Kode_Blitz_(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chanuka-ChandraYapa/Kode-Blitz_SpaceXplore/blob/main/Kode_Blitz_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5NwyHJdHFlw",
        "outputId": "806fbace-683f-4624-a506-c9fbd73c3e16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/drive/MyDrive/Datathon-Dataset.zip"
      ],
      "metadata": {
        "id": "K5bJ13FZLOLp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import  warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "hPllJVdp745C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import math\n",
        "import shutil\n",
        "import glob"
      ],
      "metadata": {
        "id": "feohb8Zz751c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ROOT_DIR = \"/content/Datathon-Dataset\"\n",
        "no_of_images = {}\n",
        "for dir in os.listdir(ROOT_DIR):\n",
        "  no_of_images[dir] = len(os.listdir(os.path.join(ROOT_DIR, dir)))"
      ],
      "metadata": {
        "id": "x3vJ4v2u76Sw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dataFolder(path, split):\n",
        "  if not os.path.exists(\"./\"+path):\n",
        "    os.mkdir(\"./\" + path)\n",
        "\n",
        "    for dir in os.listdir(ROOT_DIR):\n",
        "      os.makedirs(\"./\"+ path + \"/\" + dir)\n",
        "      for img in np.random.choice(a = os.listdir(os.path.join(ROOT_DIR, dir)), size = (math.floor(split*no_of_images[dir])-5), replace= False):\n",
        "        o = os.path.join(ROOT_DIR,dir,img)\n",
        "        D = os.path.join(\"./\"+ path, dir)\n",
        "        shutil.move(o, D)\n",
        "  else:\n",
        "    print(f\"{path} folder exists\")"
      ],
      "metadata": {
        "id": "7fk6o8Ox8TDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataFolder(\"train\", 0.7)\n",
        "dataFolder(\"val\", 0.15)\n",
        "dataFolder(\"test\",0.15)"
      ],
      "metadata": {
        "id": "0Vd96cIZ8a4t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#import Keral module\n",
        "from keras.applications import VGG16\n",
        "from keras.layers import Flatten, Dense, BatchNormalization, Dropout\n",
        "from keras.models import Model\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau"
      ],
      "metadata": {
        "id": "vvgcaxY294Is"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load pre-trained VGG16 model without top layers\n",
        "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))"
      ],
      "metadata": {
        "id": "HxIfbqzC-KGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the number of classes\n",
        "num_classes = 4\n",
        "\n",
        "# Create a custom top model\n",
        "top_model = base_model.output\n",
        "top_model = Flatten()(top_model)\n",
        "top_model = Dense(512, activation='relu')(top_model)\n",
        "top_model = BatchNormalization()(top_model)\n",
        "top_model = Dropout(0.5)(top_model)\n",
        "top_model = Dense(num_classes, activation='softmax')(top_model)"
      ],
      "metadata": {
        "id": "hPjBFGQ6-SrO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the base model and top model\n",
        "model = Model(inputs=base_model.input, outputs=top_model)\n"
      ],
      "metadata": {
        "id": "AQ28bbTq-V0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tune more layers of the base model\n",
        "for layer in base_model.layers[:10]:\n",
        "    layer.trainable = False\n",
        "for layer in base_model.layers[10:]:\n",
        "    layer.trainable = True\n"
      ],
      "metadata": {
        "id": "d6upsfaj-ZMS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model with a lower learning rate\n",
        "model.compile(optimizer=Adam(learning_rate=0.00001), loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "aEiCdY__-b0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Augmentation and Preprocessing\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1.0 / 255,\n",
        "    rotation_range=30,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1.0 / 255)\n",
