 0.5650 - val_loss: 1.2316\n",
+      "Epoch 20/20\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 271ms/step - accuracy: 0.5154 - loss: 1.3820 - val_accuracy: 0.5649 - val_loss: 1.2322\n",
+      "Test Loss: 1.232221245765686\n",
+      "Test Accuracy: 0.5648999810218811\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    airplane       0.62      0.72      0.67      1000\n",
+      "  automobile       0.54      0.69      0.61      1000\n",
+      "        bird       0.55      0.41      0.47      1000\n",
+      "         cat       0.45      0.34      0.39      1000\n",
+      "        deer       0.61      0.37      0.46      1000\n",
+      "         dog       0.57      0.49      0.52      1000\n",
+      "        frog       0.48      0.76      0.59      1000\n",
+      "       horse       0.63      0.64      0.64      1000\n",
+      "        ship       0.72      0.64      0.68      1000\n",
+      "       truck       0.53      0.60      0.56      1000\n",
+      "\n",
+      "    accuracy                           0.56     10000\n",
+      "   macro avg       0.57      0.56      0.56     10000\n",
+      "weighted avg       0.57      0.56      0.56     10000\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.applications import VGG16\n",
+    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Load the VGG16 model with pre-trained weights\n",
+    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n",
+    "\n",
+    "# Freeze the layers of the base model\n",
+    "base_model.trainable = False\n",
+    "\n",
+    "# Add custom classification layers\n",
+    "transfer_model = Sequential([\n",
+    "    base_model,\n",
+    "    GlobalAveragePooling2D(),\n",
+    "    Dense(128, activation='relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(10, activation='softmax')  # Adjust for CIFAR-10's 10 classes\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "transfer_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Train the model (assuming `train_dataset` and `test_dataset` are properly set up)\n",
+    "history = transfer_model.fit(\n",
+    "    train_dataset,\n",
+    "    epochs=20,\n",
+    "    validation_data=test_dataset,\n",
+    "    callbacks=[early_stopping]\n",
+    ")\n",
+    "\n",
+    "# Evaluate the model on the test dataset\n",
+    "test_loss, test_acc = transfer_model.evaluate(test_dataset, verbose=0)\n",
+    "print(f\"Test Loss: {test_loss}\")\n",
+    "print(f\"Test Accuracy: {test_acc}\")\n",
+    "\n",
+    "# Predict the classes using the test dataset\n",
+    "y_pred = []\n",
+    "y_true = []\n",
+    "\n",
+    "for images, labels in test_dataset:\n",
+    "    predictions = transfer_model.predict(images, verbose=0)\n",
+    "    y_pred.extend(np.argmax(predictions, axis=1))  # Convert predictions to class indices\n",
+    "    y_true.extend(np.argmax(labels.numpy(), axis=1))  # Convert one-hot labels to class indices\n",
+    "\n",
+    "# Classification report\n",
+    "print(classification_report(y_true, y_pred, target_names=class_names))\n",
+    "\n",
+    "# Confusion matrix\n",
+    "cm = confusion_matrix(y_true, y_pred)\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
+    "plt.ylabel('Actual')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 00000000..41d5f90e
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,832 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.datasets import cifar10\n",
+    "from tensorflow.keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape: (50000, 32, 32, 3), Training labels shape: (50000, 1)\n",
+      "Test data shape: (10000, 32, 32, 3), Test labels shape: (10000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the CIFAR-10 dataset and divide it into training and testing sets\n",
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
+    "\n",
+    "# Check the shape of the datasets\n",
+    "print(f\"Training data shape: {X_train.shape}, Training labels shape: {y_train.shape}\")\n",
+    "print(f\"Test data shape: {X_test.shape}, Test labels shape: {y_test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape: (50000, 32, 32, 3), Training labels shape: (50000, 10)\n",
+      "Test data shape: (10000, 32, 32, 3), Test labels shape: (10000, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define class names for visualization\n",
+    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
+    "\n",
+    "\n",
+    "# Normalize the images\n",
+    "X_train = X_train.astype('float32') / 255.0\n",
+    "X_test = X_test.astype('float32') / 255.0\n",
+    "\n",
+    "#const\n",
+    "num_classes = 10\n",
+    "input_shape = (32, 32, 3)\n",
+    "\n",
+    "# One-hot encode the labels\n",
+    "y_train = to_categorical(y_train, num_classes)\n",
+    "y_test = to_categorical(y_test, num_classes)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Check the shape of the datasets\n",
+    "print(f\"Training data shape: {X_train.shape}, Training labels shape: {y_train.shape}\")\n",
+    "print(f\"Test data shape: {X_test.shape}, Test labels shape: {y_test.shape}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display a few random images from the training set\n",
+    "plt.figure(figsize=(5,5))\n",
+    "for i in range(9):\n",
+    "    plt.subplot(3, 3, i+1)\n",
+    "    plt.imshow(X_train[i])\n",
+    "    plt.title(f\"Class: {y_train[i].argmax()}\")\n",
+    "    plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jaime\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "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_3\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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",
