def call(self,
logits=None,
input_length=None,
labels=None,
label_length=None,
soft_labels=None):
tags_scores = tf.reshape(logits,
[-1, self.max_seq_len, self.num_classes],
name="scores")
loss, _ = crf_log_likelihood(tags_scores, labels, input_length,
self.transitions)
return loss
def call(self,
logits=None,
input_length=None,
labels=None,
label_length=None,
**kwargs):
assert "model" in kwargs
model = kwargs["model"]
tags_scores = tf.reshape(
logits, [-1, model.max_len, model.seq_num_classes], name="scores")
loss, _ = crf_log_likelihood(tags_scores, labels, input_length,
model.transitions)
return loss
def test_crf_loss(self):
''' test crf loss '''
with self.cached_session():
loss_true = np.float32(5.5096426)
logits = np.asarray([[[0.3, 0.4, 0.3], [0.1, 0.9, 0.0], [0.2, 0.7, 0.1],
[0.3, 0.2, 0.5], [0.6, 0.2, 0.2]]],
dtype=np.float32) # [1,5,3]
trans_params = tf.fill([3, 3], 0.5, name='trans_params')
labels = np.asarray([[0, 1, 2, 0, 1]]) # shape=[1,5]
sequence_lengths = np.asarray([5]) # shape=[1,]
loss, _ = loss_utils.crf_log_likelihood(
tf.constant(logits), tf.constant(labels),
tf.constant(sequence_lengths), trans_params)
self.assertEqual(loss.eval(), loss_true)
