I'm currently working on a deep learning project involving image classification with TensorFlow and Keras. Every time I try to run my model.fit() function, I get a 'ValueError: Shapes are incompatible' error. My input images are 28x28 grayscale, and I thought my input_shape was set correctly, but it keeps crashing during the first epoch. Can someone explain how to debug tensor shapes?
3 answers
Try using model.summary(). It shows exactly where the shape changes. If your input is 28x28, your first layer needs input_shape=(28, 28, 1) for grayscale images.
A shape mismatch usually occurs because the output of one layer doesn't match the expected input of the next, or your labels don't match the final layer's activation. If you're doing multiclass classification with 10 classes, ensure your last Dense layer has 10 units and you're using 'sparse_categorical_crossentropy' if your labels are integers. Use model.summary() right before fitting to trace the tensor dimensions through each layer; it is the best way to spot where the "None" or extra dimensions are causing the bottleneck in your pipeline.
Have you checked if your data preprocessing pipeline is accidentally adding a batch dimension twice or flattening the input before it hits the Conv2D layer?
Michael, that's a great point. Often the tf.data.Dataset.batch() method is called twice in a script by mistake. If Sarah is using a custom generator, she should check the yield shape. If the data is (28, 28) but the layer expects (28, 28, 1), the model will fail. Adding a simple tf.expand_dims(x, -1) can often fix grayscale channel issues instantly.
I agree with David. Most beginners forget that TensorFlow expects a 4D tensor for images (batch, height, width, channels). Adding that channel dimension is usually the fix!