Classification Generator Testing Attempt - Unconventional Neural Networks p.5

TL;DR

Neural network training results in a classification generator with approximately 22% accuracy after 316 tests.

Transcript

what's going on everybody and welcome to part 5 of our shenanigans with the neural networks tutorial series in this part what we're gonna talk about is the results from training the gener the call it a classification generator so let's talk about it so as you can see what I'm running right here is actually the let's see you can I guess you can't se... Read More

Key Insights

• 🏆 The classification generator has achieved an accuracy of approximately 22% after 316 tests.
• ❓ The model is still in training and has completed less than 10% of the desired 50 epochs.
• 👨‍💻 The code logic for classification and detection of correct predictions needs further refinement.
• 🙈 The generator performs better at generating numbers it has seen before compared to numbers it hasn't seen.
• #️⃣ The accuracy of the generator in classifying unseen numbers is higher than in generating numbers.
• 🌱 The video creator plans to continue training the model and check the progress at later epochs.

Questions & Answers

Q: How accurate is the current classification generator after training?

The classification generator currently has an accuracy of approximately 22% after 316 tests.

Q: How far along is the model in training?

The model has completed less than 10% of the desired 50 epochs.

Q: What changes were made in the code for classification and detection of correct predictions?

The code logic uses Arg max to determine if the prediction matches the label, but the current logic is not optimal and needs refinement.

Q: How well does the generator perform when it comes to generating numbers?

The generator performs relatively well in generating numbers it has seen before, but struggles with generating numbers it hasn't seen.

Summary & Key Takeaways

• The video discusses the results of training a classification generator using neural networks, which currently has an accuracy of around 22%.

• The model is still in training and has only completed less than 10% of the desired 50 epochs.

• The code used for classification and detection of correct predictions is briefly explained.