diff --git "a/gtsrb-by-cnn.ipynb" "b/gtsrb-by-cnn.ipynb"
deleted file mode 100644--- "a/gtsrb-by-cnn.ipynb"
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-08-03T13:03:20.922200Z","iopub.status.busy":"2024-08-03T13:03:20.921575Z","iopub.status.idle":"2024-08-03T13:05:13.950393Z","shell.execute_reply":"2024-08-03T13:05:13.949558Z","shell.execute_reply.started":"2024-08-03T13:03:20.922170Z"},"trusted":true},"outputs":[],"source":["import numpy as np \n","import pandas as pd \n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        #print(os.path.join(dirname, filename))\n","        pass"]},{"cell_type":"markdown","metadata":{},"source":["## Bu projemizde Almanyanın Trafik plakalarına bakarak makinamızı eğitip yüksek bir başarı oranına sahip bir model Oluşturacağuz ,  oluşan bu model ise bir uygulamada kullanılmak üzere test edilecek \"app.py\" dosyası olarak streamlit ile uygulama oluşturacağız ve dışardan gelen verilerin hangi trafik işareti olduğunu anlayacak "]},{"cell_type":"markdown","metadata":{},"source":["![AI Generated Image](https://cdn.pixabay.com/photo/2023/04/18/17/21/ai-generated-7935630_640.jpg)\n"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:13.952385Z","iopub.status.busy":"2024-08-03T13:05:13.951972Z","iopub.status.idle":"2024-08-03T13:05:13.960200Z","shell.execute_reply":"2024-08-03T13:05:13.959349Z","shell.execute_reply.started":"2024-08-03T13:05:13.952359Z"},"trusted":true},"outputs":[{"data":{"text/plain":["['Meta',\n"," 'meta',\n"," 'Meta.csv',\n"," 'Train.csv',\n"," 'Test.csv',\n"," 'Test',\n"," 'test',\n"," 'Train',\n"," 'train']"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["path='/kaggle/input/gtsrb-german-traffic-sign/'\n","os.listdir(path)"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:13.961654Z","iopub.status.busy":"2024-08-03T13:05:13.961306Z","iopub.status.idle":"2024-08-03T13:05:14.085352Z","shell.execute_reply":"2024-08-03T13:05:14.084541Z","shell.execute_reply.started":"2024-08-03T13:05:13.961610Z"},"trusted":true},"outputs":[],"source":["a=pd.read_csv('/kaggle/input/gtsrb-german-traffic-sign/Train.csv')\n","b=pd.read_csv('/kaggle/input/gtsrb-german-traffic-sign/Test.csv')\n"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.087727Z","iopub.status.busy":"2024-08-03T13:05:14.087421Z","iopub.status.idle":"2024-08-03T13:05:14.106781Z","shell.execute_reply":"2024-08-03T13:05:14.105895Z","shell.execute_reply.started":"2024-08-03T13:05:14.087702Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Width</th>\n","      <th>Height</th>\n","      <th>Roi.X1</th>\n","      <th>Roi.Y1</th>\n","      <th>Roi.X2</th>\n","      <th>Roi.Y2</th>\n","      <th>ClassId</th>\n","      <th>Path</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>27</td>\n","      <td>26</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>22</td>\n","      <td>20</td>\n","      <td>20</td>\n","      <td>Train/20/00020_00000_00000.png</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>28</td>\n","      <td>27</td>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>23</td>\n","      <td>22</td>\n","      <td>20</td>\n","      <td>Train/20/00020_00000_00001.png</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>29</td>\n","      <td>26</td>\n","      <td>6</td>\n","      <td>5</td>\n","      <td>24</td>\n","      <td>21</td>\n","      <td>20</td>\n","      <td>Train/20/00020_00000_00002.png</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>28</td>\n","      <td>27</td>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>23</td>\n","      <td>22</td>\n","      <td>20</td>\n","      <td>Train/20/00020_00000_00003.png</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>28</td>\n","      <td>26</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>23</td>\n","      <td>21</td>\n","      <td>20</td>\n","      <td>Train/20/00020_00000_00004.png</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Width  Height  Roi.X1  Roi.Y1  Roi.X2  Roi.Y2  ClassId  \\\n","0     27      26       5       5      22      20       20   \n","1     28      27       5       6      23      22       20   \n","2     29      26       6       5      24      21       20   \n","3     28      27       5       6      23      22       20   \n","4     28      26       5       5      23      21       20   \n","\n","                             Path  \n","0  Train/20/00020_00000_00000.png  \n","1  Train/20/00020_00000_00001.png  \n","2  Train/20/00020_00000_00002.png  \n","3  Train/20/00020_00000_00003.png  \n","4  Train/20/00020_00000_00004.png  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["a.