+       "│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_27          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_28          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_15 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_29          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_30          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_16 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_31          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_32          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</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\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_17 (<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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</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\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_33          │ (<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\">256</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</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\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_34          │ (<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\">256</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_18 (<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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (<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\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,400</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_35          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_19 (<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\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (<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\">2,570</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "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",
+       "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_27          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_28          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_15 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_29          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_30          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_31          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m147,584\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_32          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_17 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m295,168\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_33          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │         \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m590,080\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_34          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │         \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_18 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m262,400\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_35          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_19 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">1,442,090</span> (5.50 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,442,090\u001b[0m (5.50 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">1,439,658</span> (5.49 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,439,658\u001b[0m (5.49 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">2,432</span> (9.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,432\u001b[0m (9.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
+    "\n",
+    "# Define the model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# Convolutional Block 1\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=input_shape))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "# Convolutional Block 2\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "# Convolutional Block 3\n",
+    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "# Convolutional Block 4\n",
+    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "# Flatten and Fully Connected Block\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(256, activation='relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Output Layer\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 151ms/step - accuracy: 0.9531 - loss: 0.1392 - val_accuracy: 0.8607 - val_loss: 0.5254\n",
+      "Epoch 2/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 148ms/step - accuracy: 0.9612 - loss: 0.1140 - val_accuracy: 0.8603 - val_loss: 0.5685\n",
+      "Epoch 3/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 148ms/step - accuracy: 0.9631 - loss: 0.1088 - val_accuracy: 0.8543 - val_loss: 0.5730\n",
+      "Epoch 4/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1792s\u001b[0m 2s/step - accuracy: 0.9619 - loss: 0.1089 - val_accuracy: 0.8590 - val_loss: 0.5834\n",
+      "Epoch 5/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 97ms/step - accuracy: 0.9636 - loss: 0.1084 - val_accuracy: 0.8510 - val_loss: 0.6424\n",
+      "Epoch 6/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 124ms/step - accuracy: 0.9651 - loss: 0.1034 - val_accuracy: 0.8539 - val_loss: 0.6097\n",
+      "Epoch 7/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 128ms/step - accuracy: 0.9627 - loss: 0.1040 - val_accuracy: 0.8522 - val_loss: 0.6243\n",
+      "Epoch 8/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 141ms/step - accuracy: 0.9673 - loss: 0.0980 - val_accuracy: 0.8536 - val_loss: 0.6064\n",
+      "Epoch 9/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m149s\u001b[0m 190ms/step - accuracy: 0.9660 - loss: 0.0967 - val_accuracy: 0.8596 - val_loss: 0.6027\n",
+      "Epoch 10/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 127ms/step - accuracy: 0.9683 - loss: 0.0926 - val_accuracy: 0.8558 - val_loss: 0.6219\n",
+      "Epoch 11/60\n",
+      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 143ms/step - accuracy: 0.9682 - loss: 0.0925 - val_accuracy: 0.8509 - val_loss: 0.6455\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "\n",
+    "# Early stopping\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
+    "\n",
+    "# Train the model\n",
+    "history = model.fit(\n",
+    "    X_train, y_train,\n",
+    "    batch_size=64,\n",
+    "    epochs=60,\n",
+    "    verbose=1,\n",
+    "    validation_data=(X_test, y_test),\n",
+    "    callbacks=[early_stopping]   \n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 0.6455296874046326\n",
+      "Test Accuracy: 0.8508999943733215\n",
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "\n",
+    "# Evaluate the model on the test dataset\n",
+    "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
+    "print(f\"Test Loss: {test_loss}\")\n",
+    "print(f\"Test Accuracy: {test_acc}\")\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+    "y_true = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "#Display confusion matrix\n",
+    "cm = confusion_matrix(y_true, y_pred_classes)\n",
+    "cmd = ConfusionMatrixDisplay(cm, display_labels=class_names)\n",
+    "cmd.plot(cmap=plt.cm.Blues)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the Model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: saved_model/my_model/1/assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: saved_model/my_model/1/assets\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved artifact at 'saved_model/my_model/1/'. The following endpoints are available:\n",
+      "\n",
+      "* Endpoint 'serve'\n",
+      "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 32, 32, 3), dtype=tf.float32, name='keras_tensor_1308')\n",
+      "Output Type:\n",
+      "  TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)\n",
+      "Captures:\n",
+      "  1664798814096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798815440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798815824: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798816016: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798814672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798816400: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798817360: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798817744: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798815248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751649232: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798817168: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664798815056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751650000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751650960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751649808: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751650576: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751649040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751651728: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751652112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751653456: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751652496: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751653072: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751652880: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751654224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751653648: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751656144: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751654032: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751655760: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751653264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751656912: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751658064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751658448: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751657872: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751658640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751658832: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751659408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751663440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664751664592: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830046864: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830046288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830047056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830047632: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830048976: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830050320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830051280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830050896: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830051472: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830052240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830055696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830057232: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830057424: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830057040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830056272: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830057808: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830058768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+      "  1664830059920: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the model in TensorFlow SavedModel format\n",
+    "\n",
+    "model.export('saved_model/my_model/1/')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 383ms/step\n",
