Upload Dog_Training.py

Dog_Training.py

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+import os
+from PIL import Image
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+import pickle
+
+
+def load_images_from_folder(folder, img_size=(128, 128)):
+    images = []
+    labels = []
+    for breed in os.listdir(folder):
+        breed_path = os.path.join(folder, breed)
+        if os.path.isdir(breed_path):
+            for img in os.listdir(breed_path):
+                img_path = os.path.join(breed_path, img)
+                try:
+                    image = Image.open(img_path).convert('RGB')
+                    image = image.resize(img_size)
+                    images.append(np.array(image))
+                    labels.append(breed)
+                except Exception as e:
+                    print(f"Error loading image {img_path}: {e}")
+    return np.array(images), np.array(labels)
+
+
+# Lade und verarbeite die Trainings- und Testdaten
+train_images, train_labels = load_images_from_folder('DataDogs')
+test_images, test_labels = load_images_from_folder('DataDogs')
+
+# Label-Encoding für die Labels
+label_encoder = LabelEncoder()
+train_labels_encoded = label_encoder.fit_transform(train_labels)
+test_labels_encoded = label_encoder.transform(test_labels)
+
+# Definiere das CNN-Modell
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
+    MaxPooling2D((2, 2)),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D((2, 2)),
+    Conv2D(128, (3, 3), activation='relu'),
+    MaxPooling2D((2, 2)),
+    Flatten(),
+    Dense(512, activation='relu'),
+    Dropout(0.5),
+    Dense(len(label_encoder.classes_), activation='softmax')
+])
+
+# Kompiliere das Modell
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# Trainiere das Modell
+model.fit(train_images, train_labels_encoded, epochs=10, validation_data=(test_images, test_labels_encoded))
+
+# Speichere das trainierte Modell und den LabelEncoder
+model.save('dog_breed_classifier.h5')
+
+with open('label_encoder.pkl', 'wb') as f:
+    pickle.dump(label_encoder, f)