def main(server): num_rows = 7000 num_features = 32 num_epoch = 5 batch_size = 200 num_batches = (num_rows // batch_size) * num_epoch #who shall receive the output model_owner = ModelOwner('alice') data_schema0 = DataSchema([tf.float64] * 16, [0.0] * 16) data_schema1 = DataSchema([tf.int64] + [tf.float64] * 16, [0] + [0.0] * 16) data_owner_0 = DataOwner('alice', 'aliceTrainFile.csv', data_schema0, batch_size=batch_size) data_owner_1 = DataOwner('bob', 'bobTrainFileWithLabel.csv', data_schema1, batch_size=batch_size) tfe.set_protocol( tfe.protocol.Pond( tfe.get_config().get_player(data_owner_0.player_name), tfe.get_config().get_player(data_owner_1.player_name))) x_train_0 = tfe.define_private_input(data_owner_0.player_name, data_owner_0.provide_data) x_train_1 = tfe.define_private_input(data_owner_1.player_name, data_owner_1.provide_data) y_train = tfe.gather(x_train_1, 0, axis=1) y_train = tfe.reshape(y_train, [batch_size, 1]) #Remove bob's first column (which is label) x_train_1 = tfe.strided_slice(x_train_1, [0, 1], [x_train_1.shape[0], x_train_1.shape[1]], [1, 1]) x_train = tfe.concat([x_train_0, x_train_1], axis=1) model = LogisticRegression(num_features) reveal_weights_op = model_owner.receive_weights(model.weights) with tfe.Session() as sess: sess.run(tfe.global_variables_initializer(), tag='init') start_time = time.time() model.fit(sess, x_train, y_train, num_batches) end_time = time.time() # TODO(Morten) # each evaluation results in nodes for a forward pass being added to the graph; # maybe there's some way to avoid this, even if it means only if the shapes match model.evaluate(sess, x_train, y_train, data_owner_0) model.evaluate(sess, x_train, y_train, data_owner_1) print(sess.run(reveal_weights_op, tag='reveal'), ((end_time - start_time) * 1000))
def _gather(converter, node: Any, inputs: List[str]) -> Any: x_in = converter.outputs[inputs[0]] indices = converter.outputs[inputs[1]] axis = converter.outputs[inputs[2]] if isinstance(x_in, tf.NodeDef): input_out = _nodef_to_private_pond(converter, x_in) else: input_out = x_in indices_out = list(_nodef_to_numpy_array(indices)) axis_val = axis.attr["value"].tensor.int_val[0] return tfe.gather(input_out, indices_out, axis_val)
batch_size=batch_size) data_owner_1 = DataOwner('bob', f'{name}/tfe_label_train.csv', data_schema1, batch_size=batch_size) tfe.set_protocol( tfe.protocol.Pond( tfe.get_config().get_player(data_owner_0.player_name), tfe.get_config().get_player(data_owner_1.player_name))) x_train_0 = tfe.define_private_input(data_owner_0.player_name, data_owner_0.provide_data) x_train_1 = tfe.define_private_input(data_owner_1.player_name, data_owner_1.provide_data) y_train = tfe.gather(x_train_1, 0, axis=1) y_train = tfe.reshape(y_train, [batch_size, 1]) # Remove bob's first column (which is label) x_train_1 = tfe.strided_slice(x_train_1, [0, 1], [x_train_1.shape[0], x_train_1.shape[1]], [1, 1]) x_train = tfe.concat([x_train_0, x_train_1], axis=1) model = LinearRegression(num_features) # model = LogisticRegression(num_features) fit_forward_op = model.fit_forward(x_train, y_train, learning_rate=case['l']) reveal_weights_op = model_owner.receive_weights(model.weights)