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License: MIT License
Center Loss implementation with TensorFlow
License: MIT License
在原有网络上添加center loss 更改了原有网络结构,因为添加了hidden_dim == center_dim的全连接层。
改成并行的两个全连接:
x = slim.flatten(x, scope='flatten')
feature = slim.fully_connected(x, num_outputs=2, activation_fn=None, scope='fc1')
x = slim.fully_connected(x, num_outputs=10, activation_fn=None, scope='fc2')
这样不会改变原结构,只是加了一个分支训练。求大神指正。。。
在每个batch中计算loss的时候,代码中是以每个batch中类别的中心。
按照paper中,应该是以当前经过网络的所有数据的中心计算吧,即centers_update_op
代码中的 centers_update_op 有什么用?看到只是一个返回值
谢谢。
能够用keras,但是具体的backend作为更改,不是很会。
请问如果在keras中添加,需要怎么操作。
参考论文里面(4)这个公式,在更新center points 的时候是不是应该只考虑用分类正确的features来更新center points?
whats the difference between training trainable centers jointly with major losses and update untrainable centers independently? ? I think, in joint way, the trainable centers will be updated as the independent way except the latter can have a different learning rate.
InvalidArgumentError (see above for traceback): Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero
[[node flatten/flatten/Reshape (defined at center_main+model.py:80) ]]
[[node acc/Mean (defined at center_main+model.py:101) ]]
hi,
TensorFlow_Center_Loss/center_loss.py
Line 31 in a5c1ad7
As the title, tks.
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