(self)
Generator that can be used to iterate over this dataset.

        Yields:
            tuple : the next minibatch of data.

        Note:
            The second element of the tuple is itself a tuple (t,m) with:
                t: the actual target as generated by the task object
                m: the output mask to account for the difference between
                    the seq_length for this minibatch and the max seq_len,
                    which is also the number of columns in X,t, and m
Generator that can be used to iterate over this dataset.
Generator that can be used to iterate over this dataset .
def __iter__(self):
        """
        Generator that can be used to iterate over this dataset.

        Yields:
            tuple : the next minibatch of data.

        Note:
            The second element of the tuple is itself a tuple (t,m) with:
                t: the actual target as generated by the task object
                m: the output mask to account for the difference between
                    the seq_length for this minibatch and the max seq_len,
                    which is also the number of columns in X,t, and m
        """

        self.batch_index = 0

        while self.batch_index < self.nbatches:

            # The task object writes minibatch data into buffers we pass it
            self.task.synthesize(self.dev_X, self.dev_y, self.mask)

            self.batch_index += 1

            yield self.dev_X, (self.dev_y, self.mask)
def __iter__ ( self ) : self . batch_index = 0 while self . batch_index < self . nbatches : # The task object writes minibatch data into buffers we pass it self . task . synthesize ( self . dev_X , self . dev_y , self . mask ) self . batch_index += 1 yield self . dev_X , ( self . dev_y , self . mask )