+      "Predicted Class: automobile\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load and preprocess the image\n",
+    "def preprocess_image(img_path, target_size=(32, 32)):\n",
+    "    img = image.load_img(img_path, target_size=target_size)\n",
+    "    img_array = image.img_to_array(img)\n",
+    "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
+    "    img_array /= 255.0  # Normalize to [0, 1] range\n",
+    "    return img_array\n",
+    "\n",
+    "# Path to the image you want to predict\n",
+    "img_path = 'image-prediction/images/360_F_177742846_umwpEr5OqwEQd4a9VyS7BGJX3tINNDe7.jpg'  # Replace with the path to your image\n",
+    "\n",
+    "# Preprocess the image\n",
+    "img_array = preprocess_image(img_path)\n",
+    "\n",
+    "# Make prediction\n",
+    "predictions = model.predict(img_array)\n",
+    "predicted_class = np.argmax(predictions)\n",
+    "\n",
+    "# Print the predicted class\n",
+    "print(f\"Predicted Class: {class_names[predicted_class]}\")\n",
+    "\n",
+    "# Visualize the image\n",
+    "img = image.load_img(img_path, target_size=(32, 32))\n",
+    "plt.imshow(img)\n",
+    "plt.title(f\"Predicted: {class_names[predicted_class]}\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m270s\u001b[0m 171ms/step - accuracy: 0.3855 - loss: 1.7446 - val_accuracy: 0.5411 - val_loss: 1.3114\n",
+      "Epoch 2/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m267s\u001b[0m 171ms/step - accuracy: 0.5246 - loss: 1.3651 - val_accuracy: 0.5620 - val_loss: 1.2518\n",
+      "Epoch 3/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m248s\u001b[0m 159ms/step - accuracy: 0.5474 - loss: 1.2951 - val_accuracy: 0.5756 - val_loss: 1.2113\n",
+      "Epoch 4/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 156ms/step - accuracy: 0.5590 - loss: 1.2638 - val_accuracy: 0.5863 - val_loss: 1.1974\n",
+      "Epoch 5/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m253s\u001b[0m 162ms/step - accuracy: 0.5655 - loss: 1.2412 - val_accuracy: 0.5896 - val_loss: 1.1739\n",
+      "Epoch 6/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m312s\u001b[0m 200ms/step - accuracy: 0.5739 - loss: 1.2240 - val_accuracy: 0.5932 - val_loss: 1.1619\n",
+      "Epoch 7/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m269s\u001b[0m 172ms/step - accuracy: 0.5777 - loss: 1.2069 - val_accuracy: 0.5968 - val_loss: 1.1606\n",
+      "Epoch 8/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m254s\u001b[0m 162ms/step - accuracy: 0.5782 - loss: 1.2039 - val_accuracy: 0.5991 - val_loss: 1.1506\n",
+      "Epoch 9/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 151ms/step - accuracy: 0.5840 - loss: 1.1845 - val_accuracy: 0.5992 - val_loss: 1.1626\n",
+      "Epoch 10/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 147ms/step - accuracy: 0.5851 - loss: 1.1776 - val_accuracy: 0.6009 - val_loss: 1.1504\n",
+      "Epoch 11/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m234s\u001b[0m 149ms/step - accuracy: 0.5945 - loss: 1.1685 - val_accuracy: 0.5990 - val_loss: 1.1464\n",
+      "Epoch 12/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13295s\u001b[0m 9s/step - accuracy: 0.5900 - loss: 1.1665 - val_accuracy: 0.6023 - val_loss: 1.1466\n",
+      "Epoch 13/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 111ms/step - accuracy: 0.5963 - loss: 1.1613 - val_accuracy: 0.6019 - val_loss: 1.1458\n",
+      "Epoch 14/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 131ms/step - accuracy: 0.5948 - loss: 1.1588 - val_accuracy: 0.5996 - val_loss: 1.1404\n",
+      "Epoch 15/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 136ms/step - accuracy: 0.6024 - loss: 1.1381 - val_accuracy: 0.6076 - val_loss: 1.1336\n",
+      "Epoch 16/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 141ms/step - accuracy: 0.6073 - loss: 1.1270 - val_accuracy: 0.6050 - val_loss: 1.1435\n",
+      "Epoch 17/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m233s\u001b[0m 149ms/step - accuracy: 0.6029 - loss: 1.1355 - val_accuracy: 0.6065 - val_loss: 1.1332\n",
+      "Epoch 18/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 152ms/step - accuracy: 0.6071 - loss: 1.1309 - val_accuracy: 0.6052 - val_loss: 1.1368\n",
+      "Epoch 19/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m234s\u001b[0m 150ms/step - accuracy: 0.6100 - loss: 1.1137 - val_accuracy: 0.6069 - val_loss: 1.1325\n",
+      "Epoch 20/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 156ms/step - accuracy: 0.6123 - loss: 1.1102 - val_accuracy: 0.6065 - val_loss: 1.1329\n",
+      "Test Loss: 1.132887601852417\n",
+      "Test Accuracy: 0.6065000295639038\n",
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 122ms/step\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'ConfusionMatrixDisplay' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[8], line 51\u001b[0m\n\u001b[0;32m     48\u001b[0m y_true \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(y_test, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     50\u001b[0m cm \u001b[38;5;241m=\u001b[39m confusion_matrix(y_true, y_pred_classes)\n\u001b[1;32m---> 51\u001b[0m cmd \u001b[38;5;241m=\u001b[39m \u001b[43mConfusionMatrixDisplay\u001b[49m(cm, display_labels\u001b[38;5;241m=\u001b[39mclass_names)\n\u001b[0;32m     52\u001b[0m cmd\u001b[38;5;241m.\u001b[39mplot(cmap\u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39mcm\u001b[38;5;241m.\u001b[39mBlues)\n\u001b[0;32m     53\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'ConfusionMatrixDisplay' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.applications import VGG16\n",
+    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "\n",
