model_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Load and Explore the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  File Name        Class\n",
      "0     1.jpg        other\n",
      "1     2.jpg  bright dune\n",
      "2     3.jpg        other\n",
      "3     4.jpg       crater\n",
      "4     5.jpg        other\n",
      "Class\n",
      "other            3651\n",
      "crater           1062\n",
      "bright dune       597\n",
      "slope streak      335\n",
      "swiss cheese      223\n",
      "dark dune         216\n",
      "spider             66\n",
      "impact ejecta      51\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "train_df = pd.read_csv('train.csv')\n",
    "\n",
    "# Display the first few rows of the dataframe\n",
    "print(train_df.head())\n",
    "\n",
    "# Print class distribution\n",
    "print(train_df['Class'].value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Preprocess the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6201/6201 [01:05<00:00, 94.61it/s] \n",
      "100%|██████████| 2000/2000 [00:20<00:00, 97.93it/s] \n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Directory paths (update with actual paths)\n",
    "train_dir = 'train_dataset'  # Update with the actual path\n",
    "test_dir = 'test_dataset'    # Update with the actual path\n",
    "\n",
    "# Parameters\n",
    "image_size = (227, 227)  # Standard size for all images\n",
    "\n",
    "# Function to preprocess images\n",
    "def preprocess_images(directory, image_size):\n",
    "    images = []\n",
    "    filenames = []\n",
    "    for filename in tqdm(os.listdir(directory)):\n",
    "        filepath = os.path.join(directory, filename)\n",
    "        image = cv2.imread(filepath)\n",
    "        if image is not None:\n",
    "            image = cv2.resize(image, image_size)\n",
    "            image = image / 255.0  # Normalize pixel values\n",
    "            images.append(image)\n",
    "            filenames.append(filename)\n",
    "    return np.array(images), filenames\n",
    "\n",
    "# Preprocess train and test images\n",
    "train_images, train_filenames = preprocess_images(train_dir, image_size)\n",
    "test_images, test_filenames = preprocess_images(test_dir, image_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Handle Class Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Data augmentation\n",
    "datagen = ImageDataGenerator(\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",
    "# Prepare labels\n",
    "train_labels = train_df['Class'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Split the Data into Training and Validation Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "train_images, val_images, train_labels, val_labels = train_test_split(\n",
    "    train_images, train_labels, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create data generators for training and validation sets\n",
    "train_generator = datagen.flow(train_images, train_labels, batch_size=32)\n",
    "validation_generator = datagen.flow(val_images, val_labels, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Build and Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "155/155 [==============================] - 172s 1s/step - loss: 1.3700 - accuracy: 0.5804 - val_loss: 1.3245 - val_accuracy: 0.5947\n",
      "Epoch 2/10\n",
      "155/155 [==============================] - 197s 1s/step - loss: 1.3463 - accuracy: 0.5871 - val_loss: 1.3243 - val_accuracy: 0.5955\n",
      "Epoch 3/10\n",
      "155/155 [==============================] - 198s 1s/step - loss: 1.3356 - accuracy: 0.5871 - val_loss: 1.3221 - val_accuracy: 0.5955\n",
      "Epoch 4/10\n",
      "155/155 [==============================] - 215s 1s/step - loss: 1.3331 - accuracy: 0.5871 - val_loss: 1.3155 - val_accuracy: 0.5955\n",
      "Epoch 5/10\n",
      "155/155 [==============================] - 212s 1s/step - loss: 1.3332 - accuracy: 0.5873 - val_loss: 1.3061 - val_accuracy: 0.5955\n",
      "Epoch 6/10\n",
      "155/155 [==============================] - 208s 1s/step - loss: 1.3298 - accuracy: 0.5871 - val_loss: 1.3067 - val_accuracy: 0.5955\n",
      "Epoch 7/10\n",
      "155/155 [==============================] - 208s 1s/step - loss: 1.3258 - accuracy: 0.5871 - val_loss: 1.3083 - val_accuracy: 0.5955\n",
      "Epoch 8/10\n",
      "155/155 [==============================] - 210s 1s/step - loss: 1.3251 - accuracy: 0.5873 - val_loss: 1.3215 - val_accuracy: 0.5955\n",
      "Epoch 9/10\n",
      "155/155 [==============================] - 205s 1s/step - loss: 1.3262 - accuracy: 0.5871 - val_loss: 1.3161 - val_accuracy: 0.5955\n",
      "Epoch 10/10\n",
      "155/155 [==============================] - 209s 1s/step - loss: 1.3244 - accuracy: 0.5873 - val_loss: 1.3040 - val_accuracy: 0.5955\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.applications import VGG16\n",
    "\n",
    "# Load the pre-trained model\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n",
    "\n",
    "# Freeze the base model\n",
    "base_model.trainable = False\n",
    "\n",
    "# Build the model\n",
    "model = Sequential([\n",
    "    base_model,\n",
    "    GlobalAveragePooling2D(),\n",
    "    Dense(512, activation='relu'),\n",
    "    Dense(8, activation='softmax')  # 8 classes\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(train_generator, epochs=10, validation_data=validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 13). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_1\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_1\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('model_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39/39 [==============================] - 41s 1s/step\n",
      "Validation Accuracy: 59.55%\n"
     ]
    }
   ],
   "source": [
    "# Predict the labels of the validation set\n",
    "val_predictions = model.predict(val_images)\n",
    "val_pred_labels = np.argmax(val_predictions, axis=1)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = np.sum(val_labels == val_pred_labels) / len(val_labels)\n",
    "print(f\"Validation Accuracy: {accuracy * 100:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "All arrays must be of the same length",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Create a DataFrame to store filenames and their predicted classes\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m results_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mFileName\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_filenames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPredictedClass\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_pred_labels\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Save the DataFrame to a CSV file\u001b[39;00m\n\u001b[0;32m      8\u001b[0m results_df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_predictions.csv\u001b[39m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\utkar\\.conda\\envs\\mars_landscape_env\\lib\\site-packages\\pandas\\core\\frame.py:778\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    772\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m    773\u001b[0m         data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m    774\u001b[0m     )\n\u001b[0;32m    776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m    777\u001b[0m     \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 778\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    779\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m    780\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
      "File \u001b[1;32mc:\\Users\\utkar\\.conda\\envs\\mars_landscape_env\\lib\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m    499\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    500\u001b[0m         \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[0;32m    501\u001b[0m         arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\utkar\\.conda\\envs\\mars_landscape_env\\lib\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m    112\u001b[0m     \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 114\u001b[0m         index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    116\u001b[0m         index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
      "File \u001b[1;32mc:\\Users\\utkar\\.conda\\envs\\mars_landscape_env\\lib\\site-packages\\pandas\\core\\internals\\construction.py:677\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    675\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[0;32m    676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[0;32m    680\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    681\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    682\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: All arrays must be of the same length"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame to store filenames and their predicted classes\n",
    "results_df = pd.DataFrame({\n",
    "    'FileName': test_filenames,\n",
    "    'PredictedClass': val_pred_labels\n",
    "})\n",
    "\n",
    "# Save the DataFrame to a CSV file\n",
    "results_df.to_csv('test_predictions.csv', index=False)\n",
    "\n",
    "print(\"Test predictions saved to 'test_predictions.csv'\")\n"
   ]
  }
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