def convert_lambda(layer):
if layer.function == modellib.lambda_a:
params = NeuralNetwork_pb2.CustomLayerParams()
params.className = 'lambda_a'
elif layer.function == modellib.lambda_b:
params = NeuralNetwork_pb2.CustomLayerParams()
params.className = 'lambda_b'
elif layer.function == modellib.lambda_c:
params = NeuralNetwork_pb2.CustomLayerParams()
params.className = 'lambda_c'
elif layer.function == modellib.lambda_d:
params = NeuralNetwork_pb2.CustomLayerParams()
params.className = 'lambda_d'
else:
params = None
return params
def convert_lambda(layer):
if layer.function == scaling:
params = NeuralNetwork_pb2.CustomLayerParams()
params.className = "scaling"
params.parameters["scale"].doubleValue = layer.arguments['scale']
return params
else:
return None
def convert_coreml(layer):
params = NeuralNetwork_pb2.CustomLayerParams()
params.className = 'ResizeLayer'
params.description = 'Perform nearest neighbour / bilinear resizing of input tensors'
layer_config = layer.get_config()
params.parameters['bilinear'].intValue = int(layer_config['bilinear'])
params.parameters['new_height'].intValue = layer_config['new_height']
params.parameters['new_width'].intValue = layer_config['new_width']
return params
def convert_FullSizePReLU(keras_layer):
coreml_layer = NeuralNetwork_pb2.CustomLayerParams()
coreml_layer.className = className_FullSizePReLU
coreml_layer.description = 'Custom activation layer: ' + className_FullSizePReLU
weightList = keras_layer.get_weights()
p_alpha = weightList[0] # numpy array
alpha = coreml_layer.weights.add()
alpha.floatValue.extend(map(float, p_alpha.flatten()))
return coreml_layer
def convert_swish(layer):
params = NeuralNetwork_pb2.CustomLayerParams()
# The name of the Swift or Obj-C class that implements this layer.
params.className = "Swish"
# The desciption is shown in Xcode's mlmodel viewer.
params.description = "A fancy new activation function"
# Set configuration parameters
params.parameters["beta"].doubleValue = layer.beta
return params
def convert_learnable_swish(layer):
params = NeuralNetwork_pb2.CustomLayerParams()
# The name of the Swift or Obj-C class that implements this layer.
params.className = "Swish"
# The desciption is shown in Xcode's mlmodel viewer.
params.description = "A fancy new activation function"
# Add the weights
beta_weights = params.weights.add()
beta_weights.floatValue.extend(layer.get_weights()[0].astype(float))
return params
def convert_SynapticTransmissionRegulator(keras_layer):
coreml_layer = NeuralNetwork_pb2.CustomLayerParams()
coreml_layer.className = className_SynapticTransmissionRegulator
coreml_layer.description = 'Custom Synaptic Transmission Regulator (STR) layer: ' + className_SynapticTransmissionRegulator
weightList = keras_layer.get_weights()
p_weight = weightList[0] # numpy array
p_bias = weightList[1] # numpy array
weight = coreml_layer.weights.add()
weight.floatValue.extend(map(float, p_weight.flatten()))
bias = coreml_layer.weights.add()
bias.floatValue.extend(map(float, p_bias.flatten()))
return coreml_layer