AssertionError: compute_gradients() on the differentially private optimizer was not called. Which means that the training is not differentially private. It happens for example in Keras training in TensorFlow 2.0+.

Dear Developers,

Thank you for implementing and maintaining such a great repository.
I tried to train my model without eager execution in TensorFlow version 2.3.1.
Some code snippets are below:

optimizer = DPGradientDescentGaussianOptimizer(
        l2_norm_clip=1.0,
        noise_multiplier=0.1,
        num_microbatches=args.batch_size,
        learning_rate=0.15
)

with tf.GradientTape() as gradient_tape:
    loss_real = tf.keras.losses.binary_crossentropy(tf.ones_like(Y_real), Y_real, from_logits=True)
    loss_fake = tf.keras.losses.binary_crossentropy(tf.zeros_like(Y_fake), Y_fake, from_logits=True)
    loss = loss_real + loss_fake
            
var_list = self.model.trainable_weights
grads = gradient_tape.gradient(loss, var_list)
optimizer.apply_gradients(zip(grads, var_list))

How can I solve this problem? Does TensorFlow Privacy now support the newer version of Optimizer in TensorFlow2.x?

Start with DPGradientDescentGaussianOptimizer and the TensorFlow 2.3.1 Keras training example in the report. Investigate why apply_gradients does not invoke compute_gradients, then verify the supported TensorFlow 2.x usage by confirming the differential-privacy assertion is no longer raised.