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best_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:04ea03a925b5705497c7a6dcef747055eaf14b3cc786810836150b79a567f6c0
+size 81816136

bettermodel.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 579 images belonging to 5 classes.\n",
+      "Found 124 images belonging to 5 classes.\n",
+      "Found 235 images belonging to 5 classes.\n",
+      "Epoch 1/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 423ms/step - accuracy: 0.2483 - loss: 2.7352 - val_accuracy: 0.2339 - val_loss: 2.1511\n",
+      "Epoch 2/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 370ms/step - accuracy: 0.2506 - loss: 2.0916 - val_accuracy: 0.2339 - val_loss: 1.9442\n",
+      "Epoch 3/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 397ms/step - accuracy: 0.2298 - loss: 1.9145 - val_accuracy: 0.2339 - val_loss: 1.8353\n",
+      "Epoch 4/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 374ms/step - accuracy: 0.2367 - loss: 1.8245 - val_accuracy: 0.2339 - val_loss: 1.7828\n",
+      "Epoch 5/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 373ms/step - accuracy: 0.2411 - loss: 1.7718 - val_accuracy: 0.2339 - val_loss: 1.7444\n",
+      "Epoch 6/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 369ms/step - accuracy: 0.1975 - loss: 1.7408 - val_accuracy: 0.2339 - val_loss: 1.7163\n",
+      "Epoch 7/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 387ms/step - accuracy: 0.2479 - loss: 1.7097 - val_accuracy: 0.2339 - val_loss: 1.6960\n",
+      "Epoch 8/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 360ms/step - accuracy: 0.2480 - loss: 1.6955 - val_accuracy: 0.2339 - val_loss: 1.6844\n",
+      "Epoch 9/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 381ms/step - accuracy: 0.2417 - loss: 1.6826 - val_accuracy: 0.2339 - val_loss: 1.6750\n",
+      "Epoch 10/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 415ms/step - accuracy: 0.2540 - loss: 1.6696 - val_accuracy: 0.2339 - val_loss: 1.6658\n",
+      "Epoch 11/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 377ms/step - accuracy: 0.2436 - loss: 1.6617 - val_accuracy: 0.2339 - val_loss: 1.6583\n",
+      "Epoch 12/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 367ms/step - accuracy: 0.2333 - loss: 1.6566 - val_accuracy: 0.2339 - val_loss: 1.6490\n",
+      "Epoch 13/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 363ms/step - accuracy: 0.2229 - loss: 1.6558 - val_accuracy: 0.2339 - val_loss: 1.6456\n",
+      "Epoch 14/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 359ms/step - accuracy: 0.2277 - loss: 1.6478 - val_accuracy: 0.2339 - val_loss: 1.6423\n",
+      "Epoch 15/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 354ms/step - accuracy: 0.2638 - loss: 1.6418 - val_accuracy: 0.2339 - val_loss: 1.6430\n",
+      "Epoch 16/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 374ms/step - accuracy: 0.2305 - loss: 1.6422 - val_accuracy: 0.2339 - val_loss: 1.6400\n",
+      "Epoch 17/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 382ms/step - accuracy: 0.2539 - loss: 1.6356 - val_accuracy: 0.2339 - val_loss: 1.6367\n",
+      "Epoch 18/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 391ms/step - accuracy: 0.2547 - loss: 1.6336 - val_accuracy: 0.2339 - val_loss: 1.6331\n",
+      "Epoch 19/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 366ms/step - accuracy: 0.2326 - loss: 1.6322 - val_accuracy: 0.2339 - val_loss: 1.6280\n",
+      "Epoch 20/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 365ms/step - accuracy: 0.2219 - loss: 1.6340 - val_accuracy: 0.2339 - val_loss: 1.6312\n",
+      "Epoch 21/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 351ms/step - accuracy: 0.2411 - loss: 1.6299 - val_accuracy: 0.2339 - val_loss: 1.6290\n",
+      "Epoch 22/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 356ms/step - accuracy: 0.2371 - loss: 1.6308 - val_accuracy: 0.2339 - val_loss: 1.6272\n",
+      "Epoch 23/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 359ms/step - accuracy: 0.2460 - loss: 1.6243 - val_accuracy: 0.2339 - val_loss: 1.6255\n",
+      "Epoch 24/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 365ms/step - accuracy: 0.2181 - loss: 1.6281 - val_accuracy: 0.2339 - val_loss: 1.6240\n",
+      "Epoch 25/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 364ms/step - accuracy: 0.2336 - loss: 1.6254 - val_accuracy: 0.2339 - val_loss: 1.6226\n",
