model.ipynb

{
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     "text": [
      "Found 7112 images belonging to 2 classes.\n",
      "Found 1114 images belonging to 2 classes.\n",
      "Train generator class indices: {'Normal': 0, 'With Glaucoma': 1}\n",
      "Validation generator class indices: {'Normal': 0, 'With Glaucoma': 1}\n",
      "Number of train images: 7112\n",
      "Number of validation images: 1114\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ramkrishna Das\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ramkrishna Das\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 738ms/step - accuracy: 0.9741 - loss: 0.1791 - val_accuracy: 0.9022 - val_loss: 0.0869\n",
      "Epoch 2/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 3/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ramkrishna Das\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n",
      "c:\\Users\\Ramkrishna Das\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\callbacks\\model_checkpoint.py:206: UserWarning: Can save best model only with val_accuracy available, skipping.\n",
      "  self._save_model(epoch=epoch, batch=None, logs=logs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 596ms/step - accuracy: 0.9863 - loss: 0.0141 - val_accuracy: 0.9937 - val_loss: 0.0650\n",
      "Epoch 4/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 5/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 596ms/step - accuracy: 0.9993 - loss: 0.0098 - val_accuracy: 0.9892 - val_loss: 0.0501\n",
      "Epoch 6/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 7/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 590ms/step - accuracy: 0.9986 - loss: 0.0058 - val_accuracy: 1.0000 - val_loss: 0.0086\n",
      "Epoch 8/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 9/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 609ms/step - accuracy: 0.9982 - loss: 0.0071 - val_accuracy: 0.9964 - val_loss: 0.0096\n",
      "Epoch 10/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 11/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 620ms/step - accuracy: 0.9996 - loss: 0.0018 - val_accuracy: 0.9982 - val_loss: 0.0091\n",
      "Epoch 12/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 13/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 587ms/step - accuracy: 0.9993 - loss: 0.0047 - val_accuracy: 0.9991 - val_loss: 0.0062\n",
      "Epoch 14/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 15/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 594ms/step - accuracy: 0.9994 - loss: 0.0028 - val_accuracy: 0.9991 - val_loss: 0.0033\n",
      "Epoch 16/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 17/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 600ms/step - accuracy: 0.9998 - loss: 9.3304e-04 - val_accuracy: 0.9991 - val_loss: 0.0025\n",
      "Epoch 18/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 19/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 595ms/step - accuracy: 0.9993 - loss: 0.0060 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 20/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 21/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 588ms/step - accuracy: 0.9995 - loss: 0.0021 - val_accuracy: 1.0000 - val_loss: 0.0016\n",
      "Epoch 22/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 23/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 600ms/step - accuracy: 0.9996 - loss: 0.0015 - val_accuracy: 0.9964 - val_loss: 0.0101\n",
      "Epoch 24/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 25/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 605ms/step - accuracy: 0.9991 - loss: 0.0041 - val_accuracy: 1.0000 - val_loss: 0.0014\n",
      "Epoch 26/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 27/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 592ms/step - accuracy: 0.9992 - loss: 0.0028 - val_accuracy: 0.9991 - val_loss: 0.0041\n",
      "Epoch 28/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n",
      "Epoch 29/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 592ms/step - accuracy: 0.9997 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 30/30\n",
      "\u001b[1m223/223\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# Paths to your image folders\n",
    "train_data_dir = r'C:\\Users\\Ramkrishna Das\\Desktop\\Final Output\\Train' \n",
    "validation_data_dir = r'C:\\Users\\Ramkrishna Das\\Desktop\\Final Output\\Validation'\n",
    "\n",
    "# Image Data Generator for Data Augmentation\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1.0/255.0,\n",
    "    rotation_range=20,\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",
    "validation_datagen = ImageDataGenerator(rescale=1.0/255.0)\n",
    "\n",
    "# Training and Validation Generators\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size=(64, 64),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "    validation_data_dir,\n",
    "    target_size=(64, 64),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "# Print information about the datasets\n",
    "print('Train generator class indices:', train_generator.class_indices)\n",
    "print('Validation generator class indices:', validation_generator.class_indices)\n",
    "print('Number of train images:', len(train_generator.filenames))\n",
    "print('Number of validation images:', len(validation_generator.filenames))\n",
    "\n",
    "# CNN Model\n",
    "model = Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Conv2D(64, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Conv2D(128, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Flatten(),\n",
    "    \n",
    "    Dense(256, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    \n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(learning_rate=0.0001),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Model Checkpoint to save the best model\n",
    "checkpoint = ModelCheckpoint('glaucoma_model.keras', monitor='val_accuracy', save_best_only=True, mode='max')\n",
    "\n",
    "# Training the model\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=len(train_generator),\n",
    "    epochs=30,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=len(validation_generator),\n",
    "    callbacks=[checkpoint]\n",
    ")\n"
   ]
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