[Python] Layer count mismatch when loading weights from file. Model expected 106 layers, found...

I have this network (from SimClR paper):

base_model = tf.keras.applications.ResNet50(include_top=False, weights=None, input_shape=(224, 224, 3))
base_model.trainable = True
#inputs = Input((224, 224, 3))
inputs = Input((224, 224, 3))
h = base_model(inputs, training=True)
h = GlobalAveragePooling2D()(h)

projection_1 = Dense(256)(h)
projection_1 = Activation("relu")(projection_1)
projection_2 = Dense(128)(projection_1)
projection_2 = Activation("relu")(projection_2)
projection_3 = Dense(50)(projection_2)

resnet_simclr = Model(inputs, projection_3)


thane after training the model, I saved it:

resnet_simclr.save_weights(filename)


However, after trying to load the weight in ResNet50 like this:

inputs = tf.keras.layers.Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3), name="input_image")
Resmodel = tf.keras.applications.ResNet50(input_tensor=inputs, weights=weights_path, include_top=False)


I got this error:

---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-92-cb642379c2ae> in <module>()
3 from keras.utils.vis_utils import plot_model
4 inputs = tf.keras.layers.Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3), name="input_image")
----> 5 Resmodel = tf.keras.applications.ResNet50(input_tensor=inputs, weights=weights_path, include_top=False) # weights="imagenet"
6 #Resmodel.load_weights(weights_path)
7 Resmodel.summary()

3 frames
/usr/local/lib/python3.7/dist-packages/keras/saving/hdf5_format.py in load_weights_from_hdf5_group(f, model)
718 if len(layer_names) != len(filtered_layers):
719 raise ValueError(
--> 720 f'Layer count mismatch when loading weights from file. '
721 f'Model expected {len(filtered_layers)} layers, found '
722 f'{len(layer_names)} saved layers.')

ValueError: Layer count mismatch when loading weights from file. Model expected 106 layers, found 4 saved layers.


EDIT

The issue I'm trying to do transfer learning (down stream) from RestNet50 with have also 3 projection layer to RestNet50 ( part of Unet backbone) but this Rasnet do not have project head like the trained model. I'm not sure how to fix that.

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