Am I moving MobileNetV2 hidden layers correctly?

I am trying to move a couple of hidden layers from the MobileNetV2 and add them to my head of my model and make them trainable in an attempt to increase my accuracy when running a benchmark (CORe50 NICv2 - 391 benchmark).

I wrote my code to do so but running the benchmark and graphing my results it seems that with different hidden layers moved I get no difference in accuracy at all (nor good nor bad) so I was wondering if I’m doing something wrong.

This is the code part that I build the base of the model and you can see that I remove (truncate) the last N hidden layers given from the parameter.

def buildBaseHidden(self,hidden_layers=0):
        baseModel = tf.keras.applications.MobileNetV2(input_shape=(self.image_size, self.image_size, 3),
        # Batch normalization layers replaced with bactch renormalization layers - Better for CL
        for l in baseModel.layers:
            if ('_BN' in
                l.renorm = True
        baseModel.trainable = False

        base_model_truncated = tf.keras.Model(inputs=baseModel.input, outputs=baseModel.layers[-hidden_layers-1].output)
        self.base = base_model_truncated

        inputs = tf.keras.Input(shape=(self.image_size, self.image_size, 3))
        f = inputs
        f_out = self.base(f)
        self.feature_extractor = tf.keras.Model(f, f_out)
        self.feature_extractor.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001), loss='categorical_crossentropy',

You can also see below my building of the head of the model where I add those N amount of hidden layers from the MobileNetV2 and make them trainable right before the flattening layer.

def buildHeadHidden(self, sl_units=32, hidden_layers=0):

        baseModel = tf.keras.applications.MobileNetV2(input_shape=(self.image_size, self.image_size, 3),

        self.sl_units = sl_units

        # Create a new head model
        self.head = tf.keras.Sequential()

        # Add the last N layers of MobileNetV2 to the head model
        for i in range(-hidden_layers, 0):
            layer = baseModel.layers[i]
            layer.trainable = True

        self.head.add(layers.Flatten(input_shape=(4, 4, 1280)))
        # Removed the dense layer since we add Hidden Layers from MobileNetV2 now

        # Softmax layer (Last layer)
            units=50,  # Number of classes

        self.head.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001), loss='sparse_categorical_crossentropy',

Lastly, I’m printing the body and the head of my model to make sure that the layers have moved and they are trainable as you can see below (not seeing the whole body layers since its all the layers of the MobileNetV2 minus the last few N ones that we moved, in this example below we moved 3 layers)

...a lot of MobileNetV2 layers...
block_16_expand: False
block_16_expand_BN: False
block_16_expand_relu: False
block_16_depthwise: False
block_16_depthwise_BN: False
block_16_depthwise_relu: False
block_16_project: False
block_16_project_BN: False

Head model trainable status:
Conv_1: True
Conv_1_bn: True
out_relu: True
flatten: True
dense: True
dense_1: True

Complete model trainable status:
input_4: True
model: True
sequential: True

Any help or feedback is appreciated.

Thanks in advance,