keras-team/keras · error · ValueError

Unable to serialize to JSON, because the TypeSpec class has…

Error message

Unable to serialize {obj} to JSON, because the TypeSpec class {type(obj)} has not been registered.

What it means

When saving a Keras model to JSON, get_json_type serializes TensorFlow TypeSpec objects (e.g. TensorSpec in an input signature) by looking up a registered serialization name. If the concrete TypeSpec class has no registration, the lookup raises ValueError and this wrapper error names the object and type that failed.

Solutions

1. Save with the native format: model.save('model.keras') instead of to_json()
2. Align keras and tensorflow versions so the spec class is registered
3. For custom TypeSpecs, register them with TF's TypeSpec serialization registry

Example fix

# before
json_config = model.to_json()
# after
model.save('model.keras')  # native format, no JSON type registry

spec = tf.type_spec_from_value(x)
from tensorflow.python.saved_model import nested_structure_coder
assert spec.__class__ in nested_structure_coder._TYPE_SPEC_TO_CODEC  # crude registry check

try:
    model.to_json()
except ValueError:
    model.save('model.keras')  # fallback to native format

When it happens

Trigger: model.to_json() (or any legacy JSON save path) on a model whose config contains an unregistered tf.TypeSpec subclass, e.g. custom ragged/sparse tensor specs or specs from a TF version whose registry does not match Keras.

Common situations: Models with ragged or sparse inputs after a TensorFlow/Keras version mismatch; custom input types introduced by an upgrade.

Understand the failure class

Background: "JSON serialization failed", "not JSON serializable", "Failed to serialize": why JSON marshaling errors happen and how to fix them — this error's family across 46 libraries.

Related errors

• Data not JSON Serializable
• Targets not JSON Serializable
• Unable to serialize to JSON. Unrecognized type .
• A Sequential model configuration must be a dictionary…
• Cannot do batch_dot on inputs with tf.shapes

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/05fac86b6be40a6b. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/legacy/saving/json_utils.py:197

    if obj is Ellipsis:
        return {"class_name": "__ellipsis__"}

    # if isinstance(obj, wrapt.ObjectProxy):
    #     return obj.__wrapped__

    if tf.available and isinstance(obj, tf.TypeSpec):
        from tensorflow.python.framework import type_spec_registry

        try:
            type_spec_name = type_spec_registry.get_name(type(obj))
            return {
                "class_name": "TypeSpec",
                "type_spec": type_spec_name,
                "serialized": obj._serialize(),
            }
        except ValueError:
            raise ValueError(
                f"Unable to serialize {obj} to JSON, because the TypeSpec "
                f"class {type(obj)} has not been registered."
            )
    if tf.available and isinstance(obj, tf.__internal__.CompositeTensor):
        spec = tf.type_spec_from_value(obj)
        tensors = []
        for tensor in tf.nest.flatten(obj, expand_composites=True):
            tensors.append((tensor.dtype.name, tensor.numpy().tolist()))
        return {
            "class_name": "CompositeTensor",
            "spec": get_json_type(spec),
            "tensors": tensors,
        }

    if isinstance(obj, enum.Enum):
        return obj.value

    if isinstance(obj, bytes):

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