Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Only the first 50 images considered during training ? !!! #105

Open
Abduoit opened this issue Jan 29, 2019 · 3 comments
Open

Only the first 50 images considered during training ? !!! #105

Abduoit opened this issue Jan 29, 2019 · 3 comments

Comments

@Abduoit
Copy link

Abduoit commented Jan 29, 2019

Hi,
During the training, I noticed that the network always takes the first 50 images only,
I found this line in the train.py

test_xs, test_ys = unzip(list(read_data("test/*.png"))[:50])

I changed it to this

test_xs, test_ys = unzip(list(read_data("test/*.png"))[:batch_size])

But I don't think this is correct, any suggestion please ??

@Abduoit Abduoit changed the title test_xs, test_ys = unzip(list(read_data("test/*.png"))[:batch_size]) Only the first 50 images considered during training ? !!! Jan 29, 2019
@mazcallu
Copy link

mazcallu commented Feb 1, 2019

Hi Abduoit,

I think that is only for the doreport when printing out the labeled license plates and the predictions in any case since batch_size = 50 your two lines of code seem to do exactly the same. When training, plate images are generated on the fly and the output from the network is compared to those so that loss can be minimized using gradient descent.
Regards

@Abduoit
Copy link
Author

Abduoit commented Feb 1, 2019

@mazcallu
Do you mean the number 50 is just for printing out the images in the terminal, but the actual training batch is happening based on the whole images in test folder.
I just want to make sure that training process is not only considering the first 50 images, it consider the whole images

@mazcallu
Copy link

mazcallu commented Feb 2, 2019

Please refer to this tutorial in order to understand how a similar program to train.py works:

https://pythonprogramming.net/tensorflow-neural-network-session-machine-learning-tutorial/

Once you understand this completely, go back to train.py and you should understand what is going on.

Regards

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants