Tensorflow: Build new model from input and middle layers of another model

I'm trying to build new_model from another model layers for class activation mapping purposes.

def vgg_sequential():
    input_shape = IMG_SIZE + (3,)
    model = Sequential()
    model.add(tf.keras.applications.vgg16.VGG16(input_shape=input_shape, include_top=False, weights='imagenet'))
    model.add(layers.GlobalAveragePooling2D())
    model.add(layers.Dense(1))
    return model

cam_model = tf.keras.Model(inputs=seq_vgg.layers[0].input, outputs=(seq_vgg.layers[-3].output, seq_vgg.layers[-1].output))

And with this code i get the following error:

ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 480, 480, 3), dtype=tf.float32, name='vgg16_input'), name='vgg16_input', description="created by layer 'vgg16_input'") at layer "vgg16". The following previous layers were accessed without issue: ['block1_conv1', 'block1_conv2', 'block1_pool', 'block2_conv1', 'block2_conv2', 'block2_pool', 'block3_conv1', 'block3_conv2', 'block3_conv3', 'block3_pool', 'block4_conv1', 'block4_conv2', 'block4_conv3', 'block4_pool', 'block5_conv1']

Already tried functional model API, providing Input() layer inside vgg_sequential() with the same error that my Input layer is disconected from the rest of my model. Beside this when using tf.keras.applications.efficientnet_v2 that provides input layers for rescaling and resizing images i don't have any problem.

Any help, information, tips or links to docs that getas me to a solution will be very much appreciated.

Thanks in advance.

In your case, you may need to write your model definition in the following way in order to build class activation mapping network. Know more details in this ticket.

def vgg_sequential():
    inputs = keras.Input(shape=(224, 224, 3,))
    pretrained_model = keras.applications.VGG16(
            weights='imagenet', 
            include_top=False, 
            input_tensor=inputs,
        )
    x = keras.layers.GlobalAveragePooling2D()(pretrained_model.output)
    outputs = keras.layers.Dense(1)(x)
    model = keras.Model(inputs, outputs)
    return model

And then you can do

cam_model = keras.Model(
    inputs=seq_vgg.layers[0].input, 
    outputs=(
        seq_vgg.layers[-3].output, 
        seq_vgg.layers[-1].output
    )
)

a, b = cam_model(tf.ones((1, 224, 224, 3)))
a.shape, b.shape
(TensorShape([1, 7, 7, 512]), TensorShape([1, 1]))