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import numpy as np | ||
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iris_labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] | ||
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def pre_inference(sample, metadata): | ||
return { | ||
metadata[0].name: [ | ||
sample["sepal_length"], | ||
sample["sepal_width"], | ||
sample["petal_length"], | ||
sample["petal_width"], | ||
] | ||
} | ||
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def post_inference(prediction, metadata): | ||
predicted_class_id = prediction[0][0] | ||
return {"class_label": iris_labels[predicted_class_id], "class_index": predicted_class_id} |
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keras | ||
keras2onnx | ||
tensorflow | ||
torch | ||
pandas | ||
skl2onnx | ||
sklearn | ||
onnxmltools | ||
xgboost |
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