upload 3 files

predict_breed.py

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+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import json
+
+def predict_breed(img_path):
+    model = tf.keras.models.load_model("dog_breed_classifier.h5")
+    with open("class_indices.json", "r") as f:
+        class_indices = json.load(f)
+        class_names = {v: k for k, v in class_indices.items()}
+
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)
+    predicted_class = class_names[np.argmax(prediction)]
+    print(f"Predicted Dog Breed: {predicted_class}")
+
+predict_breed(r"E:\Dog_Breed_Classification_model\dataset\Akita_Inu\Akita_Inu1.jpg")

requirements.txt

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+tensorflow>=2.11.0
+scikit-learn
+numpy
+pillow
+matplotlib
+tqdm

train_dog_breed.py

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+import os
+import shutil
+import random
+import numpy as np
+import json
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+SOURCE_DIR = r"E:\Dog_Breed_Classification_model\dataset"
+TARGET_DIR = r"E:\Dog_Breed_Classification_model\dataset_split"
+IMG_SIZE = (224, 224)
+BATCH_SIZE = 16
+EPOCHS = 20
+SEED = 42
+
+def split_dataset():
+    random.seed(SEED)
+    for split in ['train', 'val']:
+        os.makedirs(os.path.join(TARGET_DIR, split), exist_ok=True)
+
+    for breed in os.listdir(SOURCE_DIR):
+        class_path = os.path.join(SOURCE_DIR, breed)
+        if not os.path.isdir(class_path):
+            continue
+        images = [img for img in os.listdir(class_path) if img.lower().endswith(('.jpg', '.jpeg', '.png'))]
+        train_imgs, val_imgs = train_test_split(images, test_size=0.2, random_state=SEED)
+
+        for split, img_list in zip(['train', 'val'], [train_imgs, val_imgs]):
+            split_dir = os.path.join(TARGET_DIR, split, breed)
+            os.makedirs(split_dir, exist_ok=True)
+            for img in img_list:
+                shutil.copy2(os.path.join(class_path, img), os.path.join(split_dir, img))
+
+def create_generators():
+    train_gen = ImageDataGenerator(rescale=1./255, rotation_range=15, width_shift_range=0.1,
+                                   height_shift_range=0.1, horizontal_flip=True)
+    val_gen = ImageDataGenerator(rescale=1./255)
+
+    train_data = train_gen.flow_from_directory(os.path.join(TARGET_DIR, 'train'),
+                                               target_size=IMG_SIZE, batch_size=BATCH_SIZE,
+                                               class_mode='categorical')
+
+    val_data = val_gen.flow_from_directory(os.path.join(TARGET_DIR, 'val'),
+                                           target_size=IMG_SIZE, batch_size=BATCH_SIZE,
+                                           class_mode='categorical')
+    return train_data, val_data
+
+def build_model(num_classes):
+    base_model = tf.keras.applications.MobileNetV2(input_shape=(*IMG_SIZE, 3), include_top=False, weights='imagenet')
+    base_model.trainable = False
+    model = tf.keras.Sequential([
+        base_model,
+        tf.keras.layers.GlobalAveragePooling2D(),
+        tf.keras.layers.Dense(128, activation='relu'),
+        tf.keras.layers.Dropout(0.3),
+        tf.keras.layers.Dense(num_classes, activation='softmax')
+    ])
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+def save_model(model, class_indices):
+    model.save("dog_breed_classifier.h5")
+    with open("class_indices.json", "w") as f:
+        json.dump(class_indices, f)
+
+if __name__ == "__main__":
+    split_dataset()
+    train_data, val_data = create_generators()
+    model = build_model(num_classes=len(train_data.class_indices))
+    model.fit(train_data, validation_data=val_data, epochs=EPOCHS)
+    save_model(model, train_data.class_indices)