Hi,
This quetion is related to CoreML custom model conversion. I have recently re-implemented two of my networks into Keras 1.2.2 format. For testing purposes, I designed a simple Deep CNNs for MNIST and the model got converted without issues. When I tried to convert my network with Dilated Convolution layers, coremltools is generating the following error -
```
>>> coreml_model = coremltools.converters.keras.convert(model)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "//anaconda/envs/coreml/lib/python2.7/site-packages/coremltools/converters/keras/_keras_converter.py", line 278, in convert
_check_unsupported_layers(model)
File "//anaconda/envs/coreml/lib/python2.7/site-packages/coremltools/converters/keras/_keras_converter.py", line 79, in _check_unsupported_layers
"Keras layer '%s' not supported. " % str(type(layer)))
ValueError: Keras layer '<class 'keras.layers.convolutional.AtrousConvolution2D'>' not supported.
```
To verify, I checked the documentation of coremltools - http://pythonhosted.org/coremltools/generated/coremltools.models.neural_network.html#coremltools.models.neural_network.NeuralNetworkBuilder - and I didn't observe any mentioning related to Deconvolution and Dilated Convolutions. Did anyone find a solution to this problem?
To CoreML team: I am interested to know if there will be any support for those layers in your future release?