prediction_joint.py

"""Private prediction on combined features from several clients"""
import tf_encrypted as tfe

from common import LogisticRegression, PredictionClient

num_features = 10

model = LogisticRegression(num_features)
prediction_client_0 = PredictionClient('prediction-client-0', num_features // 2)
prediction_client_1 = PredictionClient('prediction-client-1', num_features // 2)
result_receiver = prediction_client_0

x_0 = tfe.define_private_input(prediction_client_0.player_name,
                               prediction_client_0.provide_input)
x_1 = tfe.define_private_input(prediction_client_1.player_name,
                               prediction_client_1.provide_input)
x = tfe.concat([x_0, x_1], axis=1)

y = model.forward(x)

reveal_output = tfe.define_output(result_receiver.player_name,
                                  y,
                                  result_receiver.receive_output)

with tfe.Session() as sess:
  sess.run(tfe.global_variables_initializer(), tag='init')

  sess.run(reveal_output, tag='predict')