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.107947Z","iopub.status.busy":"2024-08-03T13:05:14.107708Z","iopub.status.idle":"2024-08-03T13:05:14.118707Z","shell.execute_reply":"2024-08-03T13:05:14.117751Z","shell.execute_reply.started":"2024-08-03T13:05:14.107925Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Width</th>\n","      <th>Height</th>\n","      <th>Roi.X1</th>\n","      <th>Roi.Y1</th>\n","      <th>Roi.X2</th>\n","      <th>Roi.Y2</th>\n","      <th>ClassId</th>\n","      <th>Path</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>53</td>\n","      <td>54</td>\n","      <td>6</td>\n","      <td>5</td>\n","      <td>48</td>\n","      <td>49</td>\n","      <td>16</td>\n","      <td>Test/00000.png</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>42</td>\n","      <td>45</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>36</td>\n","      <td>40</td>\n","      <td>1</td>\n","      <td>Test/00001.png</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>48</td>\n","      <td>52</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>43</td>\n","      <td>47</td>\n","      <td>38</td>\n","      <td>Test/00002.png</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>27</td>\n","      <td>29</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>22</td>\n","      <td>24</td>\n","      <td>33</td>\n","      <td>Test/00003.png</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>60</td>\n","      <td>57</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>55</td>\n","      <td>52</td>\n","      <td>11</td>\n","      <td>Test/00004.png</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Width  Height  Roi.X1  Roi.Y1  Roi.X2  Roi.Y2  ClassId            Path\n","0     53      54       6       5      48      49       16  Test/00000.png\n","1     42      45       5       5      36      40        1  Test/00001.png\n","2     48      52       6       6      43      47       38  Test/00002.png\n","3     27      29       5       5      22      24       33  Test/00003.png\n","4     60      57       5       5      55      52       11  Test/00004.png"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["b.head()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.120284Z","iopub.status.busy":"2024-08-03T13:05:14.120009Z","iopub.status.idle":"2024-08-03T13:05:14.126201Z","shell.execute_reply":"2024-08-03T13:05:14.125284Z","shell.execute_reply.started":"2024-08-03T13:05:14.120255Z"},"trusted":true},"outputs":[],"source":["train = '/kaggle/input/gtsrb-german-traffic-sign/train'\n","test = '/kaggle/input/gtsrb-german-traffic-sign/test'"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.127643Z","iopub.status.busy":"2024-08-03T13:05:14.127336Z","iopub.status.idle":"2024-08-03T13:05:14.267901Z","shell.execute_reply":"2024-08-03T13:05:14.267127Z","shell.execute_reply.started":"2024-08-03T13:05:14.127598Z"},"trusted":true},"outputs":[],"source":["import os\n","\n","classes = {\n","    0: 'Speed limit (20km/h)', 1: 'Speed limit (30km/h)', 2: 'Speed limit (50km/h)', 3: 'Speed limit (60km/h)',\n","    4: 'Speed limit (70km/h)', 5: 'Speed limit (80km/h)', 6: 'End of speed limit (80km/h)', 7: 'Speed limit (100km/h)',\n","    8: 'Speed limit (120km/h)', 9: 'No passing', 10: 'No passing veh over 3.5 tons', 11: 'Right-of-way at intersection',\n","    12: 'Priority road', 13: 'Yield', 14: 'Stop', 15: 'No vehicles', 16: 'Veh > 3.5 tons prohibited', 17: 'No entry',\n","    18: 'General caution', 19: 'Dangerous curve left', 20: 'Dangerous curve right', 21: 'Double curve', 22: 'Bumpy road',\n","    23: 'Slippery road', 24: 'Road narrows on the right', 25: 'Road work', 26: 'Traffic signals', 27: 'Pedestrians',\n","    28: 'Children