resimlerde_nesne_tanıma(cifar).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e9b41b6-010e-40ee-97ca-f706be22f856",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb6cd35c-d1ed-4e72-be82-d4dde5579f6e",
   "metadata": {},
   "source": [
    "dataset indirilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "651c5476-eb0b-4895-ab39-4408a7bd8f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15d822fd-d909-4818-af63-4bf11563ac77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2371678-0579-4ba7-8bef-de855ef667ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1e46e8-8e21-4f6e-92b9-9451359c372a",
   "metadata": {},
   "source": [
    "Bu sayılar: her bir resmin 32x32 boyutunda bir kare olduğunu göstermektedir. Renkli 3 kanal mevcuttur. Red Green Blue!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1043b8-cdee-4135-8190-aaaf6d9eac9f",
   "metadata": {},
   "source": [
    "train verileri içerisinde ilk 3 fotoğraf ne diye bakmak istiyorsak:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a106b69-42ec-42dc-bb51-096c621be9b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6],\n",
       "       [9],\n",
       "       [9]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937d9a72-56dd-4433-a75b-0481e51859d1",
   "metadata": {},
   "source": [
    "bu sayılar veri setinde 6 numarasına uygun hangi resim ise ilk resim onu gösteriyor. sonraki iki resimde 9. sıradaki resimmiş demektir bu. array kelimesinden sonra iki tane kare parantez olduğunu görebiliyoruz, bu ikiboyutlu bir array demek."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a579273-e7b6-49f9-9ef0-e40a8643b4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3159d077-6128-4dab-849a-6138cda87204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 8, 8, ..., 5, 1, 7], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d33b69a-355a-4abb-bb6b-ef1e93d9e489",
   "metadata": {},
   "source": [
    "#göründüğü gibi tek paranteze düştü bu bir boyutlu bir array demektir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "baa5b7bc-e475-43ec-90dc-825413432d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "resim_sınıflari = [\"airplane\", \"automobile\",\"bird\",\"cat\",\"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e9fdb1c-3f70-4209-9a58-7e9b2dc34bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sample(X, y, index):\n",
    "    plt.figure(figsize = (20,2))\n",
    "    plt.imshow(X[index])\n",
    "    plt.xlabel(resim_sınıflari[y[index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d23b22a-2310-4dd3-97a8-c2e9d49ccce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_test, y_test, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1752b1be-3225-4c12-87e8-180104ef4988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_test, y_test, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9f5aa9-bf4b-4a40-9376-d77be8bd933f",
   "metadata": {},
   "source": [
    "NORMALİZASYON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e2b31e2-19c1-4d29-83bf-015bf85c45b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9409603-2b8f-4db9-a7c9-f06e84ec0785",
   "metadata": {},
   "source": [
    "Verileri normalize etmek gerek yoksa CNN algoritması yanlış sonuç verebilir. 3 reng kanali RGB 0-255 pixel arasındadır. Basitce pixel değerini 255e bölelim 0-1 arasına çekmek için"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a9982fd-129e-4957-9f18-98894ad6fdc7",
   "metadata": {},
   "source": [
    "CNN ALGORİTMASI TASARIMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f074489-c26f-4a6f-9c2f-8d61bc34dc5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_learning_model = tf.keras.Sequential([\n",
    "    #ilk bölümde evrişim katmanı burada fotoğraflardan tanımlama yapabilmek için özellikleri çıkarıyoruz.\n",
    "    tf.keras.layers.Conv2D(filters = 32, \n",
    "                           kernel_size = (3,3),\n",
    "                           activation = 'relu',\n",
    "                           input_shape = (32,32,3)),\n",
    "    \n",
    "    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), \n",
    "    \n",
    "    #overfittingi engelliyor ve hızlı çalışıyor.\n",
    "    \n",
    "    tf.keras.layers.Conv2D(filters = 64, \n",
    "                           kernel_size = (3,3), \n",
    "                           activation = 'relu'),\n",
    "    tf.keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
    "    \n",
    "     #ikinci bölümde Articial network katmanları kuruyoruz.\n",
    "    tf.keras.layers.Flatten(), #cnn ve ann arasındaki bağlantıyı yapıyor.\n",
    "    tf.keras.layers.Dense(64, \n",
    "                          activation = 'relu'), #hiddenlayer 64 nerön\n",
    "    tf.keras.layers.Dense(10, \n",
    "                          activation = 'softmax') #outputlayer 10 nerön çünkü 10 sınıf olduğundan 10 output oluyor\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbcddb03-219a-4966-840e-90beb055ec8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_learning_model.compile(optimizer = 'adam',\n",
    "                            loss = 'SparseCategoricalCrossentropy',\n",
    "                            metrics = ['accuracy'])\n",
    "                            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a9ed9e-3eaf-4316-a381-a5ab8e85be8f",
   "metadata": {},
   "source": [
    "Modeli eğitmeye başlayalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a233ab51-4813-4705-ab07-b6bb72fca86b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1563/1563 [==============================] - 15s 10ms/step - loss: 1.4753 - accuracy: 0.4681\n",
      "Epoch 2/5\n",
      "1563/1563 [==============================] - 15s 10ms/step - loss: 1.1101 - accuracy: 0.6112\n",
      "Epoch 3/5\n",
      "1563/1563 [==============================] - 15s 10ms/step - loss: 0.9768 - accuracy: 0.6604\n",
      "Epoch 4/5\n",
      "1563/1563 [==============================] - 15s 10ms/step - loss: 0.8899 - accuracy: 0.6913\n",
      "Epoch 5/5\n",
      "1563/1563 [==============================] - 15s 10ms/step - loss: 0.8307 - accuracy: 0.7135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f9995d5d60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_learning_model.fit(X_train, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b2add7e0-ff15-4db7-9149-c0e475cc70e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.9446 - accuracy: 0.6807\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9445817470550537, 0.6807000041007996]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_learning_model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14cb3831-febe-4468-a1a2-5c735450bd64",
   "metadata": {},
   "source": [
    "67% test verisiyle doğruluk, 70% ile train verisiyle doğruluk aldı."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d31a396-2562-4de8-9f8b-052d8f68baf7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f86228fa-eef7-44bf-b63c-add94cc25df2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.18"
  }
 },
 "nbformat": 4,
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}