APIExamples.md

API Code snippets

Converting between MLModel and Spec

import coremltools

# Load MLModel
mlmodel = coremltools.models.MLModel('path/to/the/model.mlmodel')

# use model for prediction
mlmodel.predict(...)

# save the model
mlmodel.save('path/to/the/saved/model.mlmodel')

# Get spec from the model
spec = mlmodel.get_spec()

# print input/output description for the model
print(spec.description)

# save out the model directly from the spec
coremltools.models.utils.save_spec(spec,'path/to/the/saved/model.mlmodel')

# convert spec to MLModel, this step compiles the model as well
mlmodel = coremltools.models.MLModel(spec)

# Load the spec from the saved .mlmodel file directly
spec = coremltools.models.utils.load_spec('path/to/the/model.mlmodel')

Visualizing the CoreML model

import coremltools

mlmodel = coremltools.models.MLModel('path/to/the/model.mlmodel')
mlmodel.visualize_spec()

Another useful tool for visualizing CoreML models and models from other frameworks: Netron

Printing the pre-processing parameters

This is useful for image based neural network models

import coremltools

spec = coremltools.models.utils.load_spec('path/to/the/saved/model.mlmodel')

# Get neural network portion of the spec
if spec.WhichOneof('Type') == 'neuralNetworkClassifier':
  nn = spec.neuralNetworkClassifier
if spec.WhichOneof('Type') == 'neuralNetwork':
  nn = spec.neuralNetwork
elif spec.WhichOneof('Type') == 'neuralNetworkRegressor':
  nn = spec.neuralNetworkRegressor
else:
    raise ValueError('MLModel must have a neural network')
    
print(nn.preprocessing)