crossing', 29: 'Bicycles crossing', 30: 'Beware of ice/snow', 31: 'Wild animals crossing',\n","    32: 'End speed + passing limits', 33: 'Turn right ahead', 34: 'Turn left ahead', 35: 'Ahead only',\n","    36: 'Go straight or right', 37: 'Go straight or left', 38: 'Keep right', 39: 'Keep left', 40: 'Roundabout mandatory',\n","    41: 'End of no passing', 42: 'End no passing veh > 3.5 tons'\n","}\n","\n","path = '/kaggle/input/gtsrb-german-traffic-sign/train'\n","label_of_file = []\n","img_list = []\n","\n","for kind in classes:\n","    kind_path = os.path.join(path, str(kind))\n","    \n","    for img in os.listdir(kind_path):\n","        img_list.append(os.path.join(kind_path, img))\n","        label_of_file.append(kind)\n"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.269437Z","iopub.status.busy":"2024-08-03T13:05:14.269088Z","iopub.status.idle":"2024-08-03T13:05:14.275590Z","shell.execute_reply":"2024-08-03T13:05:14.274752Z","shell.execute_reply.started":"2024-08-03T13:05:14.269406Z"},"trusted":true},"outputs":[{"data":{"text/plain":["39209"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["len(img_list)"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.277232Z","iopub.status.busy":"2024-08-03T13:05:14.276837Z","iopub.status.idle":"2024-08-03T13:05:14.303557Z","shell.execute_reply":"2024-08-03T13:05:14.302871Z","shell.execute_reply.started":"2024-08-03T13:05:14.277202Z"},"trusted":true},"outputs":[],"source":[" #Verilen Değerleri Dataya Dönüştürme \n","\n","df=pd.DataFrame({'img':img_list , 'label':label_of_file})"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.307492Z","iopub.status.busy":"2024-08-03T13:05:14.307170Z","iopub.status.idle":"2024-08-03T13:05:14.315811Z","shell.execute_reply":"2024-08-03T13:05:14.314772Z","shell.execute_reply.started":"2024-08-03T13:05:14.307470Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>img</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 img  label\n","0  /kaggle/input/gtsrb-german-traffic-sign/train/...      0\n","1  /kaggle/input/gtsrb-german-traffic-sign/train/...      0\n","2  /kaggle/input/gtsrb-german-traffic-sign/train/...      0\n","3  /kaggle/input/gtsrb-german-traffic-sign/train/...      0\n","4  /kaggle/input/gtsrb-german-traffic-sign/train/...      0"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{},"source":["##  Burada  labeller 0 1 2 3 .... gibi gittiği için bizim de her bir rowun yerini değiştirmemiz gerekiyor  yoksa verileri ayrımada yani train , text ayırmasında hata alaırız "]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.317173Z","iopub.status.busy":"2024-08-03T13:05:14.316909Z","iopub.status.idle":"2024-08-03T13:05:14.332508Z","shell.execute_reply":"2024-08-03T13:05:14.331673Z","shell.execute_reply.started":"2024-08-03T13:05:14.317151Z"},"trusted":true},"outputs":[],"source":["df = df.sample(frac=1).reset_index(drop=True)"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.334353Z","iopub.status.busy":"2024-08-03T13:05:14.333794Z","iopub.status.idle":"2024-08-03T13:05:14.343117Z","shell.execute_reply":"2024-08-03T13:05:14.342180Z","shell.execute_reply.started":"2024-08-03T13:05:14.334321Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>img</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 img  label\n","0  /kaggle/input/gtsrb-german-traffic-sign/train/...     12\n","1  /kaggle/input/gtsrb-german-traffic-sign/train/...      9\n","2  /kaggle/input/gtsrb-german-traffic-sign/train/...     17\n","3  /kaggle/input/gtsrb-german-traffic-sign/train/...     12\n","4  /kaggle/input/gtsrb-german-traffic-sign/train/...      