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We should have utility functions for constructing dense/convolutional layers (eventually more complex layers like LSTM or multihead attention), which take a context, input node identifiers, and initialization instructions, and return node identifiers for the layer output and the parameters.
A basic example of this is visible in the mnist_xla example.
Intializers should use XLA RNGs.
The text was updated successfully, but these errors were encountered:
As for construction, the main thing missing is convolutions. this should be an easy thing to finish up. the other thing is incorporating XLA RNGs which is a bit more math-heavy but still doable.
We should have utility functions for constructing dense/convolutional layers (eventually more complex layers like LSTM or multihead attention), which take a context, input node identifiers, and initialization instructions, and return node identifiers for the layer output and the parameters.
A basic example of this is visible in the mnist_xla example.
Intializers should use XLA RNGs.
The text was updated successfully, but these errors were encountered: