# how to reconstruct image from feature space of convolutional neural network?

lets say i have fed an image into VGG19 pre-trained on imagenet as follows:

from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.models import Model
import numpy as np

base_model = VGG19(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block5_pool').output)

img_path = 'C:/shared/a5/images/training/n01518878_8432.JPEG'
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

features = model.predict(x)


i then extract the values at layer5 max pool layer, which are determined by the input image, and unique for every different image that is fed into the network.

does anyone know how to reconstruct the original image based on the features extracted from a specific layer? thank you