        "test_datagen = ImageDataGenerator(rescale=1.0 / 255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    '/content/train',\n",
        "    target_size=(224, 224),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical',\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    '/content/val',\n",
        "    target_size=(224, 224),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical',\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    '/content/test',\n",
        "    target_size=(224, 224),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical',\n",
        "    shuffle=False\n",
        ")"
      ],
      "metadata": {
        "id": "8_pXKUOg-gVb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Callbacks\n",
        "mc = ModelCheckpoint(filepath=\"./bestmodel.h5\", monitor='val_accuracy', verbose=1, save_best_only=True, mode='auto')\n",
        "es = EarlyStopping(monitor='val_loss', patience=15, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.00001, verbose=1)\n",
        "\n",
        "callbacks = [mc, es, reduce_lr]"
      ],
      "metadata": {
        "id": "-54cS26z-le0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=len(train_generator),\n",
        "    epochs=100,\n",
        "    validation_data=val_generator,\n",
        "    validation_steps=len(val_generator),\n",
        "    callbacks=callbacks\n",
        ")"
      ],
      "metadata": {
        "id": "EPbBORW2-pvm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate model on test data\n",
        "test_loss, test_accuracy = model.evaluate(test_generator, steps=len(test_generator))\n",
        "print(f\"Test accuracy: {test_accuracy:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhFXgLF8z-v5",
        "outputId": "cce8c4f5-143d-4658-815a-beb8ef2a28b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2235 images belonging to 4 classes.\n",
            "Found 462 images belonging to 4 classes.\n",
            "Found 462 images belonging to 4 classes.\n",
            "Epoch 1/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 1.4801 - accuracy: 0.4550\n",
            "Epoch 1: val_accuracy improved from -inf to 0.63853, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 50s 707ms/step - loss: 1.4801 - accuracy: 0.4550 - val_loss: 0.9776 - val_accuracy: 0.6385 - lr: 1.0000e-05\n",
            "Epoch 2/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 1.0826 - accuracy: 0.6103\n",
            "Epoch 2: val_accuracy improved from 0.63853 to 0.69697, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 39s 549ms/step - loss: 1.0826 - accuracy: 0.6103 - val_loss: 0.7676 - val_accuracy: 0.6970 - lr: 1.0000e-05\n",
            "Epoch 3/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.8424 - accuracy: 0.7208\n",
            "Epoch 3: val_accuracy improved from 0.69697 to 0.74242, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 42s 595ms/step - loss: 0.8424 - accuracy: 0.7208 - val_loss: 0.7297 - val_accuracy: 0.7424 - lr: 1.0000e-05\n",
            "Epoch 4/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.7621 - accuracy: 0.7320\n",
            "Epoch 4: val_accuracy did not improve from 0.74242\n",
            "70/70 [==============================] - 38s 541ms/step - loss: 0.7621 - accuracy: 0.7320 - val_loss: 0.9195 - val_accuracy: 0.7208 - lr: 1.0000e-05\n",
            "Epoch 5/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.7282 - accuracy: 0.7414\n",
            "Epoch 5: val_accuracy improved from 0.74242 to 0.82035, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 38s 540ms/step - loss: 0.7282 - accuracy: 0.7414 - val_loss: 0.5209 - val_accuracy: 0.8203 - lr: 1.0000e-05\n",
            "Epoch 6/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.6005 - accuracy: 0.7937\n",
            "Epoch 6: val_accuracy did not improve from 0.82035\n",
            "70/70 [==============================] - 38s 541ms/step - loss: 0.6005 - accuracy: 0.7937 - val_loss: 0.7285 - val_accuracy: 0.7944 - lr: 1.0000e-05\n",
            "Epoch 7/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.5441 - accuracy: 0.8130\n",
