main_code.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Flatten, Dense, Dropout
from tensorflow.keras.models import Model
from sklearn.metrics import classification_report, confusion_matrix

# Set random seed for reproducibility
np.random.seed(42)
tf.random.set_seed(42)

# Define data directories
train_dir = 'synthetic_data/train'
test_dir = 'synthetic_data/test'

# Data preprocessing and augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary'
)

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary',
    shuffle=False
)

# Define the model architecture
base_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
for layer in base_model.layers:
    layer.trainable = False

x = Flatten()(base_model.output)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
output = Dense(1, activation='sigmoid')(x)

model = Model(inputs=base_model.input, outputs=output)

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(
    train_generator,
    steps_per_epoch=len(train_generator),
    epochs=10,
    validation_data=test_generator,
    validation_steps=len(test_generator)
)

# Evaluate the model
test_loss, test_accuracy = model.evaluate(test_generator, steps=len(test_generator))
print(f'Test Loss: {test_loss}, Test Accuracy: {test_accuracy}')

# Predictions
predictions = model.predict(test_generator)
predicted_classes = np.where(predictions > 0.5, 1, 0)

# Classification report
print(classification_report(test_generator.classes, predicted_classes))

# Confusion matrix
conf_matrix = confusion_matrix(test_generator.classes, predicted_classes)
print('Confusion Matrix:')
print(conf_matrix)