def sg_one_hot(tensor, opt):
r"""Converts a tensor into a one-hot tensor.
See `tf.one_hot()` in tensorflow.
Args:
tensor: A `Tensor` ( automatically given by chain )
opt:
depth: The number of classes.
name: If provided, replace current tensor's name.
Returns:
A `Tensor`.
"""
assert opt.depth is not None, 'depth is mandatory.'
return tf.one_hot(tensor, opt.depth, name=opt.name)
def ner_cost(tensor, opt):
one_hot_labels = tf.one_hot(opt.target - 1, opt.num_classes, dtype=tf.float32)
cross_entropy = one_hot_labels * tf.log(tensor)
cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)
mask = tf.sign(tf.abs(opt.target))
cross_entropy *= tf.cast(mask, tf.float32)
cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)
length = tf.cast(tf.reduce_sum(tf.sign(opt.target), reduction_indices=1), tf.int32)
cross_entropy /= tf.cast(length, tf.float32)
out = tf.reduce_mean(cross_entropy, name='ner_cost')
# add summary
tf.sg_summary_loss(out, name=opt.name)
return out
def ner_cost(tensor, opt):
one_hot_labels = tf.one_hot(opt.target - 1, opt.num_classes, dtype=tf.float32)
cross_entropy = one_hot_labels * tf.log(tensor)
cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)
mask = tf.sign(tf.reduce_max(tf.abs(one_hot_labels), reduction_indices=2))
cross_entropy *= tf.cast(mask, tf.float32)
cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)
length = tf.cast(tf.reduce_sum(tf.sign(opt.target), reduction_indices=1), tf.int32)
cross_entropy /= tf.cast(length, tf.float32)
out = tf.reduce_mean(cross_entropy, name='ner_cost')
# add summary
tf.sg_summary_loss(out, name=opt.name)
return out
def sg_one_hot(tensor, opt):
assert opt.depth is not None, 'depth is mandatory.'
return tf.one_hot(tensor, opt.depth, name=opt.name)