about skip-gram code
tarogege opened this issue · 0 comments
tarogege commented
I don't quite understand that why 'batch_inputs', 'batch_labels' should be updated during each loop in Word2Vec-Skipgram-Tensor(Softmax).py .
Also ,what does 'trained_embeddings = W.eval()' mean?
Could you explain it for me?I am a bit confused.
`# code
for epoch in range(5000):
batch_inputs, batch_labels = random_batch(skip_grams, batch_size)
_, loss = sess.run([optimizer, cost], feed_dict={inputs: batch_inputs, labels: batch_labels})
if (epoch + 1)%1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
trained_embeddings = W.eval()`