def predict(self, data, *args): if self.preprocessing: data = self.preprocessing(data, *args) with tf.Graph().as_default() as _: variables_test, sampler_test = machine_reading_sampler(data, batch_size=None, shuffling=False) ops = embedding_updater_model(variables_test, rank=self.rank, n_ents=self.n_ents, n_slots=self.n_slots, init_params=self.params) nll, pred, y = tf_eval(ops) return pred, y, nll
import tensorflow as tf from naga.shared.tf_addons import tf_eval a = tf.Variable([[1, 2], [3, 4], [5, 6]]) b = tf.constant(3) print(tf_eval(tf.minimum(a, 2)))