ValueError: Layer count mismatch when loading weights from file. Model expected 241 layers, found - deep-learning

Question

I'm trying to create the base pre-trained model
I did use the following code:
base_model = DenseNet121(weights='/Users/awabe/Desktop/Project/PapilaDB/ClinicalData/densenet121_weights_tf_dim_ordering_tf_kernels.h5', include_top=False)
x = base_model.output
# add a global spatial average pooling layer
x = GlobalAveragePooling2D()(x)
# and a logistic layer
predictions = Dense(len(labels), activation="sigmoid")(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.compile(optimizer='adam', loss=get_weighted_loss(pos_weights, neg_weights))
It gives me an error with message:
`` ValueError Traceback (most recent call last)
Cell In[73], line 2
1 # create the base pre-trained model
----> 2 base_model = DenseNet121(weights='/Users/awabe/Desktop/Project/PapilaDB/ClinicalData/densenet121_weights_tf_dim_ordering_tf_kernels.h5', include_top=False)
4 x = base_model.output
6 # add a global spatial average pooling layer
File /opt/anaconda3/envs/tensorflow/lib/python3.10/site-packages/keras/applications/densenet.py:358, in DenseNet121(include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation)
345 #keras_export(
346 "keras.applications.densenet.DenseNet121", "keras.applications.DenseNet121"
347 )
(...)
355 classifier_activation="softmax",
356 ):
357 """Instantiates the Densenet121 architecture."""
--> 358 return DenseNet(
359 [6, 12, 24, 16],
360 include_top,
361 weights,
362 input_tensor,
363 input_shape,
364 pooling,
365 classes,
366 classifier_activation,
367 )
File /opt/anaconda3/envs/tensorflow/lib/python3.10/site-packages/keras/applications/densenet.py:340, in DenseNet(blocks, include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation)
338 model.load_weights(weights_path)
339 elif weights is not None:
--> 340 model.load_weights(weights)
342 return model
File /opt/anaconda3/envs/tensorflow/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.traceback)
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:
72 del filtered_tb
File /opt/anaconda3/envs/tensorflow/lib/python3.10/site-packages/keras/saving/hdf5_format.py:817, in load_weights_from_hdf5_group(f, model)
815 layer_names = filtered_layer_names
816 if len(layer_names) != len(filtered_layers):
--> 817 raise ValueError(
818 f"Layer count mismatch when loading weights from file. "
819 f"Model expected {len(filtered_layers)} layers, found "
820 f"{len(layer_names)} saved layers."
821 )
823 # We batch weight value assignments in a single backend call
824 # which provides a speedup in TensorFlow.
825 weight_value_tuples = []
ValueError: Layer count mismatch when loading weights from file. Model expected 241 layers, found 242 saved layers. ``