Unable to deserialize Sequential model from config

[shkarupa-alex]

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 gpu
• Exact command to reproduce: see below
• Do you want to contribute a PR? (yes/no): no

Describe the problem.

Unable to deserialize Sequential model from config.

Describe the current behavior.

Got exception during deserialization.

Describe the expected behavior.

As far as Sequential model is a subtype of regular Model it should be sirializable and deserializable

Standalone code to reproduce the issue.

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Conv2D(32, 3, padding='same', name='conv'),
    layers.BatchNormalization(name='bn'),   
])
_ = model(tf.zeros([1, 16, 16, 3]))  # aka "build"

model2 = models.Model.from_config(model.get_config())

Source code / logs.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
File ~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/keras/src/engine/training.py:3244, in Model.from_config(cls, config, custom_objects)
   3243 try:
-> 3244     model = cls(**config)
   3245 except TypeError as e:

File ~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/tensorflow/python/trackable/base.py:204, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
    203 try:
--> 204   result = method(self, *args, **kwargs)
    205 finally:

File ~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:

File ~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/keras/src/utils/generic_utils.py:514, in validate_kwargs(kwargs, allowed_kwargs, error_message)
    513 if kwarg not in allowed_kwargs:
--> 514     raise TypeError(error_message, kwarg)

TypeError: ('Keyword argument not understood:', 'layers')

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
Cell In[3], line 13
      9 _ = model(tf.zeros([1, 16, 16, 3]))
     11 # print([w.name for w in model.weights])
---> 13 model2 = models.Model.from_config(model.get_config())
     14 # [w.name for w in model2.weights]

File ~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/keras/src/engine/training.py:3246, in Model.from_config(cls, config, custom_objects)
   3244         model = cls(**config)
   3245     except TypeError as e:
-> 3246         raise TypeError(
   3247             "Unable to revive model from config. When overriding "
   3248             "the `get_config()` method, make sure that the "
   3249             "returned config contains all items used as arguments "
   3250             f"in the  constructor to {cls}, "
   3251             "which is the default behavior. "
   3252             "You can override this default behavior by defining a "
   3253             "`from_config(cls, config)` class method to specify "
   3254             "how to create an "
   3255             f"instance of {cls.__name__} from its config.\n\n"
   3256             f"Received config={config}\n\n"
   3257             f"Error encountered during deserialization: {e}"
   3258         )
   3259 return model

TypeError: Unable to revive model from config. When overriding the `get_config()` method, make sure that the returned config contains all items used as arguments in the  constructor to <class 'keras.src.engine.training.Model'>, which is the default behavior. You can override this default behavior by defining a `from_config(cls, config)` class method to specify how to create an instance of Model from its config.

Received config={'name': 'sequential_1', 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (1, 16, 16, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'conv_input'}, 'registered_name': None}, {'module': 'keras.layers', 'class_name': 'Conv2D', 'config': {'name': 'conv', 'trainable': True, 'dtype': 'float32', 'filters': 32, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'groups': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (1, 16, 16, 3)}}, {'module': 'keras.layers', 'class_name': 'BatchNormalization', 'config': {'name': 'bn', 'trainable': True, 'dtype': 'float32', 'axis': [3], 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'gamma_initializer': {'module': 'keras.initializers', 'class_name': 'Ones', 'config': {}, 'registered_name': None}, 'moving_mean_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'moving_variance_initializer': {'module': 'keras.initializers', 'class_name': 'Ones', 'config': {}, 'registered_name': None}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (1, 16, 16, 32)}}]}

Error encountered during deserialization: ('Keyword argument not understood:', 'layers')

[tilakrayal]

@shkarupa-alex,
Could you please confirm whether you are facing the similar issue which was suggested in another issue.
#414
If it is the same, the issue is assigned to developer and the team is working on the same. Thank you!

[shkarupa-alex]

@tilakrayal , i'm not sure if these issues related.
There is no any subclassing in my case and code example.