Load the checkpoint saved from the previous fine-tuning stage to the model. Increase the ratio value to get a sparser model. Then continue to fine-tune this sparse model. Repeat this pruning and fine-tuning loop a couple of times until the sparsity reaches the desired value.
model.load_weights("model_sparse_0.2") input_shape = [28, 28, 1] input_spec = tf.TensorSpec((1, *input_shape), tf.float32) runner = IterativePruningRunner(model, input_spec) sparse_model = runner.prune(ratio=0.5)