Keras¶
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coremltools.converters.keras._keras_converter._convert_to_spec(model, input_names=None, output_names=None, image_input_names=None, input_name_shape_dict={}, is_bgr=False, red_bias=0.0, green_bias=0.0, blue_bias=0.0, gray_bias=0.0, image_scale=1.0, class_labels=None, predicted_feature_name=None, model_precision='float32', predicted_probabilities_output='', add_custom_layers=False, custom_conversion_functions=None, custom_objects=None, input_shapes=None, output_shapes=None, respect_trainable=False, use_float_arraytype=False)¶ Convert a Keras model to Core ML protobuf specification (.mlmodel).
- Parameters
- model: Keras model object | str | (str, str)
A trained Keras neural network model which can be one of the following:
a Keras model object
a string with the path to a Keras model file (h5)
a tuple of strings, where the first is the path to a Keras model
architecture (.json file), the second is the path to its weights stored in h5 file.
- input_names: [str] | str
Optional name(s) that can be given to the inputs of the Keras model. These names will be used in the interface of the Core ML models to refer to the inputs of the Keras model. If not provided, the Keras inputs are named to [input1, input2, …, inputN] in the Core ML model. When multiple inputs are present, the input feature names are in the same order as the Keras inputs.
- output_names: [str] | str
Optional name(s) that can be given to the outputs of the Keras model. These names will be used in the interface of the Core ML models to refer to the outputs of the Keras model. If not provided, the Keras outputs are named to [output1, output2, …, outputN] in the Core ML model. When multiple outputs are present, output feature names are in the same order as the Keras inputs.
- image_input_names: [str] | str
Input names to the Keras model (a subset of the input_names parameter) that can be treated as images by Core ML. All other inputs are treated as MultiArrays (N-D Arrays).
- input_name_shape_dict: {str: [int]}
Optional Dictionary of input tensor names and their corresponding shapes expressed as a list of ints
- is_bgr: bool | dict()
Flag indicating the channel order the model internally uses to represent color images. Set to True if the internal channel order is BGR, otherwise it will be assumed RGB. This flag is applicable only if image_input_names is specified. To specify a different value for each image input, provide a dictionary with input names as keys. Note that this flag is about the models internal channel order. An input image can be passed to the model in any color pixel layout containing red, green and blue values (e.g. 32BGRA or 32ARGB). This flag determines how those pixel values get mapped to the internal multiarray representation.
- red_bias: float | dict()
Bias value to be added to the red channel of the input image. Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- blue_bias: float | dict()
Bias value to be added to the blue channel of the input image. Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- green_bias: float | dict()
Bias value to be added to the green channel of the input image. Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- gray_bias: float | dict()
Bias value to be added to the input image (in grayscale). Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- image_scale: float | dict()
Value by which input images will be scaled before bias is added and Core ML model makes a prediction. Defaults to 1.0. Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- class_labels: list[int or str] | str
Class labels (applies to classifiers only) that map the index of the output of a neural network to labels in a classifier.
If the provided class_labels is a string, it is assumed to be a filepath where classes are parsed as a list of newline separated strings.
- predicted_feature_name: str
Name of the output feature for the class labels exposed in the Core ML model (applies to classifiers only). Defaults to ‘classLabel’
- model_precision: str
Precision at which model will be saved. Currently full precision (float) and half precision (float16) models are supported. Defaults to ‘_MLMODEL_FULL_PRECISION’ (full precision).
- predicted_probabilities_output: str
Name of the neural network output to be interpreted as the predicted probabilities of the resulting classes. Typically the output of a softmax function. Defaults to the first output blob.
- add_custom_layers: bool
If True, then unknown Keras layer types will be added to the model as ‘custom’ layers, which must then be filled in as postprocessing.
- custom_conversion_functions: {‘str’: (Layer -> CustomLayerParams)}
A dictionary with keys corresponding to names of custom layers and values as functions taking a Keras custom layer and returning a parameter dictionary and list of weights.
