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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) | |
RESNET50_WEIGHTS_FILE = os.path.join(FILES_DIR, 'resnet50.hdf5') | |
DOG_IMAGE_PATH = os.path.join(CURRENT_DIR, 'images', 'german-shepherd.jpg') | |
def download_resnet50_weights(): | |
if not os.path.exists(RESNET50_WEIGHTS_FILE): | |
download_file( | |
url="http://neupy.s3.amazonaws.com/tensorflow/imagenet-models/resnet50.hdf5", | |
filepath=RESNET50_WEIGHTS_FILE, | |
description='Downloading weights') | |
print("File with ResNet-50 weights: {}".format(RESNET50_WEIGHTS_FILE)) | |
return RESNET50_WEIGHTS_FILE | |
if __name__ == '__main__': | |
resnet50_weights_filename = download_resnet50_weights() | |
resnet50 = architectures.resnet50() | |
print("Recovering ResNet-50 parameters...") | |
storage.load(resnet50, resnet50_weights_filename) | |
print("Making prediction...") | |
dog_image = load_image( | |
DOG_IMAGE_PATH, | |
image_size=(256, 256), | |
crop_size=(224, 224)) | |
output = resnet50.predict(dog_image) | |
print_top_n(output, n=5) |