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+import os
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Concatenate, Input
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import matplotlib.pyplot as plt
+
+# Veri seti hazırlığı
+def load_images_and_texts(image_dir, text_data, img_size=(64, 64)):
+    """Görselleri ve metin açıklamalarını yükler."""
+    images, texts = [], []
+    for idx, row in text_data.iterrows():
+        img_path = os.path.join(image_dir, row['File_Name'] + '.png')
+        if os.path.exists(img_path):
+            img = load_img(img_path, target_size=img_size)
+            img_array = img_to_array(img) / 255.0
+            images.append(img_array)
+            texts.append(row['BERT_Embeddings'])
+    return np.array(images), np.array(texts)
+
+# CNN Modeli
+def build_cnn_model(image_shape, text_dim):
+    """CNN modeli: Görsel ve metin açıklamalarını birleştirerek sınıflandırma yapar."""
+    text_input = Input(shape=(text_dim,))
+    img_input = Input(shape=image_shape)
+
+    # Görsel kısmı
+    x_img = Conv2D(32, (3, 3), activation='relu', padding='same')(img_input)
+    x_img = MaxPooling2D((2, 2))(x_img)
+    x_img = Conv2D(64, (3, 3), activation='relu', padding='same')(x_img)
+    x_img = MaxPooling2D((2, 2))(x_img)
+    x_img = Flatten()(x_img)
+
+    # Metin kısmı
+    x_text = Dense(256, activation='relu')(text_input)
+
+    # Görsel ve metin birleşimi
+    x = Concatenate()([x_img, x_text])
+    x = Dense(128, activation='relu')(x)
+    x = Dense(1, activation='sigmoid')(x)  # Binary classification
+
+    model = Model([img_input, text_input], x, name="CNN_Model")
+    return model
+
+# Parametreler
+epochs = 1000  # 1000 epoch
+batch_size = 32
+image_shape = (64, 64, 3)
+text_dim = 768  # BERT embedding boyutu
+
+# Metin açıklamalarını yükleme
+pkl_path = '/content/drive/Othercomputers/Dizüstü Bilgisayarım/Desktop/word_embeddings_dataframe.pkl'
+data = pd.read_pickle(pkl_path)
+
+# Görseller ve metin açıklamalarını yükleme
+image_dir = '/content/drive/Othercomputers/Dizüstü Bilgisayarım/Desktop/human_annotated_images'
+images, texts = load_images_and_texts(image_dir, data)
+
+# Metin açıklamaları boyutunu düzeltme
+texts = np.squeeze(texts, axis=1)  # (N, 1, 768) -> (N, 768)
+
+# CNN Modeli oluşturma
+cnn_model = build_cnn_model(image_shape, text_dim)
+
+# Modeli derleme
+cnn_model.compile(optimizer=Adam(0.0002, 0.5), loss='binary_crossentropy', metrics=['accuracy'])
+
+# Eğitim döngüsü
+def train(epochs, batch_size):
+    for epoch in range(epochs):
+        # Gerçek görsellerden örnekleme
+        idx = np.random.randint(0, images.shape[0], batch_size)
+        real_images = images[idx]
+        real_texts = texts[idx]
+        labels = np.ones((batch_size, 1))  # Gerçek görseller için etiketler
+
+        # Eğitim
+        loss, accuracy = cnn_model.train_on_batch([real_images, real_texts], labels)
+
+        # İlerlemeyi yazdırma
+        if epoch % 10 == 0:
+            print(f"Epoch {epoch}/{epochs} | Loss: {loss} | Accuracy: {accuracy}")
+
+        # Modeli her 100 epoch'ta kaydetme
+        if epoch % 100 == 0:
+            cnn_model.save(f'cnn_model_epoch_{epoch}.h5')
+
+# Modeli eğit
+train(epochs, batch_size)
+
+# Üretilen örnekleri kaydetme
+def generate_and_save_samples(cnn_model, num_samples=5):
+    idx = np.random.randint(0, images.shape[0], num_samples)
+    sample_images = images[idx]
+    sample_texts = texts[idx]
+    
+    predictions = cnn_model.predict([sample_images, sample_texts])
+    
+    for i, img in enumerate(sample_images):
+        plt.imshow(img)
+        plt.axis('off')
+        plt.title(f"Prediction: {predictions[i]}")
+        plt.savefig(f"sample_image_{i}.png")
+
+# Üretilen görselleri kaydetme
+generate_and_save_samples(cnn_model)