class Training(PyBioSchema):
setup = fields.Nested(Setup)
source = fields.ImportableSource(required=True)
required_kwargs = fields.List(fields.Str, missing=list)
optional_kwargs = fields.Dict(fields.Str, missing=dict)
dependencies = fields.Dependencies(required=True)
description = fields.Str(missing=None)
class TransformationSpec(BaseSpec):
dependencies = fields.Dependencies(required=True)
inputs = fields.Tensors(
InputArray, valid_magic_values=[MagicTensorsValue.any, MagicTensorsValue.dynamic], required=True
)
outputs = fields.Tensors(
OutputArray, valid_magic_values=[MagicTensorsValue.same, MagicTensorsValue.dynamic], required=True
)
class SamplerSpec(BaseSpec):
dependencies = fields.Dependencies(missing=None)
outputs = fields.Tensors(OutputArray, valid_magic_values=[MagicTensorsValue.dynamic], missing=None)
class ReaderSpec(BaseSpec):
dependencies = fields.Dependencies(missing=None)
outputs = fields.Tensors(OutputArray, valid_magic_values=[MagicTensorsValue.dynamic], required=True)
class Prediction(PyBioSchema):
weights = fields.Nested(Weights, missing=None)
dependencies = fields.Dependencies(missing=None)
preprocess = fields.Nested(Transformation, many=True, missing=list)
postprocess = fields.Nested(Transformation, many=True, missing=list)