Using Denoising AutoEncoders in Keras (14.2)

Thank you so much for these videos and all that hard work Jeff, this is really opening up a whole new world of coding to me, really appreciate it! :)

Professor Heaton, I am trying the single image auto-encoder and happens to find out the accuracy is always 0 while the loss decreased from 12481.3857 to near 0.(after 200 epochs) Did I set the model wrong?( I used the same set up and Sequential model like yours in the code) Thank you! Great video!

Hi, Professor Jeff! :)

I’m just wondering—after we obtained the most important features from the bottleneck of our trained neural network, is it possible to apply the denoising capability of the autoencoder to a live feed video that is somewhat highly correlated to the training images?

Will this be better, or even recommended, instead of using traditional denoising filters of OpenCV for real-time videos?

I’d love to learn more from your expertise and advices as I explore this topic further. Thank you for the insightful explanation and demo by the way! Subscribed! :)

Jeff, you are next level. buddy <3

Hi, I study physics in Germany and i really enjoy your videos! Honestly spoken, i doubt that the network can generalize Denoising to other pictures, even if they are similar to the used one s. I think that the memory of the decoder, there are 50*100+100*128*128 weights (+biases), is more than enough to store the 10 pictures. I will try to put in some other pictures
Greedings

Sir, 
 I'm Siddhartha Chowdhary, 16 years old from Pune, India. First of all, thank you for creating such good content and a quick note 
I have successfully upgraded your GANS code written in Tensorflow 1.14 to Tensorflow 2.0 
So please provide me your e-mail ID so that I can share my work with you

And I have already prepared some progress points of the upgraded version of GAN for example Performance, Good Batch_Size….

Thank you😁😁😁😄