def summ(step, wtr = tf.summary.FileWriter(pform(P.log, C.trial))
, summary = tf.summary.merge(
( tf.summary.scalar('step_errt', model.errt)
, tf.summary.scalar('step_loss', model.loss)))):
errt, loss = map(comp(np.mean, np.concatenate), zip(*chain(*(
batch_run(sess, m, (m.errt_samp, m.loss_samp), s, t, batch= C.batch_valid)
for m, (s, t) in zip(valid, data_valid)))))
wtr.add_summary(sess.run(summary, {model.errt: errt, model.loss: loss}), step)
wtr.flush()
def trans(sents, model, vocab):
for preds in batch_run(sess, model, model.pred, sents,
batch=C.batch_infer):
yield from decode(vocab, preds)