Ahemale Tube May 2026

import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input

# Load the image of the female tube img_path = 'path_to_your_image.jpg' img = image.load_img(img_path, target_size=(224, 224)) ahemale tube

# Preprocess the image x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) import numpy as np from tensorflow

# Print the features print(features.shape) print(features) This code uses the VGG16 model to extract features from an image. You'll need to replace 'path_to_your_image.jpg' with the actual path to your image. # Extract features features = model

# Load the pre-trained VGG16 model model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

Keep in mind that this is just a starting point, and you may need to adjust the architecture, hyperparameters, and preprocessing steps to suit your specific use case.

# Extract features features = model.predict(x)