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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Copyright 2021 The TensorFlow Authors.
Step1:  <table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https
Step2:  Train a tf.keras model for MNIST to be pruned and clustered
Step3:  Evaluate the baseline model and save it for later usage
Step4:  Prune and fine-tune the model to 50% sparsity
Apply the prune_low_magnitude() API to achieve the pruned model that is to be clustered in the next step. Refer to the pruning comprehensive guide for more information on the pruning API.
Define the model and apply the sparsity API
Note that the pre-trained model is used.
Step5:  Fine-tune the model, check sparsity, and evaluate the accuracy against baseline
Fine-tune the model with pruning for 3 epochs.
Step6:  Define helper functions to calculate and print the sparsity and clusters of the model.
Step7:  Let's strip the pruning wrapper first, then check that the model kernels were correctly pruned.
Step8:  Apply sparsity preserving clustering and check its effect on model sparsity in both cases
Next, apply sparsity preserving clustering on the pruned model and observe the number of clusters and check that the sparsity is preserved.
Step9:  Strip the clustering wrapper first, then check that the model is correctly pruned and clustered.
Step10:  Apply QAT and PCQAT and check effect on model clusters and sparsity
Next, apply both QAT and PCQAT on the sparse clustered model and observe that PCQAT preserves weight sparsity and clusters in your model. Note that the stripped model is passed to the QAT and PCQAT API.
Step11:  See compression benefits of PCQAT model
Define helper function to get zipped model file.
Step12:  Observe that applying sparsity, clustering and PCQAT to a model yields significant compression benefits.
Step13:  See the persistence of accuracy from TF to TFLite
Define a helper function to evaluate the TFLite model on the test dataset.
Step14:  Evaluate the model, which has been pruned, clustered and quantized, and then see that the accuracy from TensorFlow persists in the TFLite backend.
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Python Code:
#@title 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
#
# https://www.apache.org/licenses/LICENSE-2.0
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# 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.
Explanation: Copyright 2021 The TensorFlow Authors.
End of explanation
! pip install -q tensorflow-model-optimization
import tensorflow as tf
import numpy as np
import tempfile
import zipfile
import os
Explanation: <table class="tfo-notebook-buttons" align="left">
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    <a target="_blank" href="https://www.tensorflow.org/model_optimization/guide/combine/pcqat_example"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
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    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/combine/pcqat_example.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
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    <a target="_blank" href="https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/combine/pcqat_example.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/model-optimization/tensorflow_model_optimization/g3doc/guide/combine/pcqat_example.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
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Sparsity and cluster preserving quantization aware training (PCQAT) Keras example
Overview
This is an end to end example showing the usage of the sparsity and cluster preserving quantization aware training (PCQAT) API, part of the TensorFlow Model Optimization Toolkit's collaborative optimization pipeline.
Other pages
For an introduction to the pipeline and other available techniques, see the collaborative optimization overview page.
Contents
In the tutorial, you will:
Train a tf.keras model for the MNIST dataset from scratch.
Fine-tune the model with pruning and see the accuracy and observe that the model was successfully pruned.
Apply sparsity preserving clustering on the pruned model and observe that the sparsity applied earlier has been preserved.
Apply QAT and observe the loss of sparsity and clusters.
Apply PCQAT and observe that both sparsity and clustering applied earlier have been preserved.
Generate a TFLite model and observe the effects of applying PCQAT on it.
Compare the sizes of the different models to observe the compression benefits of applying sparsity followed by the collaborative optimization techniques of sparsity preserving clustering and PCQAT.
Compare the accurracy of the fully optimized model with the un-optimized baseline model accuracy.
Setup
You can run this Jupyter Notebook in your local virtualenv or colab. For details of setting up dependencies, please refer to the installation guide.
