parameters: (model, blob_in, blob_out, num_channels=1, slope_init=None,
            **kwargs)
return_statement: return model.net.PRelu([blob_in, slope], [blob_out])
docstring: PRelu
docstring_summary: PRelu
docstring_tokens: PRelu
function: def prelu(model, blob_in, blob_out, num_channels=1, slope_init=None,
            **kwargs):
      """PRelu"""
      slope_init = (
          slope_init if slope_init else ('ConstantFill', {'value': 0.25}))
      if model.init_params:
          slope = model.param_init_net.__getattr__(slope_init[0])(
              [],
              blob_out + '_slope',
              shape=[num_channels],
              **slope_init[1]
          )
      else:
          slope = core.ScopedBlobReference(
              blob_out + '_slope', model.param_init_net)
  
      model.AddParameter(slope)
  
      return model.net.PRelu([blob_in, slope], [blob_out])
function_tokens: def prelu ( model , blob_in , blob_out , num_channels = 1 , slope_init = None , * * kwargs ) : slope_init = ( slope_init if slope_init else ( 'ConstantFill' , { 'value' : 0.25 } ) ) if model . init_params : slope = model . param_init_net . __getattr__ ( slope_init [ 0 ] ) ( [ ] , blob_out + '_slope' , shape = [ num_channels ] , * * slope_init [ 1 ] ) else : slope = core . ScopedBlobReference ( blob_out + '_slope' , model . param_init_net ) model . AddParameter ( slope ) return model . net . PRelu ( [ blob_in , slope ] , [ blob_out ] )