Training a VAE in PyTorch | Loss Curves, Reconstructions & Samples скачать в хорошем качестве

Training a VAE in PyTorch | Loss Curves, Reconstructions & Samples

In this guided Colab walkthrough, we move from building the VAE model to actually training it and evaluating whether it learns. The goal of this section is to complete Checkpoint 3 — Training, which means showing evidence that training is working through both numerical metrics and visual outputs. In this video, we cover: implementing the training and validation loops using Adam and logging total / reconstruction / KL losses running training for multiple epochs storing metrics in a history dictionary plotting train vs validation loss curves generating a reconstruction grid generating a sample grid identifying common failure modes such as: NaNs / exploding loss scaling mismatches washed-out reconstructions noisy samples posterior collapse The key message is simple: A model has not “learned” just because the notebook ran. Training is only convincing when the curves and the generated outputs tell a consistent story. By the end of this section, learners should be able to show: working train and validation loops visible train/val loss curves one reconstruction grid one sample grid a short evidence-based interpretation of results This video is part of the GenAISA course on generative AI and model literacy. GenAISA project funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.