Reshaping Tensors With Flatten Dropout & SoftMax: Before & After Linear Layers
Working with the tensors before and after they are sent through the model layers is the major part of any training or inference pipeline. The data starts out as file on a hard disk, or signal inside a iot device. The models can process tensors / matrices format only, and this video dives into process of shaping these tensors.
Chapter Navigation:
0:00 Intro & recap
0:40 Introducing the Practice Steps
1:20 Creating higher dimension Tensors
2:50 How Much memory Tensor Takes
5:40 Introducing & Creating Flatten Layer
9:00 Using Flatten on Image Matrices
11:25 Linear layer creation & review
14:00 Forward Pass to Linear Layer
15:15 Purpose of Dropout Layer
17:10 Purpose of Softmax Layer
19:00 Introducing Argmax on SM output
20:10 Recap & Practice
21:10 Outro
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