+      "Epoch 26/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 360ms/step - accuracy: 0.2102 - loss: 1.6278 - val_accuracy: 0.2339 - val_loss: 1.6217\n",
+      "Epoch 27/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 359ms/step - accuracy: 0.2553 - loss: 1.6174 - val_accuracy: 0.2339 - val_loss: 1.6204\n",
+      "Epoch 28/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 366ms/step - accuracy: 0.2452 - loss: 1.6189 - val_accuracy: 0.2339 - val_loss: 1.6191\n",
+      "Epoch 29/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 360ms/step - accuracy: 0.2409 - loss: 1.6163 - val_accuracy: 0.2339 - val_loss: 1.6163\n",
+      "Epoch 30/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 388ms/step - accuracy: 0.2268 - loss: 1.6160 - val_accuracy: 0.2339 - val_loss: 1.6186\n",
+      "Epoch 31/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 393ms/step - accuracy: 0.2233 - loss: 1.6230 - val_accuracy: 0.2339 - val_loss: 1.6179\n",
+      "Epoch 32/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 359ms/step - accuracy: 0.2562 - loss: 1.6140 - val_accuracy: 0.2339 - val_loss: 1.6170\n",
+      "Epoch 33/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 360ms/step - accuracy: 0.2387 - loss: 1.6158 - val_accuracy: 0.2339 - val_loss: 1.6163\n",
+      "Epoch 34/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 352ms/step - accuracy: 0.2353 - loss: 1.6159 - val_accuracy: 0.2339 - val_loss: 1.6155\n",
+      "Epoch 35/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 367ms/step - accuracy: 0.2491 - loss: 1.6107 - val_accuracy: 0.2339 - val_loss: 1.6145\n",
+      "Epoch 36/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 347ms/step - accuracy: 0.2277 - loss: 1.6167 - val_accuracy: 0.2339 - val_loss: 1.6142\n",
+      "Epoch 37/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 361ms/step - accuracy: 0.2633 - loss: 1.6109 - val_accuracy: 0.2339 - val_loss: 1.6143\n",
+      "Epoch 38/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 355ms/step - accuracy: 0.2541 - loss: 1.6108 - val_accuracy: 0.2339 - val_loss: 1.6136\n",
+      "Epoch 39/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 349ms/step - accuracy: 0.2679 - loss: 1.6037 - val_accuracy: 0.2339 - val_loss: 1.6120\n",
+      "Epoch 40/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 362ms/step - accuracy: 0.2324 - loss: 1.6133 - val_accuracy: 0.2339 - val_loss: 1.6077\n",
+      "Epoch 41/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 357ms/step - accuracy: 0.2440 - loss: 1.6085 - val_accuracy: 0.2339 - val_loss: 1.6138\n",
+      "Epoch 42/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 380ms/step - accuracy: 0.2423 - loss: 1.6108 - val_accuracy: 0.2339 - val_loss: 1.6136\n",
+      "Epoch 43/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 354ms/step - accuracy: 0.2276 - loss: 1.6149 - val_accuracy: 0.2339 - val_loss: 1.6136\n",
+      "Epoch 44/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 357ms/step - accuracy: 0.2335 - loss: 1.6132 - val_accuracy: 0.2339 - val_loss: 1.6128\n",
+      "Epoch 45/50\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 356ms/step - accuracy: 0.2477 - loss: 1.6109 - val_accuracy: 0.2339 - val_loss: 1.6119\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 89ms/step - accuracy: 0.1173 - loss: 1.6083\n",
+      "Test loss: 1.607354760169983\n",
+      "Test accuracy: 0.12340425699949265\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
+    "from tensorflow.keras.regularizers import l2\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "\n",
+    "# Definiere die Größe der Bilder und den Pfad zum Datensatz\n",
+    "img_size = (150, 150)\n",
+    "data_dir = r'C:\\kiamep'\n",
+    "\n",
+    "# Erstelle Datengeneratoren für Training, Validierung und Test mit Datenvergrößerung\n",
+    "train_datagen = ImageDataGenerator(\n",
+    "    rescale=1./255,\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='nearest',\n",
+    "    vertical_flip=True,  # Vertikale Spiegelung hinzugefügt\n",
+    "    brightness_range=[0.8, 1.2],  # Helligkeitsanpassung hinzugefügt\n",
+    "    \n",
+    "\n",
+    ")\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Trainingsdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Validierungsdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "test_generator = test_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Testdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "# Definiere das verbesserte CNN-Modell\n",
