Several observations during the empirical study about tf_unet HOT 2 CLOSED

1&2) I looked at the code, it could be that there is a bug when I set up the exponential decaying learning rate. Maybe it should be global_step=global_step*self.batch_size but I'm not sure and I can't test it atm.

3. I don't have any experience with the dice coefficient. I could change the tf_unet code a bit such that it becomes easier to change the loss function

4. everything you pass as opt_kwargs is forwarded to the optimizer. According to the API, momentum is not a parameter of AdamOptimizer. So the exception is expected

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Hi Joel,

I think your finding is right. This mnist example https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/mnist/convolutional.py#L245 discusses the same thing.

However, I have a question regarding your using global_step

the sess.run((self.optimizer ....)) (line 380 in unet.py)is called within the iteration setup by

for step in range((epoch*training_iters), ((epoch+1)*training_iters)):

It seems to me, we should use step*self.batch_size to get the current index in the dataset.
I understand when you define self.learning_rate_nodeat line 285, you can only get access global_step.

In specific, my question is how do you connect stepwith global_step. I could not find the code in your program that
can bridge these two vectors. I know I may miss something, but I am kind of confused where I am wrong.

Besides, during the training epochs, the prediction figures looks like the background (or opposite) of the raw image. Did you have similar observations even for your other data sets, if you have not tested the kaggle set. Thanks.

surfreta

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