mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fa55ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
    "\n",
    "# Load the MNIST dataset\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "# Preprocess the data\n",
    "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255\n",
    "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255\n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)\n",
    "\n",
    "\n",
    "\n",
    "# Split data for classical ML models\n",
    "X_train_flat = X_train.reshape(X_train.shape[0], -1)\n",
    "X_test_flat = X_test.reshape(X_test.shape[0], -1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00aef697",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Halil\\anaconda3\\Lib\\site-packages\\matplotlib\\text.py:1279: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if s != self._text:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Function to plot images in a grid\n",
    "def plot_images(images, labels, rows=2, cols=5):\n",
    "    fig, axes = plt.subplots(rows, cols, figsize=(10, 5))\n",
    "    axes = axes.ravel()\n",
    "    \n",
    "    for i in np.arange(0, rows*cols):\n",
    "        axes[i].imshow(images[i], cmap='gray')\n",
    "        axes[i].set_title(labels[i])\n",
    "        axes[i].axis('off')\n",
    "    \n",
    "    plt.subplots_adjust(hspace=0.5)\n",
    "    plt.show()\n",
    "\n",
    "# Display first 10 images from the training dataset\n",
    "plot_images(X_train, y_train, rows=2, cols=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38a61751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(X_train[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f294ef8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 30 görüntüyü görselleştirelim\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "for i in range(30):\n",
    "    plt.subplot(6,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(X_train[i], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "921bea79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.9712\n"
     ]
    }
   ],
   "source": [
    "# Train Random Forest\n",
    "rf_model = RandomForestClassifier(n_estimators=100)\n",
    "rf_model.fit(X_train_flat, y_train.argmax(axis=1))\n",
    "\n",
    "# Evaluate Random Forest\n",
    "y_pred_rf = rf_model.predict(X_test_flat)\n",
    "accuracy_rf = accuracy_score(y_test.argmax(axis=1), y_pred_rf)\n",
    "print(f\"Random Forest Accuracy: {accuracy_rf:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb664cda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC (Linear Kernel) Accuracy: 0.9404\n",
      "SVC (RBF Kernel) Accuracy: 0.9792\n"
     ]
    }
   ],
   "source": [
    "# Train SVC with linear kernel\n",
    "svc_linear = SVC(kernel='linear')\n",
    "svc_linear.fit(X_train_flat, y_train.argmax(axis=1))\n",
    "\n",
    "# Evaluate SVC with linear kernel\n",
    "y_pred_svc_linear = svc_linear.predict(X_test_flat)\n",
    "accuracy_svc_linear = accuracy_score(y_test.argmax(axis=1), y_pred_svc_linear)\n",
    "print(f\"SVC (Linear Kernel) Accuracy: {accuracy_svc_linear:.4f}\")\n",
    "\n",
    "# Train SVC with RBF kernel\n",
    "svc_rbf = SVC(kernel='rbf')\n",
    "svc_rbf.fit(X_train_flat, y_train.argmax(axis=1))\n",
    "\n",
    "# Evaluate SVC with RBF kernel\n",
    "y_pred_svc_rbf = svc_rbf.predict(X_test_flat)\n",
    "accuracy_svc_rbf = accuracy_score(y_test.argmax(axis=1), y_pred_svc_rbf)\n",
    "print(f\"SVC (RBF Kernel) Accuracy: {accuracy_svc_rbf:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48810d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Halil\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: 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__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "300/300 - 4s - 15ms/step - accuracy: 0.9271 - loss: 0.2534 - val_accuracy: 0.9647 - val_loss: 0.1150\n",
      "Epoch 2/10\n",
      "300/300 - 2s - 8ms/step - accuracy: 0.9730 - loss: 0.0914 - val_accuracy: 0.9756 - val_loss: 0.0787\n",
      "Epoch 3/10\n",
      "300/300 - 2s - 8ms/step - accuracy: 0.9827 - loss: 0.0566 - val_accuracy: 0.9758 - val_loss: 0.0747\n",
      "Epoch 4/10\n",
      "300/300 - 3s - 9ms/step - accuracy: 0.9883 - loss: 0.0383 - val_accuracy: 0.9800 - val_loss: 0.0661\n",
      "Epoch 5/10\n",
      "300/300 - 3s - 9ms/step - accuracy: 0.9912 - loss: 0.0276 - val_accuracy: 0.9782 - val_loss: 0.0687\n",
      "Epoch 6/10\n",
      "300/300 - 3s - 8ms/step - accuracy: 0.9927 - loss: 0.0229 - val_accuracy: 0.9787 - val_loss: 0.0689\n",