+    "# Regularisierung gegen overfiting (Gwicht)\n",
+    "# Dropout auch gegen overfiting (Neuronen Drop)\n",
+    "# Mehr Neuronen 512\n",
+    "model = Sequential([\n",
+    "    Conv2D(32, (3, 3), activation='relu', kernel_regularizer=l2(0.001), input_shape=(img_size[0], img_size[1], 3)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Conv2D(64, (3, 3), activation='relu', kernel_regularizer=l2(0.001)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Conv2D(128, (3, 3), activation='relu', kernel_regularizer=l2(0.001)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Conv2D(256, (3, 3), activation='relu', kernel_regularizer=l2(0.001)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Flatten(),\n",
+    "    Dense(512, activation='relu', kernel_regularizer=l2(0.001)),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(5, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "# Kompiliere das Modell\n",
+    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Definiere Callbacks\n",
+    "# Auch gegen Overfiting early stop \n",
+    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
+    "model_checkpoint = ModelCheckpoint('best_model.keras', monitor='val_accuracy', save_best_only=True)\n",
+    "\n",
+    "# Trainiere das Modell\n",
+    "# mehr epochen\n",
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    epochs=50,\n",
+    "    validation_data=validation_generator,\n",
+    "    callbacks=[early_stopping, model_checkpoint]\n",
+    ")\n",
+    "\n",
+    "# Evaluiere das Modell auf den Testdaten\n",
+    "test_loss, test_accuracy = model.evaluate(test_generator)\n",
+    "print(\"Test loss:\", test_loss)\n",
+    "print(\"Test accuracy:\", test_accuracy)\n",
+    "\n",
+    "# Speichere das beste Modell im Keras-Format\n",
+    "model.save('best_model.keras')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kia",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

dog_breed_classification_training.ipynb

@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 579 images belonging to 5 classes.\n",
+      "Found 124 images belonging to 5 classes.\n",
+      "Found 235 images belonging to 5 classes.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\papic\\anaconda3\\envs\\kia\\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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\papic\\anaconda3\\envs\\kia\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 319ms/step - accuracy: 0.2299 - loss: 2.0734 - val_accuracy: 0.2823 - val_loss: 1.6074\n",
+      "Epoch 2/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 309ms/step - accuracy: 0.2566 - loss: 1.6081 - val_accuracy: 0.1774 - val_loss: 1.6082\n",
+      "Epoch 3/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 339ms/step - accuracy: 0.1996 - loss: 1.6083 - val_accuracy: 0.2903 - val_loss: 1.5758\n",
+      "Epoch 4/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 309ms/step - accuracy: 0.2638 - loss: 1.5865 - val_accuracy: 0.2984 - val_loss: 1.5241\n",
+      "Epoch 5/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 299ms/step - accuracy: 0.2650 - loss: 1.5385 - val_accuracy: 0.4435 - val_loss: 1.4655\n",
+      "Epoch 6/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 302ms/step - accuracy: 0.3636 - loss: 1.4539 - val_accuracy: 0.4274 - val_loss: 1.4295\n",
+      "Epoch 7/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 286ms/step - accuracy: 0.3446 - loss: 1.4875 - val_accuracy: 0.4274 - val_loss: 1.3986\n",
+      "Epoch 8/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 284ms/step - accuracy: 0.3989 - loss: 1.4304 - val_accuracy: 0.3710 - val_loss: 1.4821\n",
+      "Epoch 9/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 280ms/step - accuracy: 0.3640 - loss: 1.4761 - val_accuracy: 0.4032 - val_loss: 1.4074\n",
+      "Epoch 10/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 282ms/step - accuracy: 0.3923 - loss: 1.4042 - val_accuracy: 0.3710 - val_loss: 1.4259\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 93ms/step - accuracy: 0.2815 - loss: 1.5283 \n",
+      "Test loss: 1.5475623607635498\n",
+      "Test accuracy: 0.2851063907146454\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+    "\n",
+    "# Definiere die Größe der Bilder und den Pfad zum Datensatz\n",
+    "img_size = (150, 150)\n",
+    "data_dir = r'C:\\kiamep'\n",
+    "\n",
+    "# Erstelle Datengeneratoren für Training mit Datenvergrößerung\n",
+    "train_datagen_augmented = ImageDataGenerator(\n",
