Delete code.py

code.py

@@ -1,60 +0,0 @@
-#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}%')