End of explanation
# Load MNIST dataset
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images  = test_images / 255.0
model = tf.keras.Sequential([
  tf.keras.layers.InputLayer(input_shape=(28, 28)),
  tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
  tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3),
                         activation=tf.nn.relu),
  tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10)
])
opt = tf.keras.optimizers.Adam(learning_rate=1e-3)
# Train the digit classification model
model.compile(optimizer=opt,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(
    train_images,
    train_labels,
    validation_split=0.1,
    epochs=10
)
Explanation: Train a tf.keras model for MNIST to be pruned and clustered
End of explanation
_, baseline_model_accuracy = model.evaluate(
    test_images, test_labels, verbose=0)
print('Baseline test accuracy:', baseline_model_accuracy)
_, keras_file = tempfile.mkstemp('.h5')
print('Saving model to: ', keras_file)
tf.keras.models.save_model(model, keras_file, include_optimizer=False)
Explanation: Evaluate the baseline model and save it for later usage
End of explanation
import tensorflow_model_optimization as tfmot
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
pruning_params = {
      'pruning_schedule': tfmot.sparsity.keras.ConstantSparsity(0.5, begin_step=0, frequency=100)
  }
callbacks = [
  tfmot.sparsity.keras.UpdatePruningStep()
]
pruned_model = prune_low_magnitude(model, **pruning_params)
# Use smaller learning rate for fine-tuning
opt = tf.keras.optimizers.Adam(learning_rate=1e-5)
pruned_model.compile(
  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
  optimizer=opt,
  metrics=['accuracy'])
Explanation: Prune and fine-tune the model to 50% sparsity
Apply the prune_low_magnitude() API to achieve the pruned model that is to be clustered in the next step. Refer to the pruning comprehensive guide for more information on the pruning API.
Define the model and apply the sparsity API
Note that the pre-trained model is used.
End of explanation
# Fine-tune model
pruned_model.fit(
  train_images,
  train_labels,
  epochs=3,
  validation_split=0.1,
  callbacks=callbacks)
Explanation: Fine-tune the model, check sparsity, and evaluate the accuracy against baseline
Fine-tune the model with pruning for 3 epochs.
End of explanation
def print_model_weights_sparsity(model):
    for layer in model.layers:
        if isinstance(layer, tf.keras.layers.Wrapper):
            weights = layer.trainable_weights
        else:
            weights = layer.weights
        for weight in weights:
            if "kernel" not in weight.name or "centroid" in weight.name:
                continue
            weight_size = weight.numpy().size
            zero_num = np.count_nonzero(weight == 0)
            print(
                f"{weight.name}: {zero_num/weight_size:.2%} sparsity ",
                f"({zero_num}/{weight_size})",
            )
def print_model_weight_clusters(model):
    for layer in model.layers:
        if isinstance(layer, tf.keras.layers.Wrapper):
            weights = layer.trainable_weights
        else:
            weights = layer.weights
        for weight in weights:
            # ignore auxiliary quantization weights
            if "quantize_layer" in weight.name:
                continue
            if "kernel" in weight.name:
                unique_count = len(np.unique(weight))
                print(
                    f"{layer.name}/{weight.name}: {unique_count} clusters "
                )
Explanation: Define helper functions to calculate and print the sparsity and clusters of the model.
End of explanation
stripped_pruned_model = tfmot.sparsity.keras.strip_pruning(pruned_model)
print_model_weights_sparsity(stripped_pruned_model)
Explanation: Let's strip the pruning wrapper first, then check that the model kernels were correctly pruned.
End of explanation
import tensorflow_model_optimization as tfmot
from tensorflow_model_optimization.python.core.clustering.keras.experimental import (
    cluster,
)
cluster_weights = tfmot.clustering.keras.cluster_weights
CentroidInitialization = tfmot.clustering.keras.CentroidInitialization
cluster_weights = cluster.cluster_weights
clustering_params = {
  'number_of_clusters': 8,
  'cluster_centroids_init': CentroidInitialization.KMEANS_PLUS_PLUS,
  'preserve_sparsity': True
}
sparsity_clustered_model = cluster_weights(stripped_pruned_model, **clustering_params)
sparsity_clustered_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
print('Train sparsity preserving clustering model:')
sparsity_clustered_model.fit(train_images, train_labels,epochs=3, validation_split=0.1)
Explanation: Apply sparsity preserving clustering and check its effect on model sparsity in both cases
Next, apply sparsity preserving clustering on the pruned model and observe the number of clusters and check that the sparsity is preserved.
End of explanation
stripped_clustered_model = tfmot.clustering.keras.strip_clustering(sparsity_clustered_model)
print("Model sparsity:\n")
print_model_weights_sparsity(stripped_clustered_model)
print("\nModel clusters:\n")
print_model_weight_clusters(stripped_clustered_model)
Explanation: Strip the clustering wrapper first, then check that the model is correctly pruned and clustered.
