Image_NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "\n",
    "# Open the HDF5 file in read mode\n",
    "h5_file = h5py.File('Autonomous_Vehicles_SVHN_single_grey1.h5', 'r')\n",
    "\n",
    "# List of dataset names\n",
    "dataset_names = ['X_test', 'X_train', 'X_val', 'y_test', 'y_train', 'y_val']\n",
    "\n",
    "# Read each dataset and store them in variables\n",
    "datasets = {}\n",
    "for dataset_name in dataset_names:\n",
    "    datasets[dataset_name] = h5_file[dataset_name][()]\n",
    "\n",
    "# Close the HDF5 file\n",
    "h5_file.close()\n",
    "\n",
    "# Access the datasets using the variables\n",
    "X_test = datasets['X_test']\n",
    "X_train = datasets['X_train']\n",
    "X_val = datasets['X_val']\n",
    "y_test = datasets['y_test']\n",
    "y_train = datasets['y_train']\n",
    "y_val = datasets['y_val']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 32, 32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assuming your labels are stored in y_train\n",
    "# Load labels corresponding to the first 10 images\n",
    "labels = y_train[:10]\n",
    "\n",
    "# Visualize the first 10 images\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    plt.imshow(X_train[i], cmap='gray')  # Assuming images are grayscale\n",
    "    plt.title(f'Label: {labels[i]}')\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of images: 60000\n",
      "Image dimensions: (32, 32)\n"
     ]
    }
   ],
   "source": [
    "num_images = X_val.shape[0]\n",
    "image_shape = X_val.shape[1:]\n",
    "\n",
    "print(\"Total number of images:\", num_images)\n",
    "print(\"Image dimensions:\", image_shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_reshaped = X_train.reshape(-1, 32, 32, 1)\n",
    "\n",
    "X_test_reshaped = X_test.reshape(-1, 32, 32, 1)\n",
    "X_val_reshaped = X_val.reshape(-1, 32, 32, 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_reshaped shape: (42000, 32, 32, 1)\n",
      "X_test_reshaped shape: (18000, 32, 32, 1)\n",
      "X_val_reshaped shape: (60000, 32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train_reshaped shape:\", X_train_reshaped.shape)\n",
    "print(\"X_test_reshaped shape:\", X_test_reshaped.shape)\n",
    "print(\"X_val_reshaped shape:\", X_val_reshaped.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_normalized = X_train_reshaped / 255.0\n",
    "X_test_normalized = X_test_reshaped / 255.0\n",
    "X_val_normalized = X_val_reshaped / 255.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of classes: 10\n"
     ]
    }
   ],
   "source": [
    "# Identifying number of unique classes in y test and y train\n",
    "\n",
    "unique_classes = np.unique(y_train)\n",
    "num_classes = len(unique_classes)\n",
    "\n",
    "print(\"Total number of classes:\", num_classes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Convert integer labels to one-hot encoded format\n",
    "num_classes = 10  # Replace with the actual number of classes\n",
    "y_train_encoded = to_categorical(y_train, num_classes=num_classes)\n",
    "y_test_encoded = to_categorical(y_test, num_classes=num_classes)\n",
    "y_val_encoded = to_categorical(y_val, num_classes=num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "657/657 [==============================] - 103s 153ms/step - loss: 1.1017 - accuracy: 0.6488 - val_loss: 0.5820 - val_accuracy: 0.8430\n",
      "Epoch 2/10\n",
      "657/657 [==============================] - 99s 151ms/step - loss: 0.5529 - accuracy: 0.8486 - val_loss: 0.5281 - val_accuracy: 0.8516\n",
      "Epoch 3/10\n",
      "657/657 [==============================] - 100s 152ms/step - loss: 0.4742 - accuracy: 0.8691 - val_loss: 0.4417 - val_accuracy: 0.8800\n",
      "Epoch 4/10\n",
      "657/657 [==============================] - 108s 165ms/step - loss: 0.4163 - accuracy: 0.8819 - val_loss: 0.4106 - val_accuracy: 0.8819\n",
      "Epoch 5/10\n",
      "657/657 [==============================] - 148s 225ms/step - loss: 0.3733 - accuracy: 0.8935 - val_loss: 0.3462 - val_accuracy: 0.9036\n",
      "Epoch 6/10\n",
      "657/657 [==============================] - 137s 209ms/step - loss: 0.3383 - accuracy: 0.9011 - val_loss: 0.3615 - val_accuracy: 0.8949\n",
      "Epoch 7/10\n",
      "657/657 [==============================] - 112s 170ms/step - loss: 0.3088 - accuracy: 0.9104 - val_loss: 0.3225 - val_accuracy: 0.9064\n",
      "Epoch 8/10\n",
      "657/657 [==============================] - 99s 150ms/step - loss: 0.2847 - accuracy: 0.9157 - val_loss: 0.2860 - val_accuracy: 0.9190\n",
      "Epoch 9/10\n",
      "657/657 [==============================] - 103s 156ms/step - loss: 0.2585 - accuracy: 0.9225 - val_loss: 0.2715 - val_accuracy: 0.9241\n",
      "Epoch 10/10\n",
      "657/657 [==============================] - 92s 140ms/step - loss: 0.2386 - accuracy: 0.9296 - val_loss: 0.2591 - val_accuracy: 0.9286\n",
