The loss becomes neagative from positive values dring taining loop about keras

[td-jakubl]

1. For small values tf.math.log(output) is negative
2. tf.clip_by_value() is not working for nan e.g. if output contains nan then tf.clip_by_value(output,epsilon,1-epsilon) also contains nan if I'm not mistaken

[yijianSU22]

1. For small values tf.math.log(output) is negative
2. tf.clip_by_value() is not working for nan e.g. if output contains nan then tf.clip_by_value(output,epsilon,1-epsilon) also contains nan if I'm not mistaken

Thanks very much, yes, you're right. here should be -y_true *tf.math.log(output)

[yijianSU22]

1. For small values tf.math.log(output) is negative
2. tf.clip_by_value() is not working for nan e.g. if output contains nan then tf.clip_by_value(output,epsilon,1-epsilon) also contains nan if I'm not mistaken

Thanks very much, yes, you're right. here should be -y_true *tf.math.log(output)

hi，Sorry to bother you again，I don't know why I used the tf.keras.losses.CategoricalCrossentropy() to compute CE, the loss value still will be negative during training loop.