def load_higgs():
url = "http://opendata.cern.ch/record/328/files/atlas-higgs-challenge-2014-v2.csv.gz"
filename = os.path.join(get_data_dir(),
"atlas-higgs-challenge-2014-v2.csv.gz")
maybe_download(filename, url)
data = pd.read_csv(filename)
return data
def invoke(body):
# print(tf.__version__)
#print(body)
# Download Inception model if not already done.
maybe_download()
# Load the Inception model so it is ready for classifying images.
model = Inception()
image_path = download.maybe_download(body, data_dir)
# Path for a jpeg-image that is included in the downloaded data.
# image_path = os.path.join(data_dir, 'cropped_panda.jpg')
# Use the Inception model to classify the image.
pred = model.classify(image_path=image_path)
# Print the scores and names for the top-10 predictions.
res = json.dumps(model.print_scores(pred=pred, k=5))
# Close the TensorFlow session.
model.close()
# Transfer Learning is demonstrated in Tutorial #08.
return res
def download_data():
download.maybe_download_and_extract(url=data_url, download_dir=data_path)
download.maybe_download(url=mat_url, download_dir=data_path)