      "563/563 [==============================] - 8s 14ms/step - loss: 0.3978 - accuracy: 0.8961\n",
      "Loss on test set: 0.3977641761302948\n",
      "Accuracy on test set: 0.8960555791854858\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Load and preprocess your data\n",
    "# X_train_reshaped, y_train_encoded, X_test_reshaped, y_test_encoded = ...\n",
    "\n",
    "# Create a Sequential model\n",
    "model = Sequential()\n",
    "\n",
    "# Add layers to the model\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 1)))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))  # Output layer with softmax activation\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model with a validation set\n",
    "model.fit(X_train_normalized, y_train_encoded, epochs=10, batch_size=64, validation_data=(X_val_normalized, y_val_encoded))\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test_normalized, y_test_encoded)\n",
    "print(f'Loss on test set: {loss}')\n",
    "print(f'Accuracy on test set: {accuracy}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 8s 13ms/step\n",
      "Accuracy: 0.8960555555555556\n",
      "Precision: 0.8964574671215205\n",
      "Recall: 0.8960555555555556\n",
      "F1-Score: 0.8959513894982148\n",
      "Confusion Matrix:\n",
      "[[1691   22   10    9   13    3   21   10   10   25]\n",
      " [  23 1619    5   23   69   11   13   42   15    8]\n",
      " [  10   14 1614   21   22    5    5   43   25   44]\n",
      " [   8   23   22 1435   16  110   22   23   42   18]\n",
      " [   8   22   10    8 1685    9   20   11   16   23]\n",
      " [   2   12    7   45    9 1613   35   10   19   16]\n",
      " [  26    9    7   10   28   75 1609    6   54    8]\n",
      " [   8   25   18   20   17    9    3 1687    7   14]\n",
      " [  23   27    4   29   20   20   65   12 1586   26]\n",
      " [  35   14   21   29   25   31   14   17   28 1590]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "y_pred = model.predict(X_test_normalized)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "y_test_classes = np.argmax(y_test_encoded, axis=1)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test_classes, y_pred_classes)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision = precision_score(y_test_classes, y_pred_classes, average='weighted')\n",
    "recall = recall_score(y_test_classes, y_pred_classes, average='weighted')\n",
    "f1 = f1_score(y_test_classes, y_pred_classes, average='weighted')\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-Score:\", f1)\n",
    "\n",
    "# Generate confusion matrix\n",
    "confusion_mtx = confusion_matrix(y_test_classes, y_pred_classes)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_mtx)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "657/657 [==============================] - 94s 144ms/step - loss: 0.2201 - accuracy: 0.9356 - val_loss: 0.2660 - val_accuracy: 0.9255\n",
      "Epoch 2/10\n",
      "657/657 [==============================] - 88s 134ms/step - loss: 0.2009 - accuracy: 0.9406 - val_loss: 0.2508 - val_accuracy: 0.9304\n",
      "Epoch 3/10\n",
      "657/657 [==============================] - 93s 142ms/step - loss: 0.1835 - accuracy: 0.9465 - val_loss: 0.2219 - val_accuracy: 0.9410\n",
      "Epoch 4/10\n",
      "657/657 [==============================] - 104s 158ms/step - loss: 0.1652 - accuracy: 0.9505 - val_loss: 0.2179 - val_accuracy: 0.9432\n",
      "Epoch 5/10\n",
      "657/657 [==============================] - 105s 159ms/step - loss: 0.1526 - accuracy: 0.9548 - val_loss: 0.2312 - val_accuracy: 0.9381\n",
      "Epoch 6/10\n",
      "657/657 [==============================] - 79s 120ms/step - loss: 0.1389 - accuracy: 0.9578 - val_loss: 0.2160 - val_accuracy: 0.9437\n",
      "Epoch 7/10\n",
      "657/657 [==============================] - 78s 118ms/step - loss: 0.1258 - accuracy: 0.9626 - val_loss: 0.2098 - val_accuracy: 0.9479\n",
      "Epoch 8/10\n",
      "657/657 [==============================] - 82s 125ms/step - loss: 0.1119 - accuracy: 0.9670 - val_loss: 0.2098 - val_accuracy: 0.9505\n",
      "Epoch 9/10\n",
      "657/657 [==============================] - 87s 133ms/step - loss: 0.1016 - accuracy: 0.9695 - val_loss: 0.2073 - val_accuracy: 0.9525\n",
      "Epoch 10/10\n",
      "657/657 [==============================] - 86s 131ms/step - loss: 0.0928 - accuracy: 0.9713 - val_loss: 0.2262 - val_accuracy: 0.9499\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Get training history from the model.fit() function\n",
    "history = model.fit(X_train_normalized, y_train_encoded, epochs=10, batch_size=64, validation_data=(X_val_normalized, y_val_encoded))\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Plot training and validation accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}