Tags: CodingBeard/tfkg
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Improve performance with optional batchSize in compile call Fix bug with yProcessor not getting saved Add benchmark example Change signature of CompileAndLoad to allow changes in future without signature change Use correct default for embedding layer initializer Improve performance of fit by moving metrics and callbacks into go func
Add MSE loss and ability for non-categorical model types Add configurable y processor for single file and values dataset Add helper methods for binary/sparse categorical raw/tokenized y processors Add concurrency to values dataset to speed things up Update examples with new y processor design Fix example output of jobs Remove testing monet example Update transfer learning example with new weights get/set design Add LeakyReLU layer Add model methods to get weights in correct order / specific layer weights Remove hardcoded [][]int32 yTrue and [][]float32 yPred casts to allow for other model types Make categorical tokenizers have unlimited numWords Add early stopping callback for when a metric flatlines
Add transfer learning functionality between TFKG models with .SetLaye… …rWeights() on layer interface Fix issue where the first row of single_file_dataset was being ignored Add transfer_learning example Save order of model weights in json file in save dir to make transfer learning nicer
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