Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D

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TensorFlow.js version

1.2.3

Browser version

Node 10.15

Describe the problem or feature request

When loading in a converted layer model, I get the following error message: Error: Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead. The model was a variation of Xception. It contains many SeparableConv2D layer and a few of them uses kernal_initializer. It appeared that the tfjs version think those fields are invalid.

Code to reproduce the bug / link to feature request

1. You can reproduce by saving the Xception application model into a h5 keras model using model.save.
2. Convert the model into tfjs_layer_models as describe by tensorflowjs_converter website
3. Load it back in using tfjs const tf = require("@tensorflow/tfjs"); const tfn = require("@tensorflow/tfjs-node"); const handler = tfn.io.fileSystem("./layer_model/model.json"); const model = await tf.loadLayersModel(handler);

[ghost]

Should this be closed? This is still an issue now. Is there an update on the progress of this bug?

[jerrydeng]

The underlying implementation of Xception is directly from tf.keras.applications with no change to any ContNet. If the use of the attributes are invalid, then the Xception implementation in tf.keras should be fixed as well.