Python map_and_batch_dataset示例

示例#1

文件： batching.py 项目： DjangoPeng/tensorflow

 def _as_variant_tensor(self):
   # pylint: disable=protected-access
   input_resource = self._input_dataset._as_variant_tensor()
   return gen_dataset_ops.map_and_batch_dataset(
       input_resource,
       self._map_func.captured_inputs,
       f=self._map_func,
       batch_size=self._batch_size,
       num_parallel_batches=self._num_parallel_batches,
       output_types=nest.flatten(self.output_types),
       output_shapes=nest.flatten(self.output_shapes))

示例#2

 def _as_variant_tensor(self):
     # pylint: disable=protected-access
     input_resource = self._input_dataset._as_variant_tensor()
     return gen_dataset_ops.map_and_batch_dataset(
         input_resource,
         self._map_func.captured_inputs,
         f=self._map_func,
         batch_size=self._batch_size,
         num_parallel_batches=self._num_parallel_batches,
         output_types=nest.flatten(self.output_types),
         output_shapes=nest.flatten(self.output_shapes))

示例#3

文件： batching.py 项目： kimr843/tensorflow

 def _as_variant_tensor(self):
   # pylint: disable=protected-access
   input_resource = self._input_dataset._as_variant_tensor()
   return gen_dataset_ops.map_and_batch_dataset(
       input_resource,
       self._map_func.captured_inputs,
       f=self._map_func,
       batch_size=self._batch_size_t,
       num_parallel_batches=self._num_parallel_batches_t,
       drop_remainder=self._drop_remainder_t,
       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)))

示例#4

 def _as_variant_tensor(self):
   # pylint: disable=protected-access
   input_resource = self._input_dataset._as_variant_tensor()
   return gen_dataset_ops.map_and_batch_dataset(
       input_resource,
       self._map_func.captured_inputs,
       f=self._map_func,
       batch_size=self._batch_size_t,
       num_parallel_batches=self._num_parallel_batches_t,
       drop_remainder=self._drop_remainder_t,
       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)))