本文整理匯總了Python中sugartensor.ones方法的典型用法代碼示例。如果您正苦於以下問題：Python sugartensor.ones方法的具體用法？Python sugartensor.ones怎麽用？Python sugartensor.ones使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sugartensor的用法示例。
在下文中一共展示了sugartensor.ones方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: sg_ctc
# 需要導入模塊: import sugartensor [as 別名]
# 或者: from sugartensor import ones [as 別名]
def sg_ctc(tensor, opt):
r"""Computes the CTC (Connectionist Temporal Classification) Loss between `tensor` and `target`.
Args:
tensor: A 3-D `float Tensor`.
opt:
target: A `Tensor` with the same length in the first dimension as the `tensor`. Labels. ( Dense tensor )
name: A `string`. A name to display in the tensor board web UI.
Returns:
A 1-D `Tensor` with the same length in the first dimension of the `tensor`.
For example,
```
tensor = [[[2., -1., 3.], [3., 1., -2.]], [[1., -1., 2.], [3., 1., -2.]]]
target = [[2., 1.], [2., 3.]]
tensor.sg_ctc(target=target) => [ 4.45940781 2.43091154]
```
"""
assert opt.target is not None, 'target is mandatory.'
# default sequence length
shape = tf.shape(tensor)
opt += tf.sg_opt(seq_len=tf.ones((shape[0],), dtype=tf.sg_intx) * shape[1], merge=True)
# ctc loss
out = tf.nn.ctc_loss(opt.target.sg_to_sparse(), tensor, opt.seq_len,
ctc_merge_repeated=opt.merge, time_major=False)
out = tf.identity(out, 'ctc')
# add summary
tf.sg_summary_loss(out, name=opt.name)
return out