cnnxgb.py

# Import required libraries

import tensorflow as tf

from tensorflow.keras.datasets import cifar10

from sklearn.model_selection import train_test_split

import xgboost as xgb

import numpy as np

from tensorflow.keras.applications.vgg16 import VGG16

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Load CIFAR10 dataset

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Preprocess data

x_train = x_train.astype('float32')

x_test = x_test.astype('float32')

x_train /= 255.0

x_test /= 255.0

# Split data into train and validation sets

x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2)

# Define VGG16 model for feature extraction

vgg16_model = VGG16(include_top=False, weights='imagenet', input_shape=(32, 32, 3))

# Extract features from train, validation, and test sets

train_features = vgg16_model.predict(x_train)

val_features = vgg16_model.predict(x_val)

test_features = vgg16_model.predict(x_test)

# Flatten features

train_features = np.reshape(train_features, (train_features.shape[0], -1))

val_features = np.reshape(val_features, (val_features.shape[0], -1))

test_features = np.reshape(test_features, (test_features.shape[0], -1))

# Define XGBoost model

xgb_model = xgb.XGBClassifier(objective='multi:softmax', num_class=10, max_depth=6, eta=0.3)

# Train XGBoost model on extracted features

xgb_model.fit(train_features, y_train)

# Evaluate XGBoost model on validation set

accuracy = xgb_model.score(val_features, y_val)

print(f"Accuracy: {accuracy}")

# Predict on test set

y_pred = xgb_model.predict(test_features)

# Evaluate XGBoost model on test set

accuracy = np.sum(y_pred == y_test) / len(y_test)

print(f"Accuracy: {accuracy}")