- custom_objects: {‘str’: (function)}
Dictionary that includes a key, value pair of {‘<function name>’: <function>} for custom objects such as custom loss in the Keras model. Provide a string of the name of the custom function as a key. Provide a function as a value.
- respect_trainable: bool
If True, then Keras layers that are marked ‘trainable’ will automatically be marked updatable in the Core ML model.
- use_float_arraytype: bool
If true, the datatype of input/output multiarrays is set to Float32 instead of double.
- Returns
- model: MLModel
Model in Core ML format.
Examples
# Make a Keras model >>> model = Sequential() >>> model.add(Dense(num_channels, input_dim = input_dim)) # Convert it with default input and output names >>> import coremltools >>> coreml_model = coremltools.converters.keras.convert(model) # Saving the Core ML model to a file. >>> coreml_model.save('my_model.mlmodel')
Converting a model with a single image input.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = ... 'image', image_input_names = 'image')
Core ML also lets you add class labels to models to expose them as classifiers.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = 'image', ... image_input_names = 'image', class_labels = ['cat', 'dog', 'rat'])
Class labels for classifiers can also come from a file on disk.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = ... 'image', image_input_names = 'image', class_labels = 'labels.txt')
Provide customized input and output names to the Keras inputs and outputs while exposing them to Core ML.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = ... ['my_input_1', 'my_input_2'], output_names = ['my_output'])
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coremltools.converters.keras._keras_converter._get_layer_converter_fn(layer)¶ Get the right converter function for Keras
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coremltools.converters.keras._keras_converter._load_keras_model(model_network_path, model_weight_path, custom_objects=None)¶ Load a keras model from disk
- Parameters
- model_network_path: str
Path where the model network path is (json file)
- model_weight_path: str
Path where the model network weights are (hd5 file)
- custom_objects:
A dictionary of layers or other custom classes or functions used by the model
- Returns
- model: A keras model
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coremltools.converters.keras._keras_converter.convert(model, input_names=None, output_names=None, image_input_names=None, input_name_shape_dict={}, is_bgr=False, red_bias=0.0, green_bias=0.0, blue_bias=0.0, gray_bias=0.0, image_scale=1.0, class_labels=None, predicted_feature_name=None, model_precision='float32', predicted_probabilities_output='', add_custom_layers=False, custom_conversion_functions=None, input_shapes=None, output_shapes=None, respect_trainable=False, use_float_arraytype=False)¶ Convert a Keras model to Core ML protobuf specification (.mlmodel).
- Parameters
- model: Keras model object | str | (str, str)
A trained Keras neural network model which can be one of the following:
a Keras model object
a string with the path to a Keras model file (h5)
a tuple of strings, where the first is the path to a Keras model
- architecture (.json file), the second is the path to its weights stored in h5 file.
- input_names: [str] | str
Optional name(s) that can be given to the inputs of the Keras model. These names will be used in the interface of the Core ML models to refer to the inputs of the Keras model. If not provided, the Keras inputs are named to [input1, input2, …, inputN] in the Core ML model. When multiple inputs are present, the input feature names are in the same order as the Keras inputs.
- output_names: [str] | str
Optional name(s) that can be given to the outputs of the Keras model. These names will be used in the interface of the Core ML models to refer to the outputs of the Keras model. If not provided, the Keras outputs are named to [output1, output2, …, outputN] in the Core ML model. When multiple outputs are present, output feature names are in the same order as the Keras inputs.
- image_input_names: [str] | str
Input names to the Keras model (a subset of the input_names parameter) that can be treated as images by Core ML. All other inputs are treated as MultiArrays (N-D Arrays).
- is_bgr: bool | dict()
Flag indicating the channel order the model internally uses to represent color images. Set to True if the internal channel order is BGR, otherwise it will be assumed RGB. This flag is applicable only if image_input_names is specified. To specify a different value for each image input, provide a dictionary with input names as keys. Note that this flag is about the models internal channel order. An input image can be passed to the model in any color pixel layout containing red, green and blue values (e.g. 32BGRA or 32ARGB). This flag determines how those pixel values get mapped to the internal multiarray representation.
