Keras converter gives incorrect output shapes about coremltools HOT 4 CLOSED

This issue is essentially the question "how do we interpret shapes". In this permute case, Keras wants to permute a shape from (10,64,3) to (3,64,10); but because Keras uses (height, width, channels) for image feature axis while CoreML uses (channels, height, width), there are two ways to interpret what Permute in Keras is really doing:
(1) It wants the output tensor to be an image feature map with height=3, width=64, channels=10. In this case, the converter should respect the meaning of the axes, so the output shape should be (10,3,64).
(2) It wants the output tensor to be exactly of shape (3,64,10) with no meaning on the axes. In this case, the converter should respect the data layout in memory, so the output shape should be (3,64,10), same as Keras.
At this point the converter chooses to use (1) where the meaning of axes are respected, simply because if a Keras model has convolutions following Permute or Reshape, the converted CoreML model has the correct (C,H,W) axes to do the convolution.

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Is there a workaround for this problem? I believe it is causing issues with Conv1D layers. See https://stackoverflow.com/questions/47557591/incorrect-input-shape-in-coreml-after-converting-keras-model and https://stackoverflow.com/questions/49047418/keras-conv1d-to-coreml-not-behaving-as-expected

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me too

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Multi-backend Keras support has been removed in coremltools 6.0.

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