Extracting features from EfficientNet Tensorflow

I have a CNN model trained using EfficientNetB6. My task is to extract the features of this trained model by removing the last dense layer and then using those weights to train a boosting model. i did this using Pytorch earlier and was able to extract the weights from the layers i was interested and predicted on my validation set and then boosted.

I am doing this now in tensorflow but currently stuck. Below is my model structure and I have tried using the code on the website but did not had any luck.

I want to remove the last dense layer and predict on the validation set using the remaining layers.

I tried using :

layer_name = 'efficientnet-b6' intermediate_layer_model = tf.keras.Model(inputs = model.input, outputs = model.get_layer(layer_name).output)

but i get an error " ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 760, 760, 3), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []"

Any way to resolve this?

Sorry my bad. I simply added a GlobalAveragePooling2D layer after the efficientnet layer and i am able to extract the weights and continue :)

just for reference:

def build_model(dim=CFG['net_size'], ef=0):
    inp = tf.keras.layers.Input(shape=(dim,dim,3))
    base = EFNS[ef](input_shape=(dim,dim,3),weights='imagenet',include_top=False)
    x = base(inp)
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dense(1,activation='sigmoid')(x)
    model = tf.keras.Model(inputs=inp,outputs=x)
    opt = tf.keras.optimizers.Adam(learning_rate=0.001)
    loss = tf.keras.losses.BinaryCrossentropy(label_smoothing=0.05) 
    model.compile(optimizer=CFG['optimizer'],loss=loss,metrics=[tf.keras.metrics.AUC(name='auc')])
    print(model.summary())
    return model