- red_bias: float | dict()
Bias value to be added to the red channel of the input image. Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- blue_bias: float | dict()
Bias value to be added to the blue channel of the input image. Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- green_bias: float | dict()
Bias value to be added to the green channel of the input image. Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- gray_bias: float | dict()
Bias value to be added to the input image (in grayscale). Defaults to 0.0 Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- image_scale: float | dict()
Value by which input images will be scaled before bias is added and Core ML model makes a prediction. Defaults to 1.0. Applicable only if image_input_names is specified. To specify different values for each image input provide a dictionary with input names as keys.
- class_labels: list[int or str] | str
Class labels (applies to classifiers only) that map the index of the output of a neural network to labels in a classifier.
If the provided class_labels is a string, it is assumed to be a filepath where classes are parsed as a list of newline separated strings.
- predicted_feature_name: str
Name of the output feature for the class labels exposed in the Core ML model (applies to classifiers only). Defaults to ‘classLabel’
- model_precision: str
Precision at which model will be saved. Currently full precision (float) and half precision (float16) models are supported. Defaults to ‘_MLMODEL_FULL_PRECISION’ (full precision).
- predicted_probabilities_output: str
Name of the neural network output to be interpreted as the predicted probabilities of the resulting classes. Typically the output of a softmax function. Defaults to the first output blob.
- add_custom_layers: bool
If yes, then unknown Keras layer types will be added to the model as ‘custom’ layers, which must then be filled in as postprocessing.
- custom_conversion_functions: {str:(Layer -> (dict, [weights])) }
A dictionary with keys corresponding to names of custom layers and values as functions taking a Keras custom layer and returning a parameter dictionary and list of weights.
- respect_trainable: bool
If yes, then Keras layers marked ‘trainable’ will automatically be marked updatable in the Core ML model.
- use_float_arraytype: bool
If true, the datatype of input/output multiarrays is set to Float32 instead of double.
- Returns
- model: MLModel
- Model in Core ML format.
Examples
# Make a Keras model >>> model = Sequential() >>> model.add(Dense(num_channels, input_dim = input_dim)) # Convert it with default input and output names >>> import coremltools >>> coreml_model = coremltools.converters.keras.convert(model) # Saving the Core ML model to a file. >>> coreml_model.save('my_model.mlmodel')
Converting a model with a single image input.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = ... 'image', image_input_names = 'image')
Core ML also lets you add class labels to models to expose them as classifiers.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = 'image', ... image_input_names = 'image', class_labels = ['cat', 'dog', 'rat'])
Class labels for classifiers can also come from a file on disk.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = ... 'image', image_input_names = 'image', class_labels = 'labels.txt')
Provide customized input and output names to the Keras inputs and outputs while exposing them to Core ML.
>>> coreml_model = coremltools.converters.keras.convert(model, input_names = ... ['my_input_1', 'my_input_2'], output_names = ['my_output'])
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coremltools.converters.keras._keras2_converter._convert_training_info(model, builder, output_features)¶ Convert the training information from the given Keras ‘model’ into the Core ML in ‘builder’.
- Parameters
model – keras.model.Sequential The source Keras model.
builder – NeutralNetworkBuilder The target model that will gain the loss and optimizer.
output_features – list of tuples, (str, datatype) The set of tensor names that are output from the layers in the Keras model.
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coremltools.converters.keras._keras2_converter._get_layer_converter_fn(layer, add_custom_layers=False)¶ Get the right converter function for Keras
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coremltools.converters.keras._keras2_converter._load_keras_model(model_network_path, model_weight_path, custom_objects=None)¶ Load a keras model from disk
- Parameters
- model_network_path: str
Path where the model network path is (json file)
- model_weight_path: str
Path where the model network weights are (hd5 file)
- custom_objects:
A dictionary of layers or other custom classes or functions used by the model
- Returns
- model: A keras model