问题描述
我发现 tf.keras.losses.sparse_categorical_crossentropy 是一个了不起的类，可以帮助我为具有大量输出类的神经网络创建损失函数.没有这个，就不可能训练模型，因为我发现 tf.keras.losses.categorical_crossentropy 导致内存不足错误，因为将索引转换为非常大的1-hot向量
I found tf.keras.losses.sparse_categorical_crossentropy is an amazing class that helps me create a loss function for a neural network that has a large number of output classes. Without this it is impossible to train the model, as I found tf.keras.losses.categorical_crossentropy gave an out-of-memory error because of converting an index into a 1-hot vector of very large size.
但是，我在理解 sparse_categorical_crossentropy 如何避免出现大内存问题方面存在问题.我看了代码来自TF，但要了解幕后的情况确实不容易.
I, however, have a problem of understanding how sparse_categorical_crossentropy avoids the big memory issue. I took a look at the code from TF but it is indeed not easy to know what goes under the hood.
那么，任何人都可以提出一些实现此目标的高级想法吗?实现是什么样的?谢谢！
So, could anyone give some high-level idea of implementing this? What does the implementation look like?Thank you!
推荐答案
它没有做任何特殊的事情，它只是在丢失的数据中生成一批编码的单热编码标签(并非同时显示所有数据) ，在需要时将其丢弃，然后将其丢弃.因此，这只是内存和计算之间的经典折衷.
It does not do anything special, it just produces the one-hot encoded labels inside the loss for a batch of data (not all data at the same time), when it is needed, and then discards the results. So its just a classic trade-off between memory and computation.
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