Updatable Lunch Classifier for Core ML

lunch_model.py

# Creating a model with TuriCreate
import turicreate as tc

data = tc.image_analysis.load_images('image/train', with_path=True)

data['label'] = data['path'].apply(lambda path: 'healthy' if '/healthy' in path else 'fast food')
model = tc.image_classifier.create(data, target='label')
model.save("LunchImageClassifier.model")
model.export_coreml('LunchImageClassifier.mlmodel')

# Make model updatable process
import coremltools
# Loading & Inspecting
coreml_model_path = './LunchImageClassifier.mlmodel'
spec = coremltools.utils.load_spec(coreml_model_path)
builder = coremltools.models.neural_network.NeuralNetworkBuilder(spec=spec)
builder.inspect_layers(last=3)

model_spec = builder.spec

# Marking the updatable layer & Setting loss function
builder.make_updatable(['fc1'])
builder.set_categorical_cross_entropy_loss(name="lossLayer", input="labelProbability")
builder.set_epochs(10, [1, 10, 50])

# Chosing optimizer
from coremltools.models.neural_network import SgdParams
sgd_params = SgdParams(lr=0.001, batch=8, momentum=0)
sgd_params.set_batch(8, [1, 2, 8, 16])
builder.set_sgd_optimizer(sgd_params)

# Describing & saving
model_spec.description.trainingInput[0].shortDescription = 'Example image of lunch'
model_spec.description.trainingInput[1].shortDescription = 'Associated true label of example image'

from coremltools.models import MLModel
mlmodel_updatable = MLModel(model_spec)
coreml_updatable_model_path = './UpdatableLunchImageClassifier.mlmodel'
mlmodel_updatable.save(coreml_updatable_model_path)