One Training Step: Gradients for the Decoder Weights, a Learning Rate and the Loss Before and After
This lesson is part of the course
In this lesson
You take one gradient-descent step on the decoder weights for one toy image and show that the reconstruction loss really drops.
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Lessons in this course
- The Compression Idea: 2 × 2 Toy Images, an Encoder and a Two-Value BottleneckFree
- Decoding and Reconstruction Error: Sigmoid Outputs and Squared Errors Locked
- One Training Step: Gradients for the Decoder Weights, a Learning Rate and the Loss Before and After Locked
- Anomaly Scores: Reconstruction Errors, a Percentile Threshold, Precision and Recall Locked
- Latent Space: Codes on a Chart, Interpolating Between Images and Denoising Locked
- Variational Autoencoders: Mean and Log-Variance, Sampling with Given ε, the KL Term and β Locked
- GANs – the Two Players: Discriminator Scores, Discriminator Loss and Generator Loss Locked
- Capstone – Reading a Training Run: the Balance Point, Spread, Occupied Bins and Mode Collapse Locked