def cross_entropy(logits,
labels,
input_length=None,
label_length=None,
smoothing=0.0,
reduction=tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS):
'''
cross entropy function for classfication and seq classfication
:param, label_length, for seq task, this for target seq length, e.g. a b c </s>, 4
'''
del input_length
onehot_labels = tf.cond(
pred=tf.equal(tf.rank(logits) - tf.rank(labels), 1),
true_fn=lambda: tf.one_hot(labels, tf.shape(logits)[-1], dtype=tf.int32),
false_fn=lambda: labels)
if label_length is not None:
weights = utils.len_to_mask(label_length)
else:
weights = 1.0
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels,
logits=logits,
weights=weights,
label_smoothing=smoothing,
reduction=reduction)
return loss
def mask_sequence_loss(logits,
labels,
input_length,
label_length,
smoothing=0.0):
'''
softmax cross entropy loss for sequence to sequence
:param logits: [batch_size, max_seq_len, vocab_size]
:param labels: [batch_size, max_seq_len]
:param input_length: [batch_size]
:param label_length: [batch_size]
:return: loss, scalar
'''
del smoothing
del input_length
if label_length is not None:
weights = tf.cast(utils.len_to_mask(label_length), tf.float32)
else:
weights = tf.ones_like(labels)
loss = tf.contrib.seq2seq.sequence_loss(logits, labels, weights)
return loss