Update code.py

code.py

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+#importing important libraries
+import numpy as np
+import keras
+from keras.applications.vgg16 import VGG16, preprocess_input
+from keras.layers import Flatten, Dense
+from keras.models import Model
+import cv2
+import os
+import numpy as np
+import tensorflow as tf
+
+from keras.models import Sequential
+from keras.preprocessing import image
+from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the pre-trained VGG16 model
+base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+
+# Freeze the base model layers
+for layer in base_model.layers:
+    layer.trainable = False
+
+# Add custom layers for face classification
+x = base_model.output
+x = Flatten()(x)
+x = Dense(1024, activation='relu')(x)
+predictions = Dense(1, activation='sigmoid')(x)
+
+# Create the final model
+model = Model(inputs=base_model.input, outputs=predictions)
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Define data generators for training and validation
+data_generator = ImageDataGenerator(preprocessing_function=preprocess_input)
+
+train_data = data_generator.flow_from_directory(
+    'img_for_deepfake_detection/train',
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='binary',
+     # Number of workers for parallel data loading
+)
+
+valid_data = data_generator.flow_from_directory(
+    'img_for_deepfake_detection/valid',
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='binary',
+     # Number of workers for parallel data loading
+)
+
+# Train the model
+model.fit(train_data, epochs=10, validation_data=valid_data)
+
+# Evaluate the model on the validation data
+loss, accuracy = model.evaluate(valid_data)
+print(f'Validation Accuracy: {accuracy*100:.2f}%')