            "Epoch 7: val_accuracy improved from 0.82035 to 0.84199, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 38s 535ms/step - loss: 0.5441 - accuracy: 0.8130 - val_loss: 0.6030 - val_accuracy: 0.8420 - lr: 1.0000e-05\n",
            "Epoch 8/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.4916 - accuracy: 0.8255\n",
            "Epoch 8: val_accuracy did not improve from 0.84199\n",
            "70/70 [==============================] - 38s 531ms/step - loss: 0.4916 - accuracy: 0.8255 - val_loss: 0.6415 - val_accuracy: 0.8095 - lr: 1.0000e-05\n",
            "Epoch 9/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.4587 - accuracy: 0.8376\n",
            "Epoch 9: val_accuracy improved from 0.84199 to 0.86580, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 38s 549ms/step - loss: 0.4587 - accuracy: 0.8376 - val_loss: 0.3655 - val_accuracy: 0.8658 - lr: 1.0000e-05\n",
            "Epoch 10/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.4326 - accuracy: 0.8367\n",
            "Epoch 10: val_accuracy did not improve from 0.86580\n",
            "70/70 [==============================] - 37s 534ms/step - loss: 0.4326 - accuracy: 0.8367 - val_loss: 0.4829 - val_accuracy: 0.8593 - lr: 1.0000e-05\n",
            "Epoch 11/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.3925 - accuracy: 0.8617\n",
            "Epoch 11: val_accuracy improved from 0.86580 to 0.87013, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 40s 567ms/step - loss: 0.3925 - accuracy: 0.8617 - val_loss: 0.4438 - val_accuracy: 0.8701 - lr: 1.0000e-05\n",
            "Epoch 12/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.3635 - accuracy: 0.8796\n",
            "Epoch 12: val_accuracy improved from 0.87013 to 0.87229, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 38s 539ms/step - loss: 0.3635 - accuracy: 0.8796 - val_loss: 0.3964 - val_accuracy: 0.8723 - lr: 1.0000e-05\n",
            "Epoch 13/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8792\n",
            "Epoch 13: val_accuracy improved from 0.87229 to 0.90260, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 38s 543ms/step - loss: 0.3443 - accuracy: 0.8792 - val_loss: 0.3119 - val_accuracy: 0.9026 - lr: 1.0000e-05\n",
            "Epoch 14/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.3208 - accuracy: 0.8872\n",
            "Epoch 14: val_accuracy did not improve from 0.90260\n",
            "70/70 [==============================] - 38s 536ms/step - loss: 0.3208 - accuracy: 0.8872 - val_loss: 0.7250 - val_accuracy: 0.8203 - lr: 1.0000e-05\n",
            "Epoch 15/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.3007 - accuracy: 0.8966\n",
            "Epoch 15: val_accuracy did not improve from 0.90260\n",
            "70/70 [==============================] - 37s 527ms/step - loss: 0.3007 - accuracy: 0.8966 - val_loss: 0.3796 - val_accuracy: 0.8831 - lr: 1.0000e-05\n",
            "Epoch 16/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.9020\n",
            "Epoch 16: val_accuracy did not improve from 0.90260\n",
            "70/70 [==============================] - 39s 558ms/step - loss: 0.2744 - accuracy: 0.9020 - val_loss: 0.3413 - val_accuracy: 0.8874 - lr: 1.0000e-05\n",
            "Epoch 17/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.2564 - accuracy: 0.9114\n",
            "Epoch 17: val_accuracy did not improve from 0.90260\n",
            "70/70 [==============================] - 36s 518ms/step - loss: 0.2564 - accuracy: 0.9114 - val_loss: 0.3479 - val_accuracy: 0.8853 - lr: 1.0000e-05\n",
            "Epoch 18/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.2408 - accuracy: 0.9204\n",
            "Epoch 18: val_accuracy did not improve from 0.90260\n",
            "70/70 [==============================] - 38s 536ms/step - loss: 0.2408 - accuracy: 0.9204 - val_loss: 0.5423 - val_accuracy: 0.8766 - lr: 1.0000e-05\n",
            "Epoch 19/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.2433 - accuracy: 0.9110\n",
