1. nan loss
It's a natural property of stochastic gradient descent, if the learning rate is too large, SGD can diverge into infinity.
Solution: 1) reduce learning rate
2) normalization helped
3) The solution for me was using tf.losses.sparse_softmax_cross_entropy(y, logits) instead of my own implementation of Safe Softmax using tf.nn.Softmax
1. nan loss It's a natural property of stochastic gradien...
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