parameters: (self, blobs)
docstring: Batch blob lists according to net's batch size.
  
      Parameters
      ----------
      blobs: Keys blob names and values are lists of blobs (of any length).
             Naturally, all the lists should have the same length.
  
      Yields
      ------
      batch: {blob name: list of blobs} dict for a single batch.
docstring_summary: Batch blob lists according to net's batch size.
docstring_tokens: Batch blob lists according to net s batch size .
function: def _Net_batch(self, blobs):
      """
      Batch blob lists according to net's batch size.
  
      Parameters
      ----------
      blobs: Keys blob names and values are lists of blobs (of any length).
             Naturally, all the lists should have the same length.
  
      Yields
      ------
      batch: {blob name: list of blobs} dict for a single batch.
      """
      num = len(six.next(six.itervalues(blobs)))
      batch_size = six.next(six.itervalues(self.blobs)).shape[0]
      remainder = num % batch_size
      num_batches = num // batch_size
  
      # Yield full batches.
      for b in range(num_batches):
          i = b * batch_size
          yield {name: blobs[name][i:i + batch_size] for name in blobs}
  
      # Yield last padded batch, if any.
      if remainder > 0:
          padded_batch = {}
          for name in blobs:
              padding = np.zeros((batch_size - remainder,)
                                 + blobs[name].shape[1:])
              padded_batch[name] = np.concatenate([blobs[name][-remainder:],
                                                   padding])
          yield padded_batch
function_tokens: def _Net_batch ( self , blobs ) : num = len ( six . next ( six . itervalues ( blobs ) ) ) batch_size = six . next ( six . itervalues ( self . blobs ) ) . shape [ 0 ] remainder = num % batch_size num_batches = num // batch_size # Yield full batches. for b in range ( num_batches ) : i = b * batch_size yield { name : blobs [ name ] [ i : i + batch_size ] for name in blobs } # Yield last padded batch, if any. if remainder > 0 : padded_batch = { } for name in blobs : padding = np . zeros ( ( batch_size - remainder , ) + blobs [ name ] . shape [ 1 : ] ) padded_batch [ name ] = np . concatenate ( [ blobs [ name ] [ - remainder : ] , padding ] ) yield padded_batch