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diff --git a/model_conditioning_examples/weight_pruning.py b/model_conditioning_examples/weight_pruning.py
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+#  Copyright (c) 2021 Arm Limited. All rights reserved.
+#  SPDX-License-Identifier: Apache-2.0
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""
+This script will provide you with a short example of how to perform magnitude-based weight pruning in TensorFlow
+using the TensorFlow Model Optimization Toolkit.
+
+The output from this example will be a TensorFlow Lite model file where ~75% percent of the weights have been 'pruned' to the
+value 0 during training - quantization has then been applied on top of this.
+
+By pruning the model we can improve compression of the model file. This can be essential for deploying certain models
+on systems with limited resources - such as embedded systems using Arm Ethos NPU. Also, if the pruned model is run
+on an Arm Ethos NPU then this pruning can improve the execution time of the model.
+
+After pruning is complete we do post-training quantization to quantize the model and then generate a TensorFlow Lite file.
+
+If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+compiler for further optimizations before it can be used.
+
+For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on weight pruning see: https://www.tensorflow.org/model_optimization/guide/pruning
+"""
+import pathlib
+
+import tensorflow as tf
+import tensorflow_model_optimization as tfmot
+
+from training_utils import get_data, create_model
+from post_training_quantization import post_training_quantize, evaluate_tflite_model
+
+
+def prepare_for_pruning(keras_model):
+    """Prepares a Keras model for pruning."""
+
+    # We use a constant sparsity schedule so the amount of sparsity in the model is kept at the same percent throughout
+    # training. An alternative is PolynomialDecay where sparsity can be gradually increased during training.
+    pruning_schedule = tfmot.sparsity.keras.ConstantSparsity(target_sparsity=0.75, begin_step=0)
+
+    # Apply the pruning wrapper to the whole model so weights in every layer will get pruned. You may find that to avoid
+    # too much accuracy loss only certain non-critical layers in your model should be pruned.
+    pruning_ready_model = tfmot.sparsity.keras.prune_low_magnitude(keras_model, pruning_schedule=pruning_schedule)
+
+    # We must recompile the model after making it ready for pruning.
+    pruning_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+                                loss=tf.keras.losses.sparse_categorical_crossentropy,
+                                metrics=['accuracy'])
+
+    return pruning_ready_model
+
+
+def main():
+    x_train, y_train, x_test, y_test = get_data()
+    model = create_model()
+
+    # Compile and train the model first.
+    # In general it is easier to do pruning as a fine-tuning step after the model is fully trained.
+    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+                  loss=tf.keras.losses.sparse_categorical_crossentropy,
+                  metrics=['accuracy'])
+
+    model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
+
+    # Test the trained model accuracy.
+    test_loss, test_acc = model.evaluate(x_test, y_test)
+    print(f"Test accuracy before pruning: {test_acc:.3f}")
+
+    # Prepare the model for pruning and add the pruning update callback needed in training.
+    pruned_model = prepare_for_pruning(model)
+    callbacks = [tfmot.sparsity.keras.UpdatePruningStep()]
+
+    # Continue training the model but now with pruning applied - remember to pass in the callbacks!
+    pruned_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True, callbacks=callbacks)
+    test_loss, test_acc = pruned_model.evaluate(x_test, y_test)
+    print(f"Test accuracy after pruning: {test_acc:.3f}")
+
+    # Remove all variables that pruning only needed in the training phase.
+    model_for_export = tfmot.sparsity.keras.strip_pruning(pruned_model)
+
+    # Apply post-training quantization on top of the pruning and save the resulting TensorFlow Lite model to file.
+    tflite_model = post_training_quantize(model_for_export, x_train)
+
+    tflite_models_dir = pathlib.Path('./conditioned_models/')
+    tflite_models_dir.mkdir(exist_ok=True, parents=True)
+
+    pruned_quant_model_save_path = tflite_models_dir / 'pruned_post_training_quant_model.tflite'
+    with open(pruned_quant_model_save_path, 'wb') as f:
+        f.write(tflite_model)
+
+    # Test the pruned quantized model accuracy. Save time by only testing a subset of the whole data.
+    num_test_samples = 1000
+    evaluate_tflite_model(pruned_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+
+
+if __name__ == "__main__":
+    main()