Exemplos de parallel_interleave_dataset em Python

Exemplo n.º 1

 def _as_variant_tensor(self):
     return gen_dataset_ops.parallel_interleave_dataset(
         self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
         self._map_func.captured_inputs,
         self._cycle_length,
         self._block_length,
         self._sloppy,
         f=self._map_func,
         output_types=nest.flatten(self.output_types),
         output_shapes=nest.flatten(self.output_shapes))

Exemplo n.º 2

Arquivo: interleave_ops.py Projeto: dyoung418/tensorflow

 def _as_variant_tensor(self):
   return gen_dataset_ops.parallel_interleave_dataset(
       self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
       self._map_func.captured_inputs,
       self._cycle_length,
       self._block_length,
       self._sloppy,
       f=self._map_func,
       output_types=nest.flatten(self.output_types),
       output_shapes=nest.flatten(self.output_shapes))

Exemplo n.º 3

 def _as_variant_tensor(self):
     # pylint: disable=protected-access
     return gen_dataset_ops.parallel_interleave_dataset(
         self._input_dataset._as_variant_tensor(),
         self._map_func.captured_inputs,
         self._cycle_length,
         self._block_length,
         self._sloppy,
         self._buffer_output_elements,
         self._prefetch_input_elements,
         f=self._map_func,
         **dataset_ops.flat_structure(self))

Exemplo n.º 4

Arquivo: readers.py Projeto: ThunderQi/tensorflow

 def _as_variant_tensor(self):
   # pylint: disable=protected-access
   return gen_dataset_ops.parallel_interleave_dataset(
       self._input_dataset._as_variant_tensor(),
       self._map_func.captured_inputs,
       self._cycle_length,
       self._block_length,
       self._sloppy,
       self._buffer_output_elements,
       self._prefetch_input_elements,
       f=self._map_func,
       **dataset_ops.flat_structure(self))

Exemplo n.º 5

 def _as_variant_tensor(self):
   return gen_dataset_ops.parallel_interleave_dataset(
       self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
       self._map_func.captured_inputs,
       self._cycle_length,
       self._block_length,
       self._sloppy,
       self._buffer_output_elements,
       self._prefetch_input_elements,
       f=self._map_func,
       output_types=nest.flatten(
           sparse.as_dense_types(self.output_types, self.output_classes)),
       output_shapes=nest.flatten(
           sparse.as_dense_shapes(self.output_shapes, self.output_classes)))