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博主你好: 我看你在代码里用的是计算loss的时候用的是rpn_class_logits, 和rcnn_class_logits为什么不用rpn_probs和rcnn_probs来计算呢? 按我的理解,既然使用的交叉熵作为损失,并且label也是one_hot之后的,应该使用归一化的概率来计算啊,是有什么特殊情况么?
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因为Tensorflow提供一些函数,可以直接传入logits和label这两个参数去计算损失和梯度值。本质上,probs的在logits基础上多了exp计算,然后算梯度的时候再求导,相当于在prob上算log,即又变回了logits。所以TF提供了一些函数,在函数的内部实现里简化了计算量。
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博主你好:
我看你在代码里用的是计算loss的时候用的是rpn_class_logits, 和rcnn_class_logits为什么不用rpn_probs和rcnn_probs来计算呢?
按我的理解,既然使用的交叉熵作为损失,并且label也是one_hot之后的,应该使用归一化的概率来计算啊,是有什么特殊情况么?
The text was updated successfully, but these errors were encountered: