I’m having an issue when I try to load a model I just saved, let me show you the code.

I loaded a pretrained model

pretrained = tf.keras.applications.ResNet50(include_top = False, 
                                      input_shape = (224,224,3), 
                                      pooling = 'avg',
                                      classes = 525,
                                      weights = 'imagenet' 
                                           )

for layer in pretrained.layers: #configurar capas para no entrenar
    layer.trainable = False

I creat my model based on the pretrained model

model = keras.Sequential()


model.add(pretrained) 
model.add(keras.layers.Dense(512, activation = 'relu', name = 'capa_1'))
model.add(keras.layers.Dense(525, activation = 'softmax', name = 'capa_de_salida'))

I train the model and get a 90% accuracy

opt = keras.optimizers.Adam(learning_rate = 5.5e-05)
model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])

history = model.fit(train_dataset,
               epochs = 10,
               validation_data = val_dor)

but once I save it and try to load it, it wont work

path_model = './ResNet50_trans_learning_final.keras'
model.save(path_model)



modelo2 = tf.keras.models.load_model(path_model, compile=False)

I get this

--------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[102], line 5
      1 model.save('ResNet50_trans_learning_final.keras')
      3 path_model = './ResNet50_trans_learning_final.keras'
----> 5 modelo2 = tf.keras.models.load_model(path_model, compile=False)
      6 # hay un problema causado por la capa flaten y nose pude leer

File /usr/local/lib/python3.11/dist-packages/keras/src/saving/saving_api.py:176, in load_model(filepath, custom_objects, compile, safe_mode)
    173         is_keras_zip = True
    175 if is_keras_zip:
--> 176     return saving_lib.load_model(
    177         filepath,
    178         custom_objects=custom_objects,
    179         compile=compile,
    180         safe_mode=safe_mode,
    181     )
    182 if str(filepath).endswith((".h5", ".hdf5")):
    183     return legacy_h5_format.load_model_from_hdf5(filepath)

File /usr/local/lib/python3.11/dist-packages/keras/src/saving/saving_lib.py:155, in load_model(filepath, custom_objects, compile, safe_mode)
    153 # Construct the model from the configuration file in the archive.
    154 with ObjectSharingScope():
--> 155     model = deserialize_keras_object(
    156         config_dict, custom_objects, safe_mode=safe_mode
    157     )
    159 all_filenames = zf.namelist()
    160 if _VARS_FNAME + ".h5" in all_filenames:

File /usr/local/lib/python3.11/dist-packages/keras/src/saving/serialization_lib.py:711, in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)
    709 with custom_obj_scope, safe_mode_scope:
    710     try:
--> 711         instance = cls.from_config(inner_config)
    712     except TypeError as e:
    713         raise TypeError(
    714             f"{cls} could not be deserialized properly. Please"
    715             " ensure that components that are Python object"
   (...)
    719             f"nnconfig={config}.nnException encountered: {e}"
    720         )

File /usr/local/lib/python3.11/dist-packages/keras/src/models/sequential.py:336, in Sequential.from_config(cls, config, custom_objects)
    331     else:
    332         layer = serialization_lib.deserialize_keras_object(
    333             layer_config,
    334             custom_objects=custom_objects,
    335         )
--> 336     model.add(layer)
    337 if (
    338     not model._functional
    339     and build_input_shape
    340     and isinstance(build_input_shape, (tuple, list))
    341 ):
    342     model.build(build_input_shape)

File /usr/local/lib/python3.11/dist-packages/keras/src/models/sequential.py:117, in Sequential.add(self, layer, rebuild)
    115 self._layers.append(layer)
    116 if rebuild:
--> 117     self._maybe_rebuild()
    118 else:
    119     self.built = False

File /usr/local/lib/python3.11/dist-packages/keras/src/models/sequential.py:136, in Sequential._maybe_rebuild(self)
    134 if isinstance(self._layers[0], InputLayer) and len(self._layers) > 1:
    135     input_shape = self._layers[0].batch_shape
--> 136     self.build(input_shape)

File /usr/local/lib/python3.11/dist-packages/keras/src/layers/layer.py:224, in Layer.__new__.<locals>.build_wrapper(*args, **kwargs)
    221 @wraps(original_build_method)
    222 def build_wrapper(*args, **kwargs):
    223     with backend.name_scope(obj.name, caller=obj):
--> 224         original_build_method(*args, **kwargs)
    225     # Record build config.
    226     signature = inspect.signature(original_build_method)

File /usr/local/lib/python3.11/dist-packages/keras/src/models/sequential.py:177, in Sequential.build(self, input_shape)
    175 for layer in self._layers[1:]:
    176     try:
--> 177         x = layer(x)
    178     except NotImplementedError:
    179         # Can happen if shape inference is not implemented.
    180         # TODO: consider reverting inbound nodes on layers processed.
    181         return

File /usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py:123, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    120     filtered_tb = _process_traceback_frames(e.__traceback__)
    121     # To get the full stack trace, call:
    122     # `keras.config.disable_traceback_filtering()`
--> 123     raise e.with_traceback(filtered_tb) from None
    124 finally:
    125     del filtered_tb

File /usr/local/lib/python3.11/dist-packages/keras/src/layers/input_spec.py:202, in assert_input_compatibility(input_spec, inputs, layer_name)
    200 if spec.min_ndim is not None:
    201     if ndim is not None and ndim < spec.min_ndim:
--> 202         raise ValueError(
    203             f'Input {input_index} of layer "{layer_name}" '
    204             "is incompatible with the layer: "
    205             f"expected min_ndim={spec.min_ndim}, "
    206             f"found ndim={ndim}. "
    207             f"Full shape received: {shape}"
    208         )
    209 # Check dtype.
    210 if spec.dtype is not None:

ValueError: Input 0 of layer "capa_de_salida" is incompatible with the layer: expected min_ndim=2, found ndim=1. Full shape received: (512,)```


I'm using this docker container, which comes with tensorflow 2.16.1

sudo docker run -it –rm -v ./:/tf/notebooks -p 8888:8888 –runtime=nvidia tensorflow/tensorflow:latest-gpu-jupyter