Transfer Learning with pretrained Keras Model

PennyLane Help
11

Hi @eltabre!

When I add the Quantum Keras Layer It has 0 params and is unused. How can I fix that?

This can be fixed by doing a forward pass through the model before printing the summary, e.g., like so:

model.add(qlayer)
model.add(clayer)

model(x_test[:2])

model.summary()

image
image841×265 12.5 KB

The summary then shows the Keras layer parameters properly loaded. I’m not sure why you see (unused) without the forward pass, perhaps KerasLayer isn’t correctly overriding a required method :thinking:

As a paranoia check, you can confirm that the model is sensitive to the Keras layer parameters by calculating the gradient:

with tf.GradientTape() as tape:
    out = model(x_test[:2])
    
tape.jacobian(out, qlayer.trainable_weights)
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