(self)
return tf.concat([pianorolls, masks], axis=3)
Returns concatenates masked out pianorolls with their masks.
Returns concatenates masked out pianorolls with their masks.
Returns concatenates masked out pianorolls with their masks .
def get_convnet_input(self):
    """Returns concatenates masked out pianorolls with their masks."""
    # pianorolls, masks = self.inputs['pianorolls'], self.inputs[
    #     'masks']
    pianorolls, masks = self.pianorolls, self.masks
    pianorolls *= 1. - masks
    if self.hparams.mask_indicates_context:
      # flip meaning of mask for convnet purposes: after flipping, mask is hot
      # where values are known. this makes more sense in light of padding done
      # by convolution operations: the padded area will have zero mask,
      # indicating no information to rely on.
      masks = 1. - masks
    return tf.concat([pianorolls, masks], axis=3)
def get_convnet_input ( self ) : # pianorolls, masks = self.inputs['pianorolls'], self.inputs[ #     'masks'] pianorolls , masks = self . pianorolls , self . masks pianorolls *= 1. - masks if self . hparams . mask_indicates_context : # flip meaning of mask for convnet purposes: after flipping, mask is hot # where values are known. this makes more sense in light of padding done # by convolution operations: the padded area will have zero mask, # indicating no information to rely on. masks = 1. - masks return tf . concat ( [ pianorolls , masks ] , axis = 3 )