from __future__ import absolute_import
import sugartensor as tf
__author__ = 'namju.kim@kakaocorp.com'
#
# evaluation layer
#
@tf.sg_sugar_func
[docs]def sg_accuracy(tensor, opt):
r"""Returns accuracy of predictions.
Args:
tensor: A `Tensor`. Probability distributions or unscaled prediction scores.
opt:
target: A 'Tensor`. Labels.
Returns:
A `Tensor` of the same shape as `tensor`. Each value will be 1 if correct else 0.
For example,
```
tensor = [[20.1, 18, -4.2], [0.04, 21.1, 31.3]]
target = [[0, 1]]
tensor.sg_accuracy(target=target) => [[ 1. 0.]]
```
"""
assert opt.target is not None, 'target is mandatory.'
opt += tf.sg_opt(k=1)
# # calc accuracy
out = tf.identity(tf.equal(tensor.sg_argmax(), tf.cast(opt.target, tf.int64)).sg_float(), name='acc')
# out = tf.identity(tf.nn.in_top_k(tensor, opt.target, opt.k).sg_float(), name='acc')
return out