def set_output_characteristics(self, nextLayer):
"""Sets the output characteristics based on the next layer"""
if nextLayer:
self.layer.ell_outputPaddingParameters = nextLayer.layer.ell_inputPaddingParameters
self.layer.ell_outputShape = utilities.get_adjusted_shape(
self.layer.output.shape, self.layer.ell_outputPaddingParameters)
self.layer.ell_outputShapeMinusPadding = utilities.get_shape(
self.layer.output.shape)
else:
# last layer
self.layer.ell_outputPaddingParameters = ell.NoPadding()
self.layer.ell_outputShape = utilities.get_adjusted_shape(
self.layer.output.shape, ell.NoPadding())
self.layer.ell_outputShapeMinusPadding = self.layer.ell_outputShape
def get_predictor(self, layer):
ell_layers = []
# remove output_padding from because CNTK doesn't have output padding.
layer.layer.ell_outputPaddingParameters = ell.neural.PaddingParameters(
ell.neural.PaddingScheme.zeros, 0)
layer.layer.ell_outputShape = cntk_utilities.get_adjusted_shape(
layer.layer.output.shape, layer.layer.ell_outputPaddingParameters)
layer.process(ell_layers)
# Create an ELL neural network predictor from the relevant CNTK layers
return ell.neural.NeuralNetworkPredictor(ell_layers)
def __init__(self, layer):
self.layer = layer
self.layer.ell_inputPaddingParameters = self.get_input_padding_parameters()
self.additional_layer_text = None
if not hasattr(self, 'input_shape'):
if (len(self.layer.arguments) > 0 and len(self.layer.arguments[0].shape) > 0):
self.input_shape = self.layer.arguments[0].shape
# else, assume derived classes have already initialized the input shape
if hasattr(self, 'input_shape'):
self.layer.ell_inputShape = utilities.get_adjusted_shape(
self.input_shape, self.layer.ell_inputPaddingParameters)
else:
raise RuntimeError(
"Could not initialize input_shape") # coding error