9"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df.head()  # Şimdi daha iyi "]},{"cell_type":"markdown","metadata":{},"source":["# Hepsinin Boyutu Aynı MI ? "]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.344458Z","iopub.status.busy":"2024-08-03T13:05:14.344194Z","iopub.status.idle":"2024-08-03T13:05:14.788884Z","shell.execute_reply":"2024-08-03T13:05:14.787517Z","shell.execute_reply.started":"2024-08-03T13:05:14.344436Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Image 1 Shape: (96, 95, 3)\n","Image 2 Shape: (35, 38, 3)\n","Image 3 Shape: (45, 45, 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size 1500x500 with 3 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import cv2\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Resim yollarını liste olarak al\n","\n","neresi = df['img'].tolist()\n","\n","restgele_3 = np.random.choice(len(neresi), 3, replace=False)\n","\n","# Seçilen görselleri oku ve RGB formatına çevir\n","images = [cv2.cvtColor(cv2.imread(     neresi[i]), cv2.COLOR_BGR2RGB) for i in restgele_3]\n","\n","# Her bir resmin boyutunu göster\n","for i, img in enumerate(images):\n","    print(f\"Image {i+1} Shape:\", img.shape)\n","\n","# Görselleri göster\n","fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n","for i, img in enumerate(images):\n","    axs[i].imshow(img)\n","    axs[i].axis('off')\n","plt.show()\n"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:14.790997Z","iopub.status.busy":"2024-08-03T13:05:14.790482Z","iopub.status.idle":"2024-08-03T13:05:15.884027Z","shell.execute_reply":"2024-08-03T13:05:15.883086Z","shell.execute_reply.started":"2024-08-03T13:05:14.790952Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 600x600 with 1 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","text/plain":["<Figure 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","text/plain":["<Figure 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","text/plain":["<Figure 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","text/plain":["<Figure size 600x600 with 1 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","text/plain":["<Figure size 600x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import cv2\n","import matplotlib.pyplot as plt\n","for index, row in df.sample(7).iterrows():\n","    # Resmi yükle\n","    img = cv2.imread(row['img'])\n","    \n","    # OpenCV resmi RGB formatına dönüştür\n","    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n","    \n","    # Resmi matplotlib ile göster\n","    plt.figure(figsize=(6, 6))\n","    plt.imshow(img_rgb)\n","    plt.title(row['label'])\n","    plt.axis('off')\n","    plt.show()"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:15.886163Z","iopub.status.busy":"2024-08-03T13:05:15.885301Z","iopub.status.idle":"2024-08-03T13:05:15.910833Z","shell.execute_reply":"2024-08-03T13:05:15.909910Z","shell.execute_reply.started":"2024-08-03T13:05:15.886126Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 39209 entries, 0 to 39208\n","Data columns (total 2 columns):\n"," #   Column  Non-Null Count  Dtype \n","---  ------  --------------  ----- \n"," 0   img     39209 non-null  object\n"," 1   label   39209 non-null  int64 \n","dtypes: int64(1), object(1)\n","memory usage: 612.8+ KB\n"]}],"source":["df.info()  # data hakkında bilgi "]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:15.912551Z","iopub.status.busy":"2024-08-03T13:05:15.912130Z","iopub.status.idle":"2024-08-03T13:05:15.920175Z","shell.execute_reply":"2024-08-03T13:05:15.919209Z","shell.execute_reply.started":"2024-08-03T13:05:15.912518Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array([12,  9, 17, 15, 38,  2, 14, 25, 10, 32, 42, 26,  1, 39, 34,  5,  3,\n","        4, 36,  8, 13, 11, 41,  7, 31, 33, 16, 22, 18, 23, 28, 27, 24, 40,\n","       35, 29,  6,  0, 20, 30, 21, 19, 37])"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["df.label.unique()"]},{"cell_type":"markdown","metadata":{},"source":["# İşaretlerin  Dağılımına bakalım\n"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:15.921876Z","iopub.status.busy":"2024-08-03T13:05:15.921488Z","iopub.status.idle":"2024-08-03T13:05:18.120119Z","shell.execute_reply":"2024-08-03T13:05:18.119170Z","shell.execute_reply.started":"2024-08-03T13:05:15.921844Z"},"trusted":true},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n","        if (typeof require !