Open grasskin opened 1 year ago
Hi @grasskin -
I am able to reproduce the issue when dtype=uint8
in layers.Rescaling(1 / 255, dtype="uint8")
.
Error Traceback:
ValueError Traceback (most recent call last)
[<ipython-input-21-4470e15678cc>](https://localhost:8080/#) in <cell line: 2>()
1 # Baseline supervised training with random initialization
----> 2 baseline_model = keras.Sequential(
3 [
4 get_augmenter(**classification_augmentation),
5 get_encoder(),
2 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py](https://localhost:8080/#) in input_shape(self)
269 if self._functional:
270 return self._functional.input_shape
--> 271 raise ValueError(
272 f"Sequential model '{self.name}' has no defined input shape yet."
273 )
ValueError: Sequential model 'sequential_24' has no defined input shape yet.
Also while visualizing image using dtype=uint8
then augmented images are not properly visualize.
Attached gist for the reference. We look into the issue more and update.
Backwards incompatibility from semisupervised_simclr.py
Input image is
tf.Tensor(shape=(8, 96, 96, 3), dtype=uint8)
and is unable to multiply by float tensor in rescaling layer.Current workaround is to add dtype via
layers.Rescaling(1 / 255, dtype="uint8")
in line 217.I assume keras used to detect that input was uint8 which is why dtype was not specified.