I am working on the neural network classifier provided on the coremltools.readme.io in the updatable->neural network section(https://coremltools.readme.io/docs/updatable-neural-network-classifier-on-mnist-dataset). I am using the same code but I get an error saying that the coremltools.converters.keras.convert does not exist. But this I know can be coreml version issue. Right know I am using coremltools version 6.2. I converted this model to mlmodel with .convert only. It got converted successfully. But I face an error in the make_updatable function saying the loss layer must be softmax output. Even the coremlt package API reference there I found its because the layer name is softmaxND but it should be softmax. Now the problem is when I convert the model from Keras sequential model to coreml model. the layer name and type change. And the softmax changes to softmaxND. Does anyone faced this issue? if I execute this builder.inspect_layers(last=4) I get this output [Id: 32], Name: sequential/dense_1/Softmax (Type: softmaxND) Updatable: False Input blobs: ['sequential/dense_1/MatMul'] Output blobs: ['Identity'] [Id: 31], Name: sequential/dense_1/MatMul (Type: batchedMatmul) Updatable: False Input blobs: ['sequential/dense/Relu'] Output blobs: ['sequential/dense_1/MatMul'] [Id: 30], Name: sequential/dense/Relu (Type: activation) Updatable: False Input blobs: ['sequential/dense/MatMul'] Output blobs: ['sequential/dense/Relu'] In the make_updatable function when I execute builder.set_categorical_cross_entropy_loss(name='lossLayer', input='Identity') I get this error ValueError: Categorical Cross Entropy loss layer input (Identity) must be a softmax layer output.