== 'undefined') {\n","        require.undef(\"plotly\");\n","        requirejs.config({\n","            paths: {\n","                'plotly': 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title='Hurma Çeşitleri Grafiği', #başlık \n","           template='plotly_dark') # arkapılan \n","\n","fig.show()"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:05:18.122226Z","iopub.status.busy":"2024-08-03T13:05:18.121824Z","iopub.status.idle":"2024-08-03T13:05:18.216720Z","shell.execute_reply":"2024-08-03T13:05:18.215828Z","shell.execute_reply.started":"2024-08-03T13:05:18.122193Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>                            <div id=\"0a22e973-f744-4a06-9412-77b2b95931e7\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"0a22e973-f744-4a06-9412-77b2b95931e7\")) {                    Plotly.newPlot(                        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.dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>img</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>15542</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>15654</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>24486</th>\n","      <td>/kaggle/input/gtsrb-german-traffic-sign/train/...</td>\n","      <td>18</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                     img  label\n","15542  /kaggle/input/gtsrb-german-traffic-sign/train/...     20\n","15654  /kaggle/input/gtsrb-german-traffic-sign/train/...      1\n","24486  /kaggle/input/gtsrb-german-traffic-sign/train/...     18"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["df.sample(3)"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:06:04.634915Z","iopub.status.busy":"2024-08-03T13:06:04.633227Z","iopub.status.idle":"2024-08-03T13:06:04.640590Z","shell.execute_reply":"2024-08-03T13:06:04.639698Z","shell.execute_reply.started":"2024-08-03T13:06:04.634879Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(39209, 2)"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"markdown","metadata":{},"source":["#  Normalizasyon "]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:06:06.706268Z","iopub.status.busy":"2024-08-03T13:06:06.705557Z","iopub.status.idle":"2024-08-03T13:11:16.322260Z","shell.execute_reply":"2024-08-03T13:11:16.321178Z","shell.execute_reply.started":"2024-08-03T13:06:06.706236Z"},"trusted":true},"outputs":[],"source":["import numpy as np\n","x=[]\n","for img in df['img']:\n","    try:\n","        img = cv2.imread(img)\n","        img = cv2.resize(img, (32, 32))# 32x32 Hepsi için uygun olur \n","        img = img / 255.0  # normalize etmee \n","        x.append(img)      # img \n","\n","    except:\n","        print(f\"Error loading image: {img}\")"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:12:12.074702Z","iopub.status.busy":"2024-08-03T13:12:12.073942Z","iopub.status.idle":"2024-08-03T13:12:12.078610Z","shell.execute_reply":"2024-08-03T13:12:12.077675Z","shell.execute_reply.started":"2024-08-03T13:12:12.074668Z"},"trusted":true},"outputs":[],"source":["#x  #[array([[[0.2745098 , 0.23921569, 0.23529412],... "]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:12:46.849950Z","iopub.status.busy":"2024-08-03T13:12:46.849516Z","iopub.status.idle":"2024-08-03T13:12:46.867358Z","shell.execute_reply":"2024-08-03T13:12:46.866181Z","shell.execute_reply.started":"2024-08-03T13:12:46.849921Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 39209 entries, 0 to 39208\n","Data columns (total 2 columns):\n"," #   Column  Non-Null Count  Dtype \n","---  ------  --------------  ----- \n"," 0   img     39209 non-null  object\n"," 1   label   39209 non-null  int64 \n","dtypes: int64(1), object(1)\n","memory usage: 612.8+ KB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","metadata":{},"source":["# Model Oluşturma"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:13:56.797471Z","iopub.status.busy":"2024-08-03T13:13:56.796851Z","iopub.status.idle":"2024-08-03T13:13:57.149107Z","shell.execute_reply":"2024-08-03T13:13:57.148293Z","shell.execute_reply.started":"2024-08-03T13:13:56.797439Z"},"trusted":true},"outputs":[],"source":["x=np.array(x)\n","y=df['label']"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:14:33.194151Z","iopub.status.busy":"2024-08-03T13:14:33.193230Z","iopub.status.idle":"2024-08-03T13:14:34.276331Z","shell.execute_reply":"2024-08-03T13:14:34.275497Z","shell.execute_reply.started":"2024-08-03T13:14:33.194116Z"},"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)"]},{"cell_type":"markdown","metadata":{},"source":["# CNN - Convolutional Netural Networks\n","#### 0 dan 42 ye toplam 43 çeşidimiz var "]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:31:50.379390Z","iopub.status.busy":"2024-08-03T13:31:50.378759Z","iopub.status.idle":"2024-08-03T13:32:37.107951Z","shell.execute_reply":"2024-08-03T13:32:37.107092Z","shell.execute_reply.started":"2024-08-03T13:31:50.379355Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 125ms/step - accuracy: 0.0561 - loss: 3.5936 - val_accuracy: 0.0994 - val_loss: 3.2712\n","Epoch 2/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.1104 - loss: 3.2212 - val_accuracy: 0.1686 - val_loss: 2.9633\n","Epoch 3/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.1585 - loss: 2.9766 - val_accuracy: 0.2061 - val_loss: 2.7948\n","Epoch 4/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.2170 - loss: 2.7529 - val_accuracy: 0.2895 - val_loss: 2.4530\n","Epoch 5/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.2748 - loss: 2.4639 - val_accuracy: 0.3565 - val_loss: 2.1572\n","Epoch 6/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.3317 - loss: 2.1792 - val_accuracy: 0.4014 - val_loss: 1.9197\n","Epoch 7/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.3810 - loss: 1.9700 - val_accuracy: 0.4344 - val_loss: 1.7658\n","Epoch 8/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4209 - loss: 1.8178 - val_accuracy: 0.4951 - val_loss: 1.5963\n","Epoch 9/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4590 - loss: 1.6779 - val_accuracy: 0.5483 - val_loss: 1.4459\n","Epoch 10/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4892 - loss: 1.5630 - val_accuracy: 0.5858 - val_loss: 1.3148\n","Epoch 11/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.5207 - loss: 1.4609 - val_accuracy: 0.6029 - val_loss: 1.2663\n","Epoch 12/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.5467 - loss: 1.3804 - val_accuracy: 0.6482 - val_loss: 1.1468\n","Epoch 13/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.5639 - loss: 1.3219 - val_accuracy: 0.6555 - val_loss: 1.0863\n","Epoch 14/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.5794 - loss: 1.2540 - val_accuracy: 0.6861 - val_loss: 0.9870\n","Epoch 15/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6025 - loss: 1.1863 - val_accuracy: 0.7065 - val_loss: 0.9460\n","Epoch 16/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6218 - loss: 1.1247 - val_accuracy: 0.7271 - val_loss: 0.8871\n","Epoch 17/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6476 - loss: 1.0575 - val_accuracy: 0.7585 - val_loss: 0.8264\n","Epoch 18/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6662 - loss: 1.0058 - val_accuracy: 0.7695 - val_loss: 0.7712\n","Epoch 19/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6861 - loss: 0.9481 - val_accuracy: 0.7865 - val_loss: 0.7100\n","Epoch 20/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6965 - loss: 0.9012 - val_accuracy: 0.8158 - val_loss: 0.6506\n","Epoch 21/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.7152 - loss: 0.8575 - val_accuracy: 0.8240 - val_loss: 0.5975\n","Epoch 22/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7294 - loss: 0.8050 - val_accuracy: 0.8463 - val_loss: 0.5343\n","Epoch 23/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7480 - loss: 0.7707 - val_accuracy: 0.8539 - val_loss: 0.4895\n","Epoch 24/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7650 - loss: 0.7127 - val_accuracy: 0.8531 - val_loss: 0.4774\n","Epoch 25/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7708 - loss: 0.6863 - val_accuracy: 0.8788 - val_loss: 0.4214\n","Epoch 26/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7906 - loss: 0.6364 - val_accuracy: 0.8945 - val_loss: 0.3810\n","Epoch 27/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7936 - loss: 0.6154 - val_accuracy: 0.8990 - val_loss: 0.3532\n","Epoch 28/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8095 - loss: 0.5821 - val_accuracy: 0.9136 - val_loss: 0.3266\n","Epoch 29/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8192 - loss: 0.5458 - val_accuracy: 0.9118 - val_loss: 0.3126\n","Epoch 30/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8296 - loss: 0.5162 - val_accuracy: 0.9210 - val_loss: 0.2882\n","Epoch 31/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8412 - loss: 0.4884 - val_accuracy: 0.9339 - val_loss: 0.2483\n","Epoch 32/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8433 - loss: 0.4740 - val_accuracy: 0.9382 - val_loss: 0.2373\n","Epoch 33/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8564 - loss: 0.4395 - val_accuracy: 0.9443 - val_loss: 0.2154\n","Epoch 34/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8619 - loss: 0.4221 - val_accuracy: 0.9408 - val_loss: 0.2171\n","Epoch 35/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8622 - loss: 0.4127 - val_accuracy: 0.9537 - val_loss: 0.1862\n","Epoch 36/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8736 - loss: 0.3867 - val_accuracy: 0.9501 - val_loss: 0.1922\n","Epoch 37/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8725 - loss: 0.3843 - val_accuracy: 0.9587 - val_loss: 0.1678\n","Epoch 38/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8804 - loss: 0.3619 - val_accuracy: 0.9563 - val_loss: 0.1715\n","Epoch 39/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8877 - loss: 0.3418 - val_accuracy: 0.9638 - val_loss: 0.1496\n","Epoch 40/40\n","\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8934 - loss: 0.3259 - val_accuracy: 0.9623 - val_loss: 0.1532\n"]}],"source":["import tensorflow as tf\n","from tensorflow.keras import layers, models\n","\n","# Model oluşturma\n","model = models.Sequential()\n","\n","# Evrişimsel katmanlar(Conv) ve havuzlama(pooling) katmanları\n","model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3), padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","model.add(layers.Dropout(0.25))\n","\n","model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","model.add(layers.Dropout(0.25))\n","\n","model.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","model.add(layers.Dropout(0.25))\n","\n","# Global pooling katmanı ekleme\n","model.add(layers.GlobalAveragePooling2D())\n","\n","# Düzleştirme ve tam bağlı katmanlar\n","model.add(layers.Dense(512, activation='relu'))\n","model.add(layers.Dropout(0.5))\n","model.add(layers.Dense(43, activation='softmax'))\n","\n","# Modeli derleme\n","model.compile(optimizer='adam',\n","              loss='sparse_categorical_crossentropy',\n","              metrics=['accuracy'])\n","\n","# Modeli eğitme\n","history = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=40, batch_size=500)\n"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:21:55.866843Z","iopub.status.busy":"2024-08-03T13:21:55.866481Z","iopub.status.idle":"2024-08-03T13:21:55.898003Z","shell.execute_reply":"2024-08-03T13:21:55.897114Z","shell.execute_reply.started":"2024-08-03T13:21:55.866818Z"},"trusted":true},"outputs":[{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"sequential_6\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 ��� (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,656</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">22,059</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_24 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_26 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_25 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_27 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼───���────────────────────┼───────────────┤\n","│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_26 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_28 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m147,584\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_27 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_29 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m262,656\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_30 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m43\u001b[0m)             │        \u001b[38;5;34m22,059\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">525,547</span> (2.00 MB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m525,547\u001b[0m (2.00 