def test_build_share_rnn(self):
with self.test_session(config=self.sess_config) as sess:
m = model.Seq2SeqModel(check_feed_dict=False)
opt = {
'emb:vocab_size': 20,
'emb:dim': 5,
'cell:num_units': 10,
'cell:cell_class': 'tensorflow.nn.rnn_cell.BasicLSTMCell'
}
opt = {
f'{n}:{k}': v
for k, v in opt.items() for n in ('enc', 'dec')
}
opt['dec:logit:output_size'] = 2
c = 0
n = m.build_graph(opt=opt, name='q', **{'share:enc_dec_rnn': True})
for v in tf.global_variables():
self.assertNotEqual(v.name,
'q/dec/rnn/basic_lstm_cell/kernel:0',
'no decoder rnn')
self.assertNotEqual(v.name, 'q/dec/rnn/basic_lstm_cell/bias:0',
'no decoder rnn')
if 'rnn/basic_lstm_cell/kernel:0' in v.name:
c += 1
self.assertEqual(c, 1, 'only one cell')
def test_run_epoch(self):
data = generator.read_seq2seq_data(self.gen(), self.vocab, self.vocab)
batch_iter = partial(generator.seq2seq_batch_iter, *data,
batch_size=20, shuffle=True)
with self.test_session(config=self.sess_config) as sess:
m = tfm.Seq2SeqModel()
n = m.build_graph(**{'dec:attn_enc_output': True})
m.set_default_feed('dec.train_loss_denom', 20)
return_ph = tf.placeholder(tf.float32, shape=(None, None), name='return')
train_op = graph.create_train_op(m.training_loss, learning_rate=0.01)
train_pg_op = graph.create_pg_train_op(m.nll, return_ph)
return_feed_fn = partial(m.set_default_feed, return_ph)
sess.run(tf.global_variables_initializer())
run_fn = partial(run.run_epoch, sess, m, batch_iter)
for __ in range(5): # do some pre-train
train_info = run_fn(train_op=train_op)
print(train_info.summary())
reward_fn = generator.reward_match_label
eval_info = run.run_sampling_epoch(
sess, m, batch_iter, greedy=True, reward_fn=reward_fn)
print(eval_info.summary('eval'))
run_fn = partial(run.run_sampling_epoch, sess, m, batch_iter,
reward_fn=reward_fn)
for __ in range(10):
train_info = run_fn(train_op=train_pg_op, return_feed_fn=return_feed_fn)
print(train_info.summary())
eval_info2 = run.run_sampling_epoch(
sess, m, batch_iter, greedy=True, reward_fn=reward_fn)
print(eval_info2.summary('eval'))
self.assertLess(eval_info2.eval_loss, eval_info.eval_loss,
'after training, eval loss is lower.')
def test_build_share_emb_rnn_reuse(self):
with self.test_session(config=self.sess_config) as sess:
m = model.Seq2SeqModel(check_feed_dict=False)
opt = {
'emb:vocab_size': 20,
'emb:dim': 5,
'cell:num_units': 10,
'cell:cell_class': 'tensorflow.nn.rnn_cell.BasicLSTMCell'
}
opt = {
f'{n}:{k}': v
for k, v in opt.items() for n in ('enc', 'dec')
}
opt['dec:logit:output_size'] = 2
c_rnn = 0
c_emb = 0
n = m.build_graph(opt=opt,
name='q',
**{
'share:enc_dec_emb': True,
'share:enc_dec_rnn': True
})
for v in tf.global_variables():
self.assertNotEqual(v.name,
'q/dec/rnn/basic_lstm_cell/kernel:0',
'no decoder rnn')
self.assertNotEqual(v.name, 'q/dec/rnn/basic_lstm_cell/bias:0',
'no decoder rnn')
if 'rnn/basic_lstm_cell/kernel:0' in v.name:
c_rnn += 1
self.assertNotEqual(v.name, 't/dec/embedding:0',
'no decoder emb')
if '/embedding:0' in v.name:
c_emb += 1
self.assertEqual(c_emb, 1, 'only one embedding')
self.assertEqual(c_rnn, 1, 'only one cell')
num_vars = len(tf.global_variables())
n = m.build_graph(opt=opt,
name='q',
reuse=True,
**{
'share:enc_dec_emb': True,
'share:enc_dec_rnn': True
})
num_vars_ = len(tf.global_variables())
self.assertEqual(num_vars, num_vars,
'no new variables when reuse is True')
def test_build_attn(self):
with self.test_session(config=self.sess_config) as sess:
m = model.Seq2SeqModel(check_feed_dict=False)
opt = {
'emb:vocab_size': 20,
'emb:dim': 5,
'cell:num_units': 10,
'cell:cell_class': 'tensorflow.nn.rnn_cell.BasicLSTMCell'
}
opt = {
f'{n}:{k}': v
for k, v in opt.items() for n in ('enc', 'dec')
}
opt['dec:logit:output_size'] = 2
expected_vars = {
'embedding:0': (20, 5),
'rnn/basic_lstm_cell/kernel:0': (10 + 5, 10 * 4),
'rnn/basic_lstm_cell/bias:0': (10 * 4, )
}
expected_vars = {
f't/{n}/{k}': v
for k, v in expected_vars.items() for n in ('enc', 'dec')
}
expected_vars.update({
't/dec/logit_w:0': (2, 10),
't/dec/logit_b:0': (2, ),
't/dec/attention/dense/kernel:0': (20, 10),
't/dec/attention/dense/bias:0': (10, )
})
n = m.build_graph(opt, name='t', **{'dec:attn_enc_output': True})
for v in tf.global_variables():
# print(f'{v.name}, {v.get_shape()}')
self.assertTrue(v.name in expected_vars,
'expected variable scope/name')
self.assertEqual(v.shape, expected_vars[v.name],
'shape is correct')
for k, v in m._fetches.items():
if k is not None:
self.assertNotEqual(v[0], v[1], 'fetch array is set')
