Wrong weight names after deserializing model from config
Opened this issue · 5 comments
System information.
- Have I written custom code (as opposed to using a stock example script provided in Keras): no
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOS 13.4.1
- TensorFlow installed from (source or binary): binary
- TensorFlow version (use command below): v2.13.0-rc2-7-g1cb1a030a62 2.13.0
- Python version: 3.11
- GPU model and memory: no
- Do you want to contribute a PR? (yes/no): no
Describe the problem.
Models with custom layers miss their weight names after restoring from config
Describe the current behavior.
See example below. When replacing BatchNormalization with it's successor (the only thing changes - core vs custom class) after restoring from config we got wrong weight names.
Describe the expected behavior.
Weight names of custom layers should be preserved just like built-in ones.
Standalone code to reproduce the issue.
import tensorflow as tf
from keras import layers, models
from keras.saving import register_keras_serializable
inputs = layers.Input(shape=[None, None, 3], dtype='float32')
x = layers.Conv2D(32, 3, padding='same', name='conv')(inputs)
x = layers.BatchNormalization(name='bn')(x) # !!! <-- core class
model = models.Model(inputs=inputs, outputs=x)
print([w.name for w in model.weights])
model2 = models.Model.from_config(model.get_config())
print([w.name for w in model2.weights])
# ========= Failure case
@register_keras_serializable(package='MyPackage>Normalization')
class CustomBatchNormalization(layers.BatchNormalization):
pass
inputs = layers.Input(shape=[None, None, 3], dtype='float32')
x = layers.Conv2D(32, 3, padding='same', name='conv')(inputs)
x = CustomBatchNormalization(name='cbn')(x) # !!! <-- custom class
model = models.Model(inputs=inputs, outputs=x)
print([w.name for w in model.weights])
model2 = models.Model.from_config(model.get_config())
print([w.name for w in model2.weights])Source code / logs.
Code posted above will print:
['conv/kernel:0', 'conv/bias:0', 'bn/gamma:0', 'bn/beta:0', 'bn/moving_mean:0', 'bn/moving_variance:0']
['conv/kernel:0', 'conv/bias:0', 'bn/gamma:0', 'bn/beta:0', 'bn/moving_mean:0', 'bn/moving_variance:0']
['conv/kernel:0', 'conv/bias:0', 'cbn/gamma:0', 'cbn/beta:0', 'cbn/moving_mean:0', 'cbn/moving_variance:0']
['conv/kernel:0', 'conv/bias:0', 'gamma:0', 'beta:0', 'moving_mean:0', 'moving_variance:0']
@shkarupa-alex,
Thank you for reporting. We are investigating the mentioned issue and will provide the resolution and cause for the issue.
Thank you!
Issue persist with TF 2.15.0
@shkarupa-alex,
I tried to execute the mentioned code with the Keras3.0 version and was able to get the same output for the weight names. Kindly find the gist of it here.
['kernel', 'bias', 'gamma', 'beta', 'moving_mean', 'moving_variance']
['kernel', 'bias', 'gamma', 'beta', 'moving_mean', 'moving_variance']
['kernel', 'bias', 'gamma', 'beta', 'moving_mean', 'moving_variance']
['kernel', 'bias', 'gamma', 'beta', 'moving_mean', 'moving_variance']Thank you!
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Issue is still here