MB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">525,547</span> (2.00 MB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m525,547\u001b[0m (2.00 MB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"]},"metadata":{},"output_type":"display_data"}],"source":["model.summary()"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:30:13.910378Z","iopub.status.busy":"2024-08-03T13:30:13.909676Z","iopub.status.idle":"2024-08-03T13:30:14.375408Z","shell.execute_reply":"2024-08-03T13:30:14.374521Z","shell.execute_reply.started":"2024-08-03T13:30:13.910343Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Eğitim ve Doğrulama Kaybı\n","plt.plot(history.history['loss'], label='Eğitim Kaybı')\n","plt.plot(history.history['val_loss'], label='Doğrulama Kaybı')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()\n","\n","# Eğitim ve Doğrulama Doğruluğu\n","plt.plot(history.history['accuracy'], label='Eğitim Doğruluğu')\n","plt.plot(history.history['val_accuracy'], label='Doğrulama Doğruluğu')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":55,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:34:24.013158Z","iopub.status.busy":"2024-08-03T13:34:24.012785Z","iopub.status.idle":"2024-08-03T13:34:28.813326Z","shell.execute_reply":"2024-08-03T13:34:28.812336Z","shell.execute_reply.started":"2024-08-03T13:34:24.013130Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m307/307\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 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size 3000x3000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n","\n","# Model ile tahmin yapma\n","y_pred = model.predict(x_test)\n","y_pred_classes = np.argmax(y_pred, axis=1)  # Tahmin edilen sınıflar\n","\n","# Gerçek etiketleri alma\n","\n","# Confusion Matrix hesaplama\n","cm = confusion_matrix(y_test, y_pred_classes)\n","\n","# Confusion Matrix'i görselleştirme\n","fig, ax = plt.subplots(figsize=(30, 30))\n","plt.title('Confusion Matrix')\n","disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=np.arange(cm.shape[0]))  # Etiketler olarak sınıf numaralarını kullan\n","disp.plot(cmap=plt.cm.Blues, ax=ax)\n","plt.show()"]},{"cell_type":"code","execution_count":62,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:39:46.596747Z","iopub.status.busy":"2024-08-03T13:39:46.596361Z","iopub.status.idle":"2024-08-03T13:39:47.538176Z","shell.execute_reply":"2024-08-03T13:39:47.537235Z","shell.execute_reply.started":"2024-08-03T13:39:46.596716Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m307/307\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n","Doğru Tahmin Oranı: 0.9614403753952872% \n"]}],"source":["import numpy as np\n","from sklearn.metrics import accuracy_score\n","\n","tahmin = model.predict(x_test)\n","tahmin_class = np.argmax(y_pred, axis=1)  # Tahmin edilen sınıflar\n","\n","y_test_classes = np.array(y_test)  # Gerçek etiketler, direkt olarak kullan\n","\n","\n","ac = accuracy_score(y_test_classes, tahmin_class)\n","\n","\n","print(f\"Doğru Tahmin Oranı: {ac}% \")  #  yüzde accuracy\n"]},{"cell_type":"code","execution_count":64,"metadata":{"execution":{"iopub.execute_input":"2024-08-03T13:40:37.618168Z","iopub.status.busy":"2024-08-03T13:40:37.617223Z","iopub.status.idle":"2024-08-03T13:40:37.672395Z","shell.execute_reply":"2024-08-03T13:40:37.671607Z","shell.execute_reply.started":"2024-08-03T13:40:37.618130Z"},"trusted":true},"outputs":[],"source":["model.save('GTSRB_model_GermanTrafic.h5')"]},{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2024-08-03T15:28:43.558827Z","iopub.status.busy":"2024-08-03T15:28:43.557980Z","iopub.status.idle":"2024-08-03T15:28:43.564355Z","shell.execute_reply":"2024-08-03T15:28:43.563118Z","shell.execute_reply.started":"2024-08-03T15:28:43.558794Z"}},"source":["modelimi%96 basari aliyor simdi bunu streamlit ile app yapalim"]},{"cell_type":"markdown","metadata":{},"source":["# Burada bittirdki "]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":82373,"sourceId":191501,"sourceType":"datasetVersion"}],"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"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":"3.10.13"}},"nbformat":4,"nbformat_minor":4}