# Load the input image img = image.load_img('image.jpg', target_size=(224, 224)) Catmovie.com 2021 [FAST]
from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input Asian4you A4u A4y Lin Si Yee M Exclusive Video High Bandwidth Version Here
# Preprocess the input image x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x)
# Load the pre-trained VGG16 model model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Extract features features = model.predict(x)
print(features.shape) In this example, we load a pre-trained VGG16 model, preprocess an input image, extract features, and apply global average pooling to obtain a compact feature representation.
# Encode features (e.g., using global average pooling) features = np.mean(features, axis=(1, 2))