FLAGS.layers)
trainer = Trainer(seq2seq, FLAGS.optim, FLAGS.eta, FLAGS.clip)
train_writer = tf.summary.FileWriter(FLAGS.outdir + "/train",
sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
trainer.prepare(saver)
err_min = 100
last_improved = 0
for i in range(FLAGS.epochs):
print('Training epoch %d' % (i + 1))
trainer.train(ts, sess, train_writer, FLAGS.dropout, FLAGS.batchsz)
#print_batch(trainer.best_in_batch(es, sess, FLAGS.batchsz), rlut2)
err_rate = trainer.test(es, sess, FLAGS.batchsz)
if err_rate < err_min:
last_improved = i
err_min = err_rate
print('Lowest error achieved yet -- writing model')
seq2seq.save(sess, FLAGS.outdir, 'seq2seq')
trainer.checkpoint(sess, FLAGS.outdir, 'seq2seq')
if (i - last_improved) > FLAGS.patience:
print('Stopping due to persistent failures to improve')
break
init = tf.global_variables_initializer()
sess.run(init)
model.save_using(tf.train.Saver())
max_acc = 0
last_improved = 0
for i in range(FLAGS.epochs):
print('Training epoch %d' % (i + 1))
trainer.train(ts, FLAGS.dropout, FLAGS.batchsz)
this_acc = trainer.test(vs, FLAGS.batchsz, 'Validation')
if this_acc > max_acc:
max_acc = this_acc
last_improved = i
trainer.checkpoint(FLAGS.save)
print('Highest dev acc achieved yet -- writing model')
if (i - last_improved) > FLAGS.patience:
print('Stopping due to persistent failures to improve')
break
print("-----------------------------------------------------")
print('Highest dev acc %.2f' % (max_acc * 100.))
print('=====================================================')
print('Evaluating best model on test data:')
print('=====================================================')
trainer.recover_last_checkpoint()
this_acc = trainer.test(es)
print("-----------------------------------------------------")
print('Test acc %.2f' % (this_acc * 100.))