+    "    rescale=1./255,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    \n",
+    ")\n",
+    "\n",
+    "# Erstelle Datengeneratoren für Validierung und Test ohne Datenvergrößerung\n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "train_generator_augmented = train_datagen_augmented.flow_from_directory(\n",
+    "    data_dir + '\\\\Trainingsdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    ")\n",
+    "\n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Validierungsdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    ")\n",
+    "\n",
+    "test_generator = test_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Testdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    ")\n",
+    "\n",
+    "# Definiere das CNN-Modell\n",
+    "model = Sequential([\n",
+    "    Conv2D(32, (3, 3), activation='relu', input_shape=(img_size[0], img_size[1], 3)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Conv2D(64, (3, 3), activation='relu'),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Conv2D(128, (3, 3), activation='relu'),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    \n",
+    "    Flatten(),\n",
+    "    Dense(128, activation='relu'),\n",
+    "    Dense(5, activation='softmax')  # Annahme: 5 verschiedene Hunderassen\n",
+    "])\n",
+    "\n",
+    "# Kompiliere das Modell\n",
+    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Trainiere das Modell mit den Trainingsdaten und validiere es mit den Validierungsdaten\n",
+    "model.fit(train_generator_augmented, epochs=10, validation_data=validation_generator)\n",
+    "\n",
+    "# Bewerte das Modell mit den Testdaten\n",
+    "test_loss, test_accuracy = model.evaluate(test_generator)\n",
+    "print(\"Test loss:\", test_loss)\n",
+    "print(\"Test accuracy:\", test_accuracy)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 579 images belonging to 5 classes.\n",
+      "Found 124 images belonging to 5 classes.\n",
+      "Found 235 images belonging to 5 classes.\n",
+      "Epoch 1/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 3s/step - accuracy: 0.2039 - loss: 2.5350 - val_accuracy: 0.2097 - val_loss: 1.6096\n",
+      "Epoch 2/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 4s/step - accuracy: 0.1875 - loss: 1.6273 - val_accuracy: 0.2097 - val_loss: 1.6332\n",
+      "Epoch 3/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 4s/step - accuracy: 0.2389 - loss: 1.6126 - val_accuracy: 0.2339 - val_loss: 1.6050\n",
+      "Epoch 4/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 3s/step - accuracy: 0.2433 - loss: 1.6050 - val_accuracy: 0.2339 - val_loss: 1.6064\n",
+      "Epoch 5/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 3s/step - accuracy: 0.2169 - loss: 1.6130 - val_accuracy: 0.2339 - val_loss: 1.6071\n",
+      "Epoch 6/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 3s/step - accuracy: 0.2531 - loss: 1.6066 - val_accuracy: 0.2339 - val_loss: 1.6066\n",
+      "Epoch 7/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 3s/step - accuracy: 0.2488 - loss: 1.6057 - val_accuracy: 0.2339 - val_loss: 1.6055\n",
+      "Epoch 8/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 3s/step - accuracy: 0.2469 - loss: 1.6042 - val_accuracy: 0.2339 - val_loss: 1.6046\n",
+      "Epoch 9/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 3s/step - accuracy: 0.2664 - loss: 1.6031 - val_accuracy: 0.2339 - val_loss: 1.6029\n",
+      "Epoch 10/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 3s/step - accuracy: 0.2423 - loss: 1.6020 - val_accuracy: 0.2339 - val_loss: 1.6027\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 724ms/step - accuracy: 0.1127 - loss: 1.6211\n",
+      "Test loss: 1.6218193769454956\n",
+      "Test accuracy: 0.12340425699949265\n",
+      "Epoch 1/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 2s/step - accuracy: 0.3654 - loss: 1.9313 - val_accuracy: 0.2097 - val_loss: 154.1117\n",
+      "Epoch 2/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2s/step - accuracy: 0.4419 - loss: 1.4328 - val_accuracy: 0.2097 - val_loss: 1264.2780\n",
+      "Epoch 3/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 2s/step - accuracy: 0.5456 - loss: 1.2366 - val_accuracy: 0.1774 - val_loss: 305.5775\n",
+      "Epoch 4/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2s/step - accuracy: 0.4869 - loss: 1.2390 - val_accuracy: 0.2339 - val_loss: 58.4713\n",
