Related to UNet_Medical/TensorFlow2
TensorFlow2/Segmentation/UNet_Medical
Describe the bug
In the U-Net model, the last line, the output is with keras soft_max:
tf.keras.activations.softmax(out, axis=-1)
In the loss function, line 35, the crossentropy is calculated with tf.nn.softmax_cross_entropy_with_logits.
However, as mentioned by the tensorflow2 library, https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits
WARNING: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. Do not call this op with the output of softmax, as it will produce incorrect results.
So, I think this is a bug. Please correct me if I am wrong. Thank you.
Expected behavior
If it is bug, it should be change to be corrected
Related to UNet_Medical/TensorFlow2
TensorFlow2/Segmentation/UNet_Medical
Describe the bug
In the U-Net model, the last line, the output is with keras soft_max:
tf.keras.activations.softmax(out, axis=-1)
In the loss function, line 35, the crossentropy is calculated with tf.nn.softmax_cross_entropy_with_logits.
However, as mentioned by the tensorflow2 library, https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits
WARNING: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. Do not call this op with the output of softmax, as it will produce incorrect results.
So, I think this is a bug. Please correct me if I am wrong. Thank you.
Expected behavior
If it is bug, it should be change to be corrected