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import os
from neupy import storage, architectures
from imagenet_tools import (CURRENT_DIR, FILES_DIR, load_image,
print_top_n, download_file)
VGG16_WEIGHTS_FILE = os.path.join(FILES_DIR, 'vgg16.hdf5')
vgg16 = architectures.vgg16()
if not os.path.exists(VGG16_WEIGHTS_FILE):
download_file(
url="http://neupy.s3.amazonaws.com/tensorflow/imagenet-models/vgg16.hdf5",
filepath=VGG16_WEIGHTS_FILE,
description='Downloading weights')
storage.load(vgg16, VGG16_WEIGHTS_FILE)
dog_image = load_image(
os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(224, 224))
output = vgg16.predict(dog_image)
print_top_n(output, n=5)