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The program generates some error messages when the images are of different sizes #1
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Hi @wenouyang. Yes, current implementation requires model to be rebuild with different |
Hi @eclique, Thanks for the reply. I got back to read your code again. The mask image, used in your case, is of shape (3, img_h*img_w, 2). I was trying to apply your model to the kaggle ultrasound case, in which the mask image is of shape (number_of_images, 1, img_h,img_w). Each pixel value can be 0 or 1 to present the labelling information. I think the different shape representation should be the reason to cause the error. I am thinking of two ways to adapt your code for the kaggle case.
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Hi, Besides, the current implementation uses "softmax" for the output layer. I noticed that some other implementations, aimed to solve semantic segmentation problems, use "sigmoid" for the output layer. Would you like to share any thoughts/consideration on when or whether to use sigmoid or softmax? Thanks. |
@wenouyang |
Hi
Thank you for sharing the code. I have been trying to apply your code a data set, which has image size of 128*128. However, running the code gives the following error message,
ValueError: Cannot feed value of shape (32, 1, 128, 128) for Tensor 'activation_26_target:0', which has shape '(?, ?, ?)'
Would you like to share any thought on the possible reason? Is that because the current implementation can only accept images with 256. If the image is of other sizes, we have to change the architecture. Thanks.The following is the whole traceback
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