【发布时间】：2017-04-21 10:42:09
【问题描述】：
我正在尝试将三类分类问题的损失函数定义如下：
def func_loss(y_true, y_pred):
return -K.mean(K.prod(K.cast(K.argmax(y_pred, axis=1), K.floatx()) - 1.0, K.cast(K.argmax(y_true, axis=1), K.floatx()) - 1.0))
我的y 看起来像这样：
[[1,0,0], [0,1,0], [1,0,0], [0,0,1], ...]
直观地说，我的三类标签是“-1”、“0”和“+1”类的单热编码。我想最大化“+/-1”的正确标签，最小化“+/-1”的错误标签并忽略所有“0”标签，无论它们是否正确。
当我用这个损失函数编译模型时，我得到的是：
Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 547, in compile
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 622, in compile
sample_weight, mask)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 324, in weighted
score_array = fn(y_true, y_pred)
File "", line 2, in func_loss
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 464, in prod
axis = _normalize_axis(axis, ndim(x))
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 435, in _normalize_axis
if axis is not None and axis < 0:
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 547, in nonzero
raise TypeError("Using a tf.Tensor as a Python bool is not allowed. "
TypeError: Using a tf.Tensor as a Python bool is not allowed. Use if t is not None: instead of if t: to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.
我一直在尝试对这个损失函数进行一些小调整，但是当我编译模型时，所有这些都会导致一些错误。我想我可能对这件事的工作原理有一些基本的误解。有人可以帮忙吗？
编辑： 新的损失函数：
def func_loss(y_true, y_pred):
return -K.mean((K.cast(K.argmax(y_pred, axis=1), K.floatx()) - 1.0 )* (K.cast(K.argmax(y_true, axis=1), K.floatx()) - 1.0))
【问题讨论】：
标签： python machine-learning tensorflow deep-learning keras