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))