+      "Epoch 5/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2s/step - accuracy: 0.5579 - loss: 1.1611 - val_accuracy: 0.1855 - val_loss: 5.8782\n",
+      "Epoch 6/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2s/step - accuracy: 0.6491 - loss: 0.9665 - val_accuracy: 0.1774 - val_loss: 2.1772\n",
+      "Epoch 7/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 2s/step - accuracy: 0.6568 - loss: 0.9378 - val_accuracy: 0.2097 - val_loss: 3.0409\n",
+      "Epoch 8/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 2s/step - accuracy: 0.6401 - loss: 0.9623 - val_accuracy: 0.1774 - val_loss: 5.2191\n",
+      "Epoch 9/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2s/step - accuracy: 0.6719 - loss: 0.9113 - val_accuracy: 0.1774 - val_loss: 9.8093\n",
+      "Epoch 10/10\n",
+      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 2s/step - accuracy: 0.6057 - loss: 1.0410 - val_accuracy: 0.1774 - val_loss: 22.0870\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 365ms/step - accuracy: 0.0961 - loss: 23.2958\n",
+      "Test loss: 23.437267303466797\n",
+      "Test accuracy: 0.0936170220375061\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.applications import VGG16, ResNet50\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
+    "\n",
+    "\n",
+    "# Definiere die Größe der Bilder und den Pfad zum Datensatz\n",
+    "img_size = (150, 150)\n",
+    "data_dir = r'C:\\kiamep'\n",
+    "\n",
+    "# Erstelle Datengeneratoren für Training mit Datenvergrößerung\n",
+    "train_datagen_augmented = ImageDataGenerator(\n",
+    "    rescale=1./255,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    ")\n",
+    "\n",
+    "# Erstelle Datengeneratoren für Validierung und Test ohne Datenvergrößerung\n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "train_generator_augmented = train_datagen_augmented.flow_from_directory(\n",
+    "    data_dir + '\\\\Trainingsdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    ")\n",
+    "\n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Validierungsdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    ")\n",
+    "\n",
+    "test_generator = test_datagen.flow_from_directory(\n",
+    "    data_dir + '\\\\Testdaten',\n",
+    "    target_size=img_size,\n",
+    "    batch_size=32,\n",
+    "    class_mode='categorical',\n",
+    ")\n",
+    "\n",
+    "# VGG16-Modell laden\n",
+    "base_model_vgg16 = VGG16(weights='imagenet', include_top=False, input_shape=(img_size[0], img_size[1], 3))\n",
+    "\n",
+    "# Füge eigene Klassifizierungsschicht hinzu\n",
+    "model_vgg16 = Sequential([\n",
+    "    base_model_vgg16,\n",
+    "    GlobalAveragePooling2D(),\n",
+    "    Dense(256, activation='relu'),\n",
+    "    Dense(5, activation='softmax')  # Annahme: 5 verschiedene Hunderassen\n",
+    "])\n",
+    "\n",
+    "# Kompiliere das Modell\n",
+    "model_vgg16.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Trainiere das Modell\n",
+    "model_vgg16.fit(train_generator_augmented, epochs=10, validation_data=validation_generator)\n",
+    "\n",
+    "# Bewerte das Modell mit den Testdaten\n",
+    "test_loss, test_accuracy = model_vgg16.evaluate(test_generator)\n",
+    "print(\"Test loss:\", test_loss)\n",
+    "print(\"Test accuracy:\", test_accuracy)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ResNet50-Modell laden\n",
+    "base_model_resnet50 = ResNet50(weights='imagenet', include_top=False, input_shape=(img_size[0], img_size[1], 3))\n",
+    "\n",
+    "# Füge eigene Klassifizierungsschicht hinzu\n",
+    "model_resnet50 = Sequential([\n",
+    "    base_model_resnet50,\n",
+    "    GlobalAveragePooling2D(),\n",
+    "    Dense(256, activation='relu'),\n",
+    "    Dense(5, activation='softmax')  # Annahme: 5 verschiedene Hunderassen\n",
+    "])\n",
+    "\n",
+    "# Kompiliere das Modell\n",
+    "model_resnet50.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Trainiere das Modell\n",
+    "model_resnet50.fit(train_generator_augmented, epochs=10, validation_data=validation_generator)\n",
+    "\n",
+    "# Bewerte das Modell mit den Testdaten\n",
+    "test_loss, test_accuracy = model_resnet50.evaluate(test_generator)\n",
+    "print(\"Test loss:\", test_loss)\n",
+    "print(\"Test accuracy:\", test_accuracy)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kia",
+   "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": "3.12.2"
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+ "nbformat": 4,
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