            "Epoch 19: val_accuracy improved from 0.90260 to 0.93506, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 42s 603ms/step - loss: 0.2433 - accuracy: 0.9110 - val_loss: 0.2117 - val_accuracy: 0.9351 - lr: 1.0000e-05\n",
            "Epoch 20/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.2324 - accuracy: 0.9217\n",
            "Epoch 20: val_accuracy did not improve from 0.93506\n",
            "70/70 [==============================] - 39s 557ms/step - loss: 0.2324 - accuracy: 0.9217 - val_loss: 0.5505 - val_accuracy: 0.8658 - lr: 1.0000e-05\n",
            "Epoch 21/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.2129 - accuracy: 0.9248\n",
            "Epoch 21: val_accuracy did not improve from 0.93506\n",
            "70/70 [==============================] - 37s 524ms/step - loss: 0.2129 - accuracy: 0.9248 - val_loss: 0.2296 - val_accuracy: 0.9242 - lr: 1.0000e-05\n",
            "Epoch 22/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1933 - accuracy: 0.9351\n",
            "Epoch 22: val_accuracy did not improve from 0.93506\n",
            "70/70 [==============================] - 37s 522ms/step - loss: 0.1933 - accuracy: 0.9351 - val_loss: 0.3525 - val_accuracy: 0.9156 - lr: 1.0000e-05\n",
            "Epoch 23/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1724 - accuracy: 0.9369\n",
            "Epoch 23: val_accuracy improved from 0.93506 to 0.94156, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 37s 533ms/step - loss: 0.1724 - accuracy: 0.9369 - val_loss: 0.2247 - val_accuracy: 0.9416 - lr: 1.0000e-05\n",
            "Epoch 24/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1670 - accuracy: 0.9454\n",
            "Epoch 24: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 36s 506ms/step - loss: 0.1670 - accuracy: 0.9454 - val_loss: 0.2690 - val_accuracy: 0.9242 - lr: 1.0000e-05\n",
            "Epoch 25/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1584 - accuracy: 0.9418\n",
            "Epoch 25: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 38s 544ms/step - loss: 0.1584 - accuracy: 0.9418 - val_loss: 0.2415 - val_accuracy: 0.9372 - lr: 1.0000e-05\n",
            "Epoch 26/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1654 - accuracy: 0.9356\n",
            "Epoch 26: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 36s 513ms/step - loss: 0.1654 - accuracy: 0.9356 - val_loss: 0.3216 - val_accuracy: 0.9242 - lr: 1.0000e-05\n",
            "Epoch 27/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1453 - accuracy: 0.9463\n",
            "Epoch 27: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 37s 523ms/step - loss: 0.1453 - accuracy: 0.9463 - val_loss: 0.5141 - val_accuracy: 0.8961 - lr: 1.0000e-05\n",
            "Epoch 28/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1173 - accuracy: 0.9570\n",
            "Epoch 28: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 37s 528ms/step - loss: 0.1173 - accuracy: 0.9570 - val_loss: 0.3837 - val_accuracy: 0.9286 - lr: 1.0000e-05\n",
            "Epoch 29/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1336 - accuracy: 0.9548\n",
            "Epoch 29: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 38s 536ms/step - loss: 0.1336 - accuracy: 0.9548 - val_loss: 0.5573 - val_accuracy: 0.8701 - lr: 1.0000e-05\n",
            "Epoch 30/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1189 - accuracy: 0.9562\n",
            "Epoch 30: val_accuracy did not improve from 0.94156\n",
            "70/70 [==============================] - 36s 515ms/step - loss: 0.1189 - accuracy: 0.9562 - val_loss: 0.6066 - val_accuracy: 0.8701 - lr: 1.0000e-05\n",
            "Epoch 31/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1078 - accuracy: 0.9629\n",
            "Epoch 31: val_accuracy improved from 0.94156 to 0.94372, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 40s 565ms/step - loss: 0.1078 - accuracy: 0.9629 - val_loss: 0.2875 - val_accuracy: 0.9437 - lr: 1.0000e-05\n",
            "Epoch 32/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1091 - accuracy: 0.9606\n",
            "Epoch 32: val_accuracy did not improve from 0.94372\n",
