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Using coremltools to convert a Keras model into mlmodel for iOS
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import coremltools | |
import numpy | |
from keras.datasets import mnist | |
from keras.models import Sequential | |
from keras.layers import Dense | |
from keras.layers import Dropout | |
from keras.utils import np_utils | |
from keras.models import load_model | |
def convert_model(model): | |
print('converting...') | |
coreml_model = coremltools.converters.keras.convert(model,input_names=['image'],image_input_names='image') | |
coreml_model.author = 'YOUR NAME' | |
coreml_model.license = 'MIT' | |
coreml_model.short_description = 'Reads a handwritten digit. The model is based on keras mnist examples here. https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py but altered to strictly set up channels last as input_shape.' | |
coreml_model.input_description['image'] = 'A 28x28 pixel Image' | |
coreml_model.output_description['output1'] = 'A one-hot Multiarray were the index with the biggest float value (0-1) is the recognized digit. ' | |
coreml_model.save('keras_mnist_cnn.mlmodel') | |
print('model converted') | |
import os.path | |
if os.path.isfile('my_mnist_keras_cnn_model.h5'): | |
model = load_model('my_mnist_keras_cnn_model.h5') | |
convert_model(model) | |
else: | |
print('no model found') |
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Hi @jiayulinfs,
The problem is that currently coremltools supports Keras 1.2.2+ but it would be better if you pip the 1.2.2 itself; also, Tensorflow(if you're using it instead of Theano) should be 1.2.1 instead of 1.4.1. Then try removing the input_names parameter; worked for me, worth giving a shot.
Peace,
Akash