"Why Aidge" in Get Started
I propose a new section in the "Get Started" page of the documentation, to highlight the main differentiating factors of the framework. I think it is something very important and could also form the basis of future presentations.
New features
- supervised learning (Optimizer, lr scheduler, loss, backpropagation)
- initializer (filler)
- New attribute (Constant) for Producers to match ONNX parameters. A constant Producer cannot be updated by the learning process.
- better GraphView display with Tensors sizes, Operator type, unique indexes
- create custom implementation for any operator
- Tensor CPU implementation in core (easier prototyping)
- Pytorch interoperability
- parallel Scheduler
- support of RNN
- enhance
Tensor
- Tensor view to access without updating them
- scalar Tensor
- operator+,-,*,/
- introduction of dataloader to handle your own datasets
- better user interface
__get_attr__
,__set_attr__
- a complete logging mechanism with adjustable level
- introduction of PTQ
- many new tutorials
- update with operators:
operator | core | cpu / forward | cpu / backward | cuda / forward | ONNX import | ONNX export |
---|---|---|---|---|---|---|
AvgPooling | ||||||
Erf | ||||||
Concat | ||||||
FC |
|
|
||||
Gather | ||||||
GlobalAveragePooling | ||||||
Identity | ||||||
LeakyReLU | ||||||
LSTM | ||||||
MatMul | ||||||
MaxPooling | ||||||
Memorize | ||||||
Pop | ||||||
ReduceMean | ||||||
ReLU | ||||||
Reshape | ||||||
Sigmoid | ||||||
Slice | ||||||
Sqrt | ||||||
Sub | ||||||
Tanh | ||||||
Transpose |
Edited by Maxence Naud