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.
anaamrasool
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Getting error while try Neural Network Classifier Updatable code as it is from coremltools.readme.io
def convert_keras_to_mlmodel(keras_url, mlmodel_url):
from keras.models import load_model
keras_model = load_model(keras_url)
from coremltools.converters import keras as keras_converter
class_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
mlmodel = keras_converter.convert(keras_model, input_names=['image'],
output_names=['digitProbabilities'],
class_labels=class_labels,
predicted_feature_name='digit')
mlmodel.save(mlmodel_url)
coreml_model_path = './MNISTDigitClassifier.mlmodel'
convert_keras_to_mlmodel(keras_model_path , coreml_model_path)
Getting Below error:
ImportError Traceback (most recent call last)
Cell In[10], line 19
16 mlmodel.save(mlmodel_url)
18 coreml_model_path = './MNISTDigitClassifier.mlmodel'
---> 19 convert_keras_to_mlmodel(keras_model_path , coreml_model_path)
Cell In[10], line 9, in convert_keras_to_mlmodel(keras_url, mlmodel_url)
6 from keras.models import load_model
7 keras_model = load_model(keras_url)
----> 9 from coremltools.converters import keras as keras_converter
10 class_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
11 mlmodel = keras_converter.convert(keras_model, input_names=['image'],
12 output_names=['digitProbabilities'],
13 class_labels=class_labels,
14 predicted_feature_name='digit')
ImportError: cannot import name 'keras' from 'coremltools.converters' (/Users/anaamrasool/new-tensorflow-env/env/lib/python3.8/site-packages/coremltools/converters/init.py)