+    "# Load the VGG16 model with pre-trained weights\n",
+    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n",
+    "\n",
+    "# Freeze the layers of the base model\n",
+    "base_model.trainable = False\n",
+    "\n",
+    "# Add custom classification layers\n",
+    "transfer_model = Sequential([\n",
+    "    base_model,\n",
+    "    GlobalAveragePooling2D(),\n",
+    "    Dense(128, activation='relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(10, activation='softmax')  # Adjust for CIFAR-10's 10 classes\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "transfer_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Early stopping\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
+    "\n",
+    "# Train the model (assuming `train_dataset` and `test_dataset` are properly set up)\n",
+    "history = transfer_model.fit(\n",
+    "    X_train, y_train,\n",
+    "    batch_size=32,\n",
+    "    epochs=20,\n",
+    "    verbose=1,\n",
+    "    validation_data=(X_test, y_test),\n",
+    "    callbacks=[early_stopping]   \n",
+    ")\n",
+    "\n",
+    "# Evaluate the model on the test dataset\n",
+    "test_loss, test_acc = transfer_model.evaluate(X_test, y_test, verbose=0)\n",
+    "print(f\"Test Loss: {test_loss}\")\n",
+    "print(f\"Test Accuracy: {test_acc}\")\n",
+    "\n",
+    "y_pred = transfer_model.predict(X_test)\n",
+    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+    "y_true = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred_classes)\n",
+    "cmd = ConfusionMatrixDisplay(cm, display_labels=class_names)\n",
+    "cmd.plot(cmap=plt.cm.Blues)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['assets', 'fingerprint.pb', 'saved_model.pb', 'variables']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "print(os.listdir(\"saved_model/my_model\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/request-google.py b/request-google.py
new file mode 100644
index 00000000..5a89798e
--- /dev/null
+++ b/request-google.py
@@ -0,0 +1,57 @@
+import requests
+import json
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from google.auth import default
+from google.auth.transport.requests import Request
+
+# Load and preprocess the image
+def preprocess_image(img_path, target_size=(32, 32)):
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)  # (32, 32, 3)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension -> (1, 32, 32, 3)
+    img_array /= 255.0  # Normalize to [0, 1] range
+    return img_array
+
+# Load the image
+img_path = 'image-prediction/images/360_F_177742846_umwpEr5OqwEQd4a9VyS7BGJX3tINNDe7.jpg'
+img_array = preprocess_image(img_path)
+
+# Debug input shape
+print("Input shape:", img_array.shape)  # Should print (1, 32, 32, 3)
+
+# Convert to JSON serializable format
+input_data = img_array.tolist()
+
+# Prepare the payload
+data = json.dumps({"instances": input_data})
+
+# Define your endpoint URL
+url = 'https://us-east1-aiplatform.googleapis.com/v1/projects/10609508497/locations/us-east1/endpoints/2868357556030406656:predict'
+
+# Authenticate using service account credentials
+credentials, project = default()
+credentials.refresh(Request())
+
+# Add the authorization header
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {credentials.token}"
+}
+
+# Send the request
+response = requests.post(url, headers=headers, data=data)
+
+# Print the entire response for debugging
+response_data = response.json()
+print("Response:", response_data)
+
+# Check for predictions
+if 'predictions' in response_data:
+    predictions = response_data['predictions']
+    prediction_values = predictions[0]  # Assuming there's one result
+    predicted_class_index = np.argmax(prediction_values)
+    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+    print(f"Predicted class: {class_names[predicted_class_index]}")
+else:
+    print("Error: 'predictions' not found in the response.")
diff --git a/request.py b/request.py
new file mode 100644
index 00000000..d33f21db
--- /dev/null
+++ b/request.py
@@ -0,0 +1,44 @@
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import requests
+import json
+
+# Load and preprocess the image
+def preprocess_image(img_path, target_size=(32, 32)):
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array /= 255.0  # Normalize to [0, 1] range
+    return img_array
+
+# Path to the image you want to predict
+img_path = './image-prediction/images/360_F_177742846_umwpEr5OqwEQd4a9VyS7BGJX3tINNDe7.jpg'  
+
+# Preprocess the image
+img_array = preprocess_image(img_path)
+
+# Prepare the request
+input_data = np.expand_dims(img_array, axis=0).tolist()
+
+# Send the request to TensorFlow Serving
+data = json.dumps({"instances": input_data})
+headers = {"content-type": "application/json"}
+response = requests.post('http://localhost:8501/v1/models/my_model:predict', data=data, headers=headers)
+
+# Print the predicted class
+predictions = response.json()
+
+# Class names
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+prediction_values = predictions['predictions'][0]
+
+# Get the index of the class with the highest probability
+predicted_class_index = np.argmax(prediction_values)
+
+# Get the class name corresponding to that index
+predicted_class_name = class_names[predicted_class_index]
+
+# Print the predicted class name
+print(f"Predicted Class: {predicted_class_name}")
+
diff --git a/saved_model/my_model/1/fingerprint.pb b/saved_model/my_model/1/fingerprint.pb
new file mode 100644
index 00000000..21a15ed8
--- /dev/null
+++ b/saved_model/my_model/1/fingerprint.pb
@@ -0,0 +1 @@
+�����ۤ��
��뵼�l�����ӥ��
 ���Ϯ��6(���������
2

\ No newline at end of file
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