tf_agents.utils.nest_utils.is_batched_nested_tensors

Compares tensors to specs to determine if all tensors are batched or not.

tf_agents.utils.nest_utils.is_batched_nested_tensors(
    tensors,
    specs,
    num_outer_dims=1,
    allow_extra_fields=False,
    check_dtypes=True
)

For each tensor, it checks the dimensions and dtypes with respect to specs.

Returns True if all tensors are batched and False if all tensors are unbatched.

Raises a ValueError if the shapes are incompatible or a mix of batched and unbatched tensors are provided.

Raises a TypeError if tensors' dtypes do not match specs.

tensors Nested list/tuple/dict of Tensors.
specs Nested list/tuple/dict of Tensors or CompositeTensors describing the shape of unbatched tensors.
num_outer_dims The integer number of dimensions that are considered batch dimensions. Default 1.
allow_extra_fields If True, then tensors may have extra subfields which are not in specs. In this case, the extra subfields will not be checked. For example: python tensors = {"a": tf.zeros((3, 4), dtype=tf.float32), "b": tf.zeros((5, 6), dtype=tf.float32)} specs = {"a": tf.TensorSpec(shape=(4,), dtype=tf.float32)} assert is_batched_nested_tensors(tensors, specs, allow_extra_fields=True) The above example would raise a ValueError if allow_extra_fields was False.
check_dtypes If True will validate that tensors and specs have the same dtypes.

True if all Tensors are batched and False if all Tensors are unbatched.

ValueError If

  1. Any of the tensors or specs have shapes with ndims == None, or
  2. The shape of Tensors are not compatible with specs, or
  3. A mix of batched and unbatched tensors are provided.
  4. The tensors are batched but have an incorrect number of outer dims.
TypeError If dtypes between tensors and specs are not compatible.