coremltools.models.MLModel¶

class coremltools.models.MLModel(model, useCPUOnly=False)¶

This class defines the minimal interface to a CoreML object in Python.

At a high level, the protobuf specification consists of:

Model description: Encodes names and type information of the inputs and outputs to the model.
Model parameters: The set of parameters required to represent a specific instance of the model.
Metadata: Information about the origin, license, and author of the model.

With this class, you can inspect a CoreML model, modify metadata, and make predictions for the purposes of testing (on select platforms).

See also

predict

Examples

# Load the model
>>> model =  MLModel('HousePricer.mlmodel')

# Set the model metadata
>>> model.author = 'Author'
>>> model.license = 'BSD'
>>> model.short_description = 'Predicts the price of a house in the Seattle area.'

# Get the interface to the model
>>> model.input_descriptions
>>> model.output_description

# Set feature descriptions manually
>>> model.input_description['bedroom'] = 'Number of bedrooms'
>>> model.input_description['bathrooms'] = 'Number of bathrooms'
>>> model.input_description['size'] = 'Size (in square feet)'

# Set
>>> model.output_description['price'] = 'Price of the house'

# Make predictions
>>> predictions = model.predict({'bedroom': 1.0, 'bath': 1.0, 'size': 1240})

# Get the spec of the model
>>> model.spec

# Save the model
>>> model.save('HousePricer.mlmodel')

__init__(self, model, useCPUOnly=False)¶

Construct an MLModel from a .mlmodel

Parameters:	model: str or Model_pb2 If a string is given it should be the location of the .mlmodel to load. useCPUOnly: bool Set to true to restrict loading of model on CPU Only. Defaults to False.

Examples

>>> loaded_model = MLModel('my_model_file.mlmodel')

Methods

`__init__`(self, model[, useCPUOnly])	Construct an MLModel from a .mlmodel
`get_spec`(self)	Get a deep copy of the protobuf specification of the model.
`predict`(self, data[, useCPUOnly])	Return predictions for the model.
`save`(self, filename)	Save the model to a .mlmodel format.
`visualize_spec`(\args, \\*kwargs)	Visualize the model.

Attributes

`author`
`input_description`
`license`
`output_description`
`short_description`
`user_defined_metadata`

__init__(self, model, useCPUOnly=False)

Construct an MLModel from a .mlmodel

Parameters:	model: str or Model_pb2 If a string is given it should be the location of the .mlmodel to load. useCPUOnly: bool Set to true to restrict loading of model on CPU Only. Defaults to False.

Examples

>>> loaded_model = MLModel('my_model_file.mlmodel')

get_spec(self)¶

Get a deep copy of the protobuf specification of the model.

Returns:	model: Model_pb2 Protobuf specification of the model.

Examples

>>> spec = model.get_spec()

predict(self, data, useCPUOnly=False, **kwargs)¶

Return predictions for the model. The kwargs gets passed into the model as a dictionary.

Parameters:	data: dict[str, value] Dictionary of data to make predictions from where the keys are the names of the input features. useCPUOnly: bool Set to true to restrict computation to use only the CPU. Defaults to False.
Returns:	out: dict[str, value] Predictions as a dictionary where each key is the output feature name.

Examples

>>> data = {'bedroom': 1.0, 'bath': 1.0, 'size': 1240}
>>> predictions = model.predict(data)

save(self, filename)¶

Save the model to a .mlmodel format.

Parameters:	filename: str Target filename for the model.

Parameters:	port: int if server is to be hosted on specific localhost port input_shape_dict: dict The shapes are calculated assuming the batch and sequence are 1 i.e. (1, 1, C, H, W). If either is not 1, then provide full input shape title: str Title for the visualized model
Returns:	None