def process(self, ellLayers):
"""Appends the ELL representation of the current layer to ellLayers."""
# Create the ell.LayerParameters for the ELL layer
layerParameters = ell.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters, self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters)
bias = -1.0 * self.layer.constants[0].value
if len(bias.shape) == 0:
biasVector = converters.get_float_vector_from_constant(bias, layerParameters.outputShape.channels)
else:
biasVector = converters.get_float_vector_from_cntk_array(bias)
# Create the ELL bias layer
ellLayers.append(ell.FloatBiasLayer(layerParameters, biasVector))
def process(self, ellLayers):
"""Appends the ELL representation of the current layer to ellLayers."""
# Create the ELL.LayerParameters for the ELL layer
layerParameters = ELL.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters,
self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters)
# Create ELL scaling layer
if (self.scale.value.size == 1):
scalesVector = converters.get_float_vector_from_constant(
self.scale.value, layerParameters.outputShape.channels)
else:
scalesVector = converters.get_float_vector_from_cntk_array(
self.scale.value)
ellLayers.append(ELL.FloatScalingLayer(layerParameters, scalesVector))