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Hello,
I have created a Neural Network → K Nearest Neighbors Classifier with python.
# followed by k-Nearest Neighbors for classification.
import coremltools
import coremltools.proto.FeatureTypes_pb2 as ft
from coremltools.models.nearest_neighbors import KNearestNeighborsClassifierBuilder
import copy
# Take the SqueezeNet feature extractor from the Turi Create model.
base_model = coremltools.models.MLModel("SqueezeNet.mlmodel")
base_spec = base_model._spec
layers = copy.deepcopy(base_spec.neuralNetworkClassifier.layers)
# Delete the softmax and innerProduct layers. The new last layer is
# a "flatten" layer that outputs a 1000-element vector.
del layers[-1]
del layers[-1]
preprocessing = base_spec.neuralNetworkClassifier.preprocessing
# The Turi Create model is a classifier, which is treated as a special
# model type in Core ML. But we need a general-purpose neural network.
del base_spec.neuralNetworkClassifier.layers[:]
base_spec.neuralNetwork.layers.extend(layers)
# Also copy over the image preprocessing options.
base_spec.neuralNetwork.preprocessing.extend(preprocessing)
# Remove other classifier stuff.
base_spec.description.ClearField("metadata")
base_spec.description.ClearField("predictedFeatureName")
base_spec.description.ClearField("predictedProbabilitiesName")
# Remove the old classifier outputs.
del base_spec.description.output[:]
# Add a new output for the feature vector.
output = base_spec.description.output.add()
output.name = "features"
output.type.multiArrayType.shape.append(1000)
output.type.multiArrayType.dataType = ft.ArrayFeatureType.FLOAT32
# Connect the last layer to this new output.
base_spec.neuralNetwork.layers[-1].output[0] = "features"
# Create the k-NN model.
knn_builder = KNearestNeighborsClassifierBuilder(input_name="features",
output_name="label",
number_of_dimensions=1000,
default_class_label="???",
number_of_neighbors=3,
weighting_scheme="inverse_distance",
index_type="linear")
knn_spec = knn_builder.spec
knn_spec.description.input[0].shortDescription = "Input vector"
knn_spec.description.output[0].shortDescription = "Predicted label"
knn_spec.description.output[1].shortDescription = "Probabilities for each possible label"
knn_builder.set_number_of_neighbors_with_bounds(3, allowed_range=(1, 10))
# Use the same name as in the neural network models, so that we
# can use the same code for evaluating both types of model.
knn_spec.description.predictedProbabilitiesName = "labelProbability"
knn_spec.description.output[1].name = knn_spec.description.predictedProbabilitiesName
# Put it all together into a pipeline.
pipeline_spec = coremltools.proto.Model_pb2.Model()
pipeline_spec.specificationVersion = coremltools._MINIMUM_UPDATABLE_SPEC_VERSION
pipeline_spec.isUpdatable = True
pipeline_spec.description.input.extend(base_spec.description.input[:])
pipeline_spec.description.output.extend(knn_spec.description.output[:])
pipeline_spec.description.predictedFeatureName = knn_spec.description.predictedFeatureName
pipeline_spec.description.predictedProbabilitiesName = knn_spec.description.predictedProbabilitiesName
# Add inputs for training.
pipeline_spec.description.trainingInput.extend([base_spec.description.input[0]])
pipeline_spec.description.trainingInput[0].shortDescription = "Example image"
pipeline_spec.description.trainingInput.extend([knn_spec.description.trainingInput[1]])
pipeline_spec.description.trainingInput[1].shortDescription = "True label"
pipeline_spec.pipelineClassifier.pipeline.models.add().CopyFrom(base_spec)
pipeline_spec.pipelineClassifier.pipeline.models.add().CopyFrom(knn_spec)
pipeline_spec.pipelineClassifier.pipeline.names.extend(["FeatureExtractor", "kNNClassifier"])
coremltools.utils.save_spec(pipeline_spec, "../Models/FaceDetection.mlmodel")
it is from the following tutorial: https://machinethink.net/blog/coreml-training-part3/
It Works and I were am to include it into my project:
I want to train the model via the MLUpdateTask:
ar batchInputs: [MLFeatureProvider] = []
let imageconstraint = (model.model.modelDescription.inputDescriptionsByName["image"]?.imageConstraint)
let imageOptions: [MLFeatureValue.ImageOption: Any] = [
.cropAndScale: VNImageCropAndScaleOption.scaleFill.rawValue]
var featureProviders = [MLFeatureProvider]()
//URLS where images are stored
let trainingData = ImageManager.getImagesAndLabel()
for data in trainingData{
let label = data.key
for imgURL in data.value{
let featureValue = try MLFeatureValue(imageAt: imgURL, constraint: imageconstraint!, options: imageOptions)
if let pixelBuffer = featureValue.imageBufferValue{
let featureProvider = FaceDetectionTrainingInput(image: pixelBuffer, label: label)
batchInputs.append(featureProvider)}}
let trainingData = MLArrayBatchProvider(array: batchInputs)
When calling the MLUpdateTask as follows, the context.model from completionHandler is null.
Unfortunately there is no other Information available from the compiler.
do{
debugPrint(context)
try context.model.write(to: ModelManager.targetURL)
}
catch{
debugPrint("Error saving the model \(error)")
}
})
updateTask.resume()
I get the following error when I want to access the context.model: Thread 5: EXC_BAD_ACCESS (code=1, address=0x0)
Can some1 more experienced tell me how to fix this?
It seems like I am missing some parameters?
I am currently not splitting the Data when training into train and test data. only preprocessing im doing is scaling the image down to 227x227 pixels.
Thanks!
Posted
by
Biberflosse83.
Last updated
Biberflosse83.
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