def test_train(self):
logger = util.get_logger(level='warning')
data = generator.read_seq_data(
self.gen(), self.vocab, self.vocab, keep_sentence=False, seq_len=20)
batch_iter = partial(generator.seq_batch_iter, *data,
batch_size=13, shuffle=True, keep_sentence=False)
with self.test_session(config=self.sess_config) as sess:
m = tfm.SeqModel()
n = m.build_graph({'rnn:fn': 'seqmodel.graph.scan_rnn',
'cell:num_layers': 2})
m.set_default_feed('train_loss_denom', 13)
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(m.training_loss)
sess.run(tf.global_variables_initializer())
train_run_fn = partial(run.run_epoch, sess, m, batch_iter, train_op=train_op)
eval_run_fn = partial(run.run_epoch, sess, m, batch_iter)
def stop_early(*args, **kwargs):
pass
eval_info = eval_run_fn()
train_state = run.train(train_run_fn, logger, max_epoch=3,
valid_run_epoch_fn=eval_run_fn,
end_epoch_fn=stop_early)
self.assertLess(train_state.best_eval, eval_info.eval_loss,
'after training, eval loss is lower.')
self.assertEqual(train_state.cur_epoch, 3, 'train for max epoch')
def test_run_epoch(self):
data = generator.read_seq_data(self.gen(),
self.vocab,
self.vocab,
keep_sentence=False,
seq_len=20)
batch_iter = partial(generator.seq_batch_iter,
*data,
batch_size=13,
shuffle=True,
keep_sentence=False)
with self.test_session(config=self.sess_config) as sess:
m = tfm.SeqModel()
n = m.build_graph()
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(m.training_loss)
sess.run(tf.global_variables_initializer())
run_fn = partial(run.run_epoch, sess, m, batch_iter)
eval_info = run_fn()
self.assertEqual(eval_info.num_tokens,
self.num_lines + self.num_tokens,
'run uses all tokens')
self.assertAlmostEqual(eval_info.eval_loss,
np.log(self.vocab.vocab_size),
places=1,
msg='eval loss is close to uniform.')
for __ in range(3):
train_info = run_fn(train_op=train_op)
self.assertLess(train_info.eval_loss, eval_info.eval_loss,
'after training, eval loss is lower.')
def test_read_seq_data(self):
x, y = generator.read_seq_data(self.gen(),
self.vocab,
self.vocab,
keep_sentence=True)
self.assertEqual(len(x), self.num_lines, 'number of sequences')
self.assertEqual(len(y), self.num_lines, 'number of sequences')
for x_, y_ in zip(x, y):
self.assertEqual(x_[1:], y_[:-1], 'output is shifted input')
def test_read_seq_data_sen(self):
x, y = generator.read_seq_data(self.gen(),
self.vocab,
self.vocab,
keep_sentence=False,
seq_len=20)
num_seq = (self.num_lines + self.num_tokens) // 20
if (self.num_lines + self.num_tokens) % 20 > 1:
num_seq += 1
self.assertEqual(len(x), num_seq, 'number of sequences')
self.assertEqual(len(y), num_seq, 'number of sequences')
for x_, y_ in zip(x, y):
self.assertEqual(x_[1:], y_[:-1], 'output is shifted input')
def test_seq_batch_iter(self):
data = generator.read_seq_data(self.gen(),
self.vocab,
self.vocab,
keep_sentence=False,
seq_len=20)
count = 0
for batch in generator.seq_batch_iter(*data,
batch_size=13,
shuffle=False,
keep_sentence=False):
count += batch.num_tokens
self.assertTrue(batch.keep_state, 'keep_state is True')
self.assertEqual(batch.num_tokens, sum(batch.features.seq_len),
'num_tokens is sum of seq_len')
self.assertEqual(count, self.num_lines + self.num_tokens,
'number of tokens (including eos symbol)')