Alright, so here’s the story about those over production under consumption images I was messing with today. It was a bit of a head-scratcher at first, but I think I’ve kinda got a handle on it now.
It all started when I was trying to generate a bunch of images for a project. The idea was to train a model to recognize… well, never mind what it was supposed to recognize. The important thing is, I needed a lot of images. Like, way more than I could ever take myself.
So, naturally, I looked into generating them. I started with some basic GANs (Generative Adversarial Networks), because everyone says they’re the bee’s knees for this sort of thing. Fired one up, tweaked the parameters, and let it run. For hours.
The Problem Hits
The initial results? Utter garbage. Just blurry messes that vaguely resembled what I was aiming for. I was getting _over production_ of garbage. My machine was spitting out images like crazy, but none of them were usable. It was like a factory churning out defective widgets.
Then, I started diving deeper into the code. I started by examining the loss functions. The discriminator loss seemed to be fine, it was learning to distinguish between real and fake images. But the generator loss? It was all over the place. Just bouncing around like a toddler on a sugar rush.
Okay, so the generator wasn’t learning properly. First thing I did was crank up the learning rate for the generator. I thought maybe it just wasn’t aggressive enough. Big mistake. That just made the whole thing collapse even faster. Even more blurry garbage.
Tweaking and Tuning
Alright, back to square one. I remembered reading somewhere that GANs are super sensitive to the architecture you use. So, I decided to try a different one. I switched to a DCGAN (Deep Convolutional GAN), which is supposed to be more stable. It’s a bit of a pain in the neck, re-writing stuff, but what choice did I have?
This time, things were… slightly better. The images were a little less blurry, but still not great. But, importantly, the generator loss was at least decreasing now. Progress! Baby steps. I was still in the _under consumption_ of actually useful images stage, mind you.
Next up: Data Augmentation. Figured maybe the discriminator needed more examples of the real stuff to learn from. So, I threw in some rotations, flips, and zooms to the training data. It looked like total chaos to my eyes, but hey, you gotta try things, right?
The (Almost) Breakthrough
And you know what? It actually helped! The images started to look… almost recognizable. Still had some weird artifacts, but the overall structure was getting there. I could finally see a glimmer of hope.
Then, the final piece of the puzzle: Batch Normalization. I had skipped it initially because I’m lazy, but decided to add it to both the generator and the discriminator. Boom! That was the magic bullet. The images suddenly snapped into focus. They weren’t perfect, but they were usable. Finally, I was getting production of images that could actually be used.
The Final Result
- Used DCGAN Architecture
- Implemented aggressive data augmentation.
- Added Batch Normalization to Generator and Discriminator.
So, that’s the story. It was a long day of debugging, tweaking, and a whole lot of frustration. But in the end, I got those over production under consumption images into a usable state. Always a good feeling!
Now, time for a beer.
