transE.py

import numpy as np

nentity = 100
nhidden = 20
nrelations = 10

entity_embedding = np.random.uniform(-10, 10, (nentity, nhidden))
relation_embedding = np.random.uniform(-1, 1, (nrelations, nhidden))

# For every iteration
head = entity_embedding[0]      # Pick any entity based negative or positive samples per batch
tail = entity_embedding[10]     # Pick any entity based negative or positive samples per batch

relation = relation_embedding[0] # Pick a relation that connects head and tail entities based on positive or negative samples

# Compute score using transE embedding
score = (head+relation) - tail

# Minimize loss etc....