            "70/70 [==============================] - 38s 536ms/step - loss: 0.1091 - accuracy: 0.9606 - val_loss: 0.5497 - val_accuracy: 0.8745 - lr: 1.0000e-05\n",
            "Epoch 33/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1059 - accuracy: 0.9642\n",
            "Epoch 33: val_accuracy did not improve from 0.94372\n",
            "70/70 [==============================] - 37s 522ms/step - loss: 0.1059 - accuracy: 0.9642 - val_loss: 0.2802 - val_accuracy: 0.9286 - lr: 1.0000e-05\n",
            "Epoch 34/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9664\n",
            "Epoch 34: val_accuracy improved from 0.94372 to 0.95022, saving model to ./bestmodel.h5\n",
            "70/70 [==============================] - 38s 543ms/step - loss: 0.0965 - accuracy: 0.9664 - val_loss: 0.1961 - val_accuracy: 0.9502 - lr: 1.0000e-05\n",
            "Epoch 35/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.1059 - accuracy: 0.9629\n",
            "Epoch 35: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 37s 522ms/step - loss: 0.1059 - accuracy: 0.9629 - val_loss: 0.2215 - val_accuracy: 0.9329 - lr: 1.0000e-05\n",
            "Epoch 36/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9624\n",
            "Epoch 36: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 40s 566ms/step - loss: 0.0997 - accuracy: 0.9624 - val_loss: 0.3480 - val_accuracy: 0.9134 - lr: 1.0000e-05\n",
            "Epoch 37/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0802 - accuracy: 0.9709\n",
            "Epoch 37: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 36s 510ms/step - loss: 0.0802 - accuracy: 0.9709 - val_loss: 0.2757 - val_accuracy: 0.9264 - lr: 1.0000e-05\n",
            "Epoch 38/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0887 - accuracy: 0.9714\n",
            "Epoch 38: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 36s 513ms/step - loss: 0.0887 - accuracy: 0.9714 - val_loss: 0.3924 - val_accuracy: 0.9221 - lr: 1.0000e-05\n",
            "Epoch 39/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0613 - accuracy: 0.9781\n",
            "Epoch 39: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 37s 521ms/step - loss: 0.0613 - accuracy: 0.9781 - val_loss: 0.3579 - val_accuracy: 0.9242 - lr: 1.0000e-05\n",
            "Epoch 40/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0590 - accuracy: 0.9812\n",
            "Epoch 40: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 36s 513ms/step - loss: 0.0590 - accuracy: 0.9812 - val_loss: 0.2499 - val_accuracy: 0.9351 - lr: 1.0000e-05\n",
            "Epoch 41/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0727 - accuracy: 0.9754\n",
            "Epoch 41: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 37s 528ms/step - loss: 0.0727 - accuracy: 0.9754 - val_loss: 0.3619 - val_accuracy: 0.9177 - lr: 1.0000e-05\n",
            "Epoch 42/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0747 - accuracy: 0.9754\n",
            "Epoch 42: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 37s 527ms/step - loss: 0.0747 - accuracy: 0.9754 - val_loss: 0.7052 - val_accuracy: 0.8593 - lr: 1.0000e-05\n",
            "Epoch 43/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0720 - accuracy: 0.9732\n",
            "Epoch 43: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 36s 513ms/step - loss: 0.0720 - accuracy: 0.9732 - val_loss: 0.3679 - val_accuracy: 0.9264 - lr: 1.0000e-05\n",
            "Epoch 44/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0786 - accuracy: 0.9732\n",
            "Epoch 44: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 37s 527ms/step - loss: 0.0786 - accuracy: 0.9732 - val_loss: 0.3473 - val_accuracy: 0.9372 - lr: 1.0000e-05\n",
            "Epoch 45/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0678 - accuracy: 0.9790\n",
            "Epoch 45: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 36s 518ms/step - loss: 0.0678 - accuracy: 0.9790 - val_loss: 0.6502 - val_accuracy: 0.9026 - lr: 1.0000e-05\n",
            "Epoch 46/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0541 - accuracy: 0.9790\n",
