tensorflow shape_error ai_generated true

InvalidArgumentError: Incompatible shapes: [32,10] vs. [32,5]

ID: tensorflow/incompatible-shapes

Also available as: JSON · Markdown
90%Fix Rate
90%Confidence
3Evidence
2023-01-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
2 active — — —

Root Cause

Tensor shapes do not match. Common with loss functions where model output and label dimensions differ.

generic

Workarounds

  1. 92% success Match loss function to label format 
    Integer labels: sparse_categorical_crossentropy. One-hot labels: categorical_crossentropy.

    Sources: https://www.tensorflow.org/api_docs/python/tf

  2. 90% success Verify model output shape matches number of classes 
    model.summary()  # check last layer output shape
  3. 85% success Check dataset label shapes before training 
    print(y_train.shape, y_train[:5])  # verify dimensions and format

Dead Ends

Common approaches that don't work:

  1. Reshape labels to match model output 78% fail

    If model outputs 10 classes but you have 5, reshape just masks the real problem: wrong model or wrong labels

  2. Use sparse_categorical_crossentropy to avoid shape issues 65% fail

    Only works if labels are integer indices, not one-hot. Using it with one-hot labels gives wrong results.