      "Epoch 7/10\n",
      "300/300 - 2s - 8ms/step - accuracy: 0.9953 - loss: 0.0158 - val_accuracy: 0.9791 - val_loss: 0.0765\n",
      "Epoch 8/10\n",
      "300/300 - 3s - 9ms/step - accuracy: 0.9962 - loss: 0.0119 - val_accuracy: 0.9789 - val_loss: 0.0739\n",
      "Epoch 9/10\n",
      "300/300 - 3s - 10ms/step - accuracy: 0.9954 - loss: 0.0135 - val_accuracy: 0.9786 - val_loss: 0.0827\n",
      "Epoch 10/10\n",
      "300/300 - 3s - 9ms/step - accuracy: 0.9958 - loss: 0.0127 - val_accuracy: 0.9782 - val_loss: 0.0906\n",
      "MLP Accuracy: 0.9782\n"
     ]
    }
   ],
   "source": [
    "# Define MLP model\n",
    "mlp_model = Sequential()\n",
    "mlp_model.add(Flatten(input_shape=(28, 28, 1)))\n",
    "mlp_model.add(Dense(512, activation='relu'))\n",
    "mlp_model.add(Dense(256, activation='relu'))\n",
    "mlp_model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "# Compile MLP model\n",
    "mlp_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train MLP model\n",
    "mlp_model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)\n",
    "\n",
    "# Evaluate MLP model\n",
    "scores_mlp = mlp_model.evaluate(X_test, y_test, verbose=0)\n",
    "accuracy_mlp = scores_mlp[1]\n",
    "print(f\"MLP Accuracy: {accuracy_mlp:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "027a1704",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Halil\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: 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__(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "300/300 - 16s - 54ms/step - accuracy: 0.9482 - loss: 0.1762 - val_accuracy: 0.9833 - val_loss: 0.0547\n",
      "Epoch 2/10\n",
      "300/300 - 13s - 42ms/step - accuracy: 0.9859 - loss: 0.0448 - val_accuracy: 0.9894 - val_loss: 0.0320\n",
      "Epoch 3/10\n",
      "300/300 - 13s - 43ms/step - accuracy: 0.9908 - loss: 0.0285 - val_accuracy: 0.9906 - val_loss: 0.0261\n",
      "Epoch 4/10\n",
      "300/300 - 14s - 48ms/step - accuracy: 0.9930 - loss: 0.0215 - val_accuracy: 0.9927 - val_loss: 0.0227\n",
      "Epoch 5/10\n",
      "300/300 - 17s - 55ms/step - accuracy: 0.9946 - loss: 0.0166 - val_accuracy: 0.9905 - val_loss: 0.0275\n",
      "Epoch 6/10\n",
      "300/300 - 16s - 54ms/step - accuracy: 0.9960 - loss: 0.0129 - val_accuracy: 0.9922 - val_loss: 0.0242\n",
      "Epoch 7/10\n",
      "300/300 - 14s - 46ms/step - accuracy: 0.9966 - loss: 0.0111 - val_accuracy: 0.9906 - val_loss: 0.0338\n",
      "Epoch 8/10\n",
      "300/300 - 14s - 46ms/step - accuracy: 0.9964 - loss: 0.0106 - val_accuracy: 0.9922 - val_loss: 0.0260\n",
      "Epoch 9/10\n",
      "300/300 - 15s - 49ms/step - accuracy: 0.9978 - loss: 0.0068 - val_accuracy: 0.9935 - val_loss: 0.0264\n",
      "Epoch 10/10\n",
      "300/300 - 14s - 48ms/step - accuracy: 0.9976 - loss: 0.0071 - val_accuracy: 0.9931 - val_loss: 0.0253\n",
      "CNN Accuracy: 0.9931\n"
     ]
    }
   ],
   "source": [
    "# Define CNN model\n",
    "cnn_model = Sequential()\n",
    "cnn_model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))\n",
    "cnn_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "cnn_model.add(Conv2D(64, (5, 5), activation='relu'))\n",
    "cnn_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "cnn_model.add(Flatten())\n",
    "cnn_model.add(Dense(1000, activation='relu'))\n",
    "cnn_model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "# Compile CNN model\n",
    "cnn_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train CNN model\n",
    "cnn_model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)\n",
    "\n",
    "# Evaluate CNN model\n",
    "scores_cnn = cnn_model.evaluate(X_test, y_test, verbose=0)\n",
    "accuracy_cnn = scores_cnn[1]\n",
    "print(f\"CNN Accuracy: {accuracy_cnn:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "259a0f34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest: 0.9712\n",
      "SVC (Linear Kernel): 0.9404\n",
      "SVC (RBF Kernel): 0.9792\n",
      "MLP: 0.9782\n",
      "CNN: 0.9931\n"
     ]
    }
   ],
   "source": [
    "results = {\n",
    "    \"Random Forest\": accuracy_rf,\n",
    "    \"SVC (Linear Kernel)\": accuracy_svc_linear,\n",
    "    \"SVC (RBF Kernel)\": accuracy_svc_rbf,\n",
    "    \"MLP\": accuracy_mlp,\n",
    "    \"CNN\": accuracy_cnn\n",
    "}\n",
    "\n",
    "for model, accuracy in results.items():\n",
    "    print(f\"{model}: {accuracy:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f95245f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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