            "Epoch 46: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 38s 539ms/step - loss: 0.0541 - accuracy: 0.9790 - val_loss: 0.3447 - val_accuracy: 0.9242 - lr: 1.0000e-05\n",
            "Epoch 47/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0580 - accuracy: 0.9790\n",
            "Epoch 47: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 37s 531ms/step - loss: 0.0580 - accuracy: 0.9790 - val_loss: 0.3739 - val_accuracy: 0.9221 - lr: 1.0000e-05\n",
            "Epoch 48/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0513 - accuracy: 0.9803\n",
            "Epoch 48: val_accuracy did not improve from 0.95022\n",
            "70/70 [==============================] - 36s 513ms/step - loss: 0.0513 - accuracy: 0.9803 - val_loss: 0.4263 - val_accuracy: 0.9091 - lr: 1.0000e-05\n",
            "Epoch 49/100\n",
            "70/70 [==============================] - ETA: 0s - loss: 0.0514 - accuracy: 0.9821\n",
            "Epoch 49: val_accuracy did not improve from 0.95022\n",
            "Restoring model weights from the end of the best epoch: 34.\n",
            "70/70 [==============================] - 36s 509ms/step - loss: 0.0514 - accuracy: 0.9821 - val_loss: 0.4518 - val_accuracy: 0.9177 - lr: 1.0000e-05\n",
            "Epoch 49: early stopping\n",
            "15/15 [==============================] - 2s 140ms/step - loss: 0.2746 - accuracy: 0.9221\n",
            "Test accuracy: 0.9221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "model = load_model(\"/content/bestmodel.h5\")"
      ],
      "metadata": {
        "id": "qD2AwoXv8Xgd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Evaluate validation accuracy\n",
        "val_loss, val_acc = model.evaluate(val_generator, steps=len(val_generator))\n",
        "print(\"Validation accuracy:\", val_acc)\n",
        "\n",
        "# Evaluate training accuracy\n",
        "train_loss, train_acc = model.evaluate(train_generator, steps=len(train_generator))\n",
        "print(\"Training accuracy:\", train_acc)\n",
        "\n",
        "# Plot training history\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot accuracy\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# Plot loss\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Calculate overfitting rate\n",
        "overfitting_rate = 1 - val_acc / train_acc\n",
        "print(\"Overfitting rate:\", overfitting_rate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "RkHwS_IE3xs8",
        "outputId": "0691f89e-a095-481f-d06e-7e99cd95b16e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 2s 128ms/step - loss: 0.1961 - accuracy: 0.9502\n",
            "Validation accuracy: 0.9502164721488953\n",
            "70/70 [==============================] - 34s 475ms/step - loss: 0.0731 - accuracy: 0.9758\n",
            "Training accuracy: 0.9758388996124268\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overfitting rate: 0.026256821155323795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "from tensorflow.keras.utils import load_img, img_to_array\n",
        "import numpy as np\n",
        "\n",
        "# Load the trained model\n",
        "model = load_model(\"/content/bestmodel.h5\")\n",
        "\n",
        "# Load and preprocess the input image\n",
        "image_path = \"/content/Datathon-Dataset/category3_tumor/pt_img (539).jpg\"  # Change to the actual path\n",
        "img = load_img(image_path, target_size=(224, 224))\n",
        "input_arr = img_to_array(img) / 255.0\n",
        "input_arr = np.expand_dims(input_arr, axis=0)  # Add batch dimension\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(input_arr)\n",
        "\n",
        "# Interpret the predictions\n",
        "class_labels = [\"cancer_category_1\", \"cancer_category_2\", \"cancer_category_3\", \"no cancer\"]\n",
        "predicted_class = class_labels[np.argmax(predictions)]\n",
        "\n",
        "print(f\"The predicted class is: {predicted_class}\")"
      ],
      "metadata": {
        "id": "ii4U-DtUOZw5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}