End of explanation
# QAT
qat_model = tfmot.quantization.keras.quantize_model(stripped_clustered_model)
qat_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
print('Train qat model:')
qat_model.fit(train_images, train_labels, batch_size=128, epochs=1, validation_split=0.1)
# PCQAT
quant_aware_annotate_model = tfmot.quantization.keras.quantize_annotate_model(
              stripped_clustered_model)
pcqat_model = tfmot.quantization.keras.quantize_apply(
              quant_aware_annotate_model,
              tfmot.experimental.combine.Default8BitClusterPreserveQuantizeScheme(preserve_sparsity=True))
pcqat_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
print('Train pcqat model:')
pcqat_model.fit(train_images, train_labels, batch_size=128, epochs=1, validation_split=0.1)
print("QAT Model clusters:")
print_model_weight_clusters(qat_model)
print("\nQAT Model sparsity:")
print_model_weights_sparsity(qat_model)
print("\nPCQAT Model clusters:")
print_model_weight_clusters(pcqat_model)
print("\nPCQAT Model sparsity:")
print_model_weights_sparsity(pcqat_model)
Explanation: Apply QAT and PCQAT and check effect on model clusters and sparsity
Next, apply both QAT and PCQAT on the sparse clustered model and observe that PCQAT preserves weight sparsity and clusters in your model. Note that the stripped model is passed to the QAT and PCQAT API.
End of explanation
def get_gzipped_model_size(file):
  # It returns the size of the gzipped model in kilobytes.
  _, zipped_file = tempfile.mkstemp('.zip')
  with zipfile.ZipFile(zipped_file, 'w', compression=zipfile.ZIP_DEFLATED) as f:
    f.write(file)
  return os.path.getsize(zipped_file)/1000
Explanation: See compression benefits of PCQAT model
Define helper function to get zipped model file.
End of explanation
# QAT model
converter = tf.lite.TFLiteConverter.from_keras_model(qat_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
qat_tflite_model = converter.convert()
qat_model_file = 'qat_model.tflite'
# Save the model.
with open(qat_model_file, 'wb') as f:
    f.write(qat_tflite_model)
# PCQAT model
converter = tf.lite.TFLiteConverter.from_keras_model(pcqat_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
pcqat_tflite_model = converter.convert()
pcqat_model_file = 'pcqat_model.tflite'
# Save the model.
with open(pcqat_model_file, 'wb') as f:
    f.write(pcqat_tflite_model)
print("QAT model size: ", get_gzipped_model_size(qat_model_file), ' KB')
print("PCQAT model size: ", get_gzipped_model_size(pcqat_model_file), ' KB')
Explanation: Observe that applying sparsity, clustering and PCQAT to a model yields significant compression benefits.
End of explanation
def eval_model(interpreter):
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]
  # Run predictions on every image in the "test" dataset.
  prediction_digits = []
  for i, test_image in enumerate(test_images):
    if i % 1000 == 0:
      print(f"Evaluated on {i} results so far.")
    # Pre-processing: add batch dimension and convert to float32 to match with
    # the model's input data format.
    test_image = np.expand_dims(test_image, axis=0).astype(np.float32)
    interpreter.set_tensor(input_index, test_image)
    # Run inference.
    interpreter.invoke()
    # Post-processing: remove batch dimension and find the digit with highest
    # probability.
    output = interpreter.tensor(output_index)
    digit = np.argmax(output()[0])
    prediction_digits.append(digit)
  print('\n')
  # Compare prediction results with ground truth labels to calculate accuracy.
  prediction_digits = np.array(prediction_digits)
  accuracy = (prediction_digits == test_labels).mean()
  return accuracy
Explanation: See the persistence of accuracy from TF to TFLite
Define a helper function to evaluate the TFLite model on the test dataset.
End of explanation
interpreter = tf.lite.Interpreter(pcqat_model_file)
interpreter.allocate_tensors()
pcqat_test_accuracy = eval_model(interpreter)
print('Pruned, clustered and quantized TFLite test_accuracy:', pcqat_test_accuracy)
print('Baseline TF test accuracy:', baseline_model_accuracy)
Explanation: Evaluate the model, which has been pruned, clustered and quantized, and then see that the accuracy from TensorFlow persists in the TFLite backend.
End of explanation
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