Cant Serialize and Deserialize ViT in Keras When Setting Classification = True

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Cant Serialize and Deserialize ViT in Keras When Setting Classification = True

Summary

  • Attempts to serialize (save) or deserialize (load) the ViT Keras model fail exclusively when classification=True
  • Models saved with classification=False work correctly
  • Error arises during deserialization when reconstructing the model architecture
  • Common error: AttributeError indicating cls_token is missing upon loading

Root Cause

  • The cls_token weight fails to persist across serialization/deserialization when classification=True
  • Keras doesn’t automatically serialize variables conditionally created in build():
    • cls_token is created dynamically in build() only when self.classification=True
    • During deserialization, Keras reconstructs the model before calling build(), causing missing cls_token references
  • Critical design flaw: build()‘s conditional weight creation conflicts with Keras serialization logic

Why This Happens in Real Systems

  • Conditional architectures (e.g., heads for classification vs. feature extraction) are common in production models
  • Engineers toggle flags like classification for transfer learning workflows
  • Serialization is essential for model deployment (TF Serving, mobile, caching)
  • Model reuse often requires saving/reloading different configurations

Real-World Impact

  • Prevents saving/loading classification-enabled ViT models
  • Blocks model deployment in production environments
  • Hampers experiment tracking/reproducibility
  • Forces workarounds like retraining with classification=False

Example or Code (if applicable)

python
def build(self, input_shape):
if self.classification: # Conditional weight creation
self.cls_token = self.add_weight( # Not captured by serialization
name=”cls_token”,
shape=(1, 1, self.hidden_size),
initializer=”zeros”,
trainable=True,
)
super().build(input_shape)

How Senior Engineers Fix It

  1. Force cls_token creation in __init__:
  2. Refactor weight initialization:
  3. Handle weight restoration:
  4. Add serialization tests:

Corrected Minimal Implementation:

Declare `self.cls_token = None` unconditionally
   Move conditional weight declaration to `__init__`
3. **Override `get_config()`**:  
   Explicitly include `classification`/`cls_token` in config
   Ensure `cls_token` exists before `super().build()` in deserialization
   Validate save/load workflow for both `classification=True/False`

python
def init(self, …, classification=False, kwargs):
super().init(kwargs)

… other init code …

self.classification = classification
self.cls_token = None # Always declare (even if unused)

def build(self, input_shape):
if self.classification and self.cls_token is None:
self.cls_token = self.add_weight(…)
super().build(input_shape)

def get_config(self):
config = super().get_config()
config.update({“classification”: self.classification})
return config

Why Juniors Miss It

  • Underestimate Keras serialization mechanics:
    • Expect variables created in build() to “just work”
    • Confusion about build() vs __init__ responsibilities
  • Focus on functional correctness during training but overlook inference/deployment needs
  • Insufficient testing of save/load pipelines across configurations
  • Misunderstanding conditional architecture implementations