model

[lyc-lang]

The link here cannot be downloaded to the trained VGG16 model weights. Could you please send me a copy? mirror_whut@qq.com Thank you

[ChengBinJin]

@lyc-lang This field is more than 1.5GB, so I think I can't send you by email. I'm tried to diagnose your problem. But the link seems to work good. I just downloaded it just few minutes ago. Can you try it again?

[lyc-lang]

Many thanks，I have worked it out in another way.

[lyc-lang]

As you said that you have implemented a cross validation with Corresponding coefficient k = 6 , but I found in your dataset.py code self.train_data = [item for sub_block in blocks for item in sub_block] + \filenames[-np.mod(len(filenames), num_cross_vals):]，In this case, train_data overlaps with val_data and test_data when the data set is partitioned as you do. Any questions here？

[ChengBinJin]

@lyc-lang DCNN model is trained several times and each time the model uses different separations of the train, validation, and test. Therefore, they are not overlapped. The variable, idx_cross_val plays an essential role to control different validation and test.

[lyc-lang]

I am sorry to disturb you again. I know what you mean, however, when I took 10 files to test result, for example,I take k =5,Dataset('brain01', 5, 0) .the outcome shows the result that test_data = [0, 1], val_data = [2, 3], train_data =[4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9](each number represents a file name).Then I make a change by self.train_data = [item for sub_block in blocks for item in sub_block],and get result as test_data = [0, 1], val_data = [2, 3], train_data =[4,5,6,7,8,9], So is there a little bit of a flaw in the code here?

[ChengBinJin]

@lyc-lang Yes, you are right. The +filenames[-np.mod(len(filenames), num_cross_val):] wants to use the remaining data if the number of data can't divide by the number of blocks correctly. In your experiments, If you have 11 files and 5 blocks, then simply ignoring the above code can't use the last data file.

The bug if fixed as the following:

Thank you for your careful testing.

[lyc-lang]

In that case, the outcome of data separation is right. Thank you for reminding me！

[zichao999]

The link here cannot be downloaded to the trained VGG16 model weights. Could you please send me a copy? 79808121@qq.com Thank you

[lyc-lang]

I'm sorry！I can't send u now，maybe I could give u a copy in the next few weeks if I can have access to the internet. 

---Original--- From: "zichao999"<notifications@github.com> Date: Sat, Feb 29, 2020 22:40 PM To: "ChengBinJin/MRI-to-CT-DCNN-TensorFlow"<MRI-to-CT-DCNN-TensorFlow@noreply.github.com>; Cc: "lyc-lang"<mirror_whut@qq.com>;"Mention"<mention@noreply.github.com>; Subject: Re: [ChengBinJin/MRI-to-CT-DCNN-TensorFlow] model (#2)

The link here cannot be downloaded to the trained VGG16 model weights. Could you please send me a copy? 79808121@qq.com Thank you

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.

[ChengBinJin]

@zichao999 Hi, you can find the pretrained VGG model from the following link (https://yunpan.360.cn/surl_yxLDnu6QqjQ (password:3ouy)

[Yangshuo732]

@lyc-lang The link here cannot be downloaded to the trained VGG16 model weights. Could you please send me a copy? 3517162165@qq.com Thank you!