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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 2020 The TensorFlow Authors.
Step1:  Pruning in Keras example
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https
Step2:  Train a model for MNIST without pruning
Step3:  Evaluate baseline test accuracy and save the model for later usage.
Step4:  Fine-tune pre-trained model with pruning
Define the model
You will apply pruning to the whole model and see this in the model summary.
In this example, you start the model with 50% sparsity (50% zeros in weights)
and end with 80% sparsity.
In the comprehensive guide, you can see how to prune some layers for model accuracy improvements.
Step5:  Train and evaluate the model against baseline
Fine tune with pruning for two epochs.
tfmot.sparsity.keras.UpdatePruningStep is required during training, and tfmot.sparsity.keras.PruningSummaries provides logs for tracking progress and debugging.
Step6:  For this example, there is minimal loss in test accuracy after pruning, compared to the baseline.
Step7:  The logs show the progression of sparsity on a per-layer basis.
Step8:  For non-Colab users, you can see the results of a previous run of this code block on TensorBoard.dev.
Create 3x smaller models from pruning
Both tfmot.sparsity.keras.strip_pruning and applying a standard compression algorithm (e.g. via gzip) are necessary to see the compression
benefits of pruning.
strip_pruning is necessary since it removes every tf.Variable that pruning only needs during training, which would otherwise add to model size during inference
Applying a standard compression algorithm is necessary since the serialized weight matrices are the same size as they were before pruning. However, pruning makes most of the weights zeros, which is
added redundancy that algorithms can utilize to further compress the model.
First, create a compressible model for TensorFlow.
Step9:  Then, create a compressible model for TFLite.
Step10:  Define a helper function to actually compress the models via gzip and measure the zipped size.
Step11:  Compare and see that the models are 3x smaller from pruning.
Step12:  Create a 10x smaller model from combining pruning and quantization
You can apply post-training quantization to the pruned model for additional benefits.
Step13:  See persistence of accuracy from TF to TFLite
Define a helper function to evaluate the TF Lite model on the test dataset.
Step14:  You evaluate the pruned and quantized model and see that the accuracy from TensorFlow persists to 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 2020 The TensorFlow Authors.
End of explanation
! pip install -q tensorflow-model-optimization
import tempfile
import os
import tensorflow as tf
import numpy as np
from tensorflow import keras
%load_ext tensorboard
Explanation: Pruning in Keras example
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/model-optimization/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
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</table>
Overview
Welcome to an end-to-end example for magnitude-based weight pruning.
Other pages
For an introduction to what pruning is and to determine if you should use it (including what's supported), see the overview page.
To quickly find the APIs you need for your use case (beyond fully pruning a model with 80% sparsity), see the
comprehensive guide.
Summary
In this tutorial, you will:
Train a tf.keras model for MNIST from scratch.
Fine tune the model by applying the pruning API and see the accuracy.
Create 3x smaller TF and TFLite models from pruning.
Create a 10x smaller TFLite model from combining pruning and post-training quantization.
See the persistence of accuracy from TF to TFLite.
Setup
End of explanation
# Load MNIST dataset
mnist = 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 and 1.
train_images = train_images / 255.0
test_images = test_images / 255.0
# Define the model architecture.
model = keras.Sequential([
  keras.layers.InputLayer(input_shape=(28, 28)),
  keras.layers.Reshape(target_shape=(28, 28, 1)),
  keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),
  keras.layers.MaxPooling2D(pool_size=(2, 2)),
  keras.layers.Flatten(),
  keras.layers.Dense(10)
])
# Train the digit classification model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(
  train_images,
  train_labels,
  epochs=4,
  validation_split=0.1,
)
Explanation: Train a model for MNIST without pruning
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')
tf.keras.models.save_model(model, keras_file, include_optimizer=False)
print('Saved baseline model to:', keras_file)
Explanation: Evaluate baseline test accuracy and save the model for later usage.
End of explanation
import tensorflow_model_optimization as tfmot
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
# Compute end step to finish pruning after 2 epochs.
batch_size = 128
epochs = 2
validation_split = 0.1 # 10% of training set will be used for validation set. 
num_images = train_images.shape[0] * (1 - validation_split)
end_step = np.ceil(num_images / batch_size).astype(np.int32) * epochs
# Define model for pruning.
pruning_params = {
      'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,
                                                               final_sparsity=0.80,
                                                               begin_step=0,
                                                               end_step=end_step)
}
model_for_pruning = prune_low_magnitude(model, **pruning_params)
# `prune_low_magnitude` requires a recompile.
model_for_pruning.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model_for_pruning.summary()
Explanation: Fine-tune pre-trained model with pruning
Define the model
You will apply pruning to the whole model and see this in the model summary.
In this example, you start the model with 50% sparsity (50% zeros in weights)
and end with 80% sparsity.
In the comprehensive guide, you can see how to prune some layers for model accuracy improvements.
End of explanation
logdir = tempfile.mkdtemp()
callbacks = [
  tfmot.sparsity.keras.UpdatePruningStep(),
  tfmot.sparsity.keras.PruningSummaries(log_dir=logdir),
]
  
model_for_pruning.fit(train_images, train_labels,
                  batch_size=batch_size, epochs=epochs, validation_split=validation_split,
                  callbacks=callbacks)
Explanation: Train and evaluate the model against baseline
Fine tune with pruning for two epochs.
tfmot.sparsity.keras.UpdatePruningStep is required during training, and tfmot.sparsity.keras.PruningSummaries provides logs for tracking progress and debugging.
End of explanation
_, model_for_pruning_accuracy = model_for_pruning.evaluate(
   test_images, test_labels, verbose=0)
print('Baseline test accuracy:', baseline_model_accuracy) 
print('Pruned test accuracy:', model_for_pruning_accuracy)
Explanation: For this example, there is minimal loss in test accuracy after pruning, compared to the baseline.
End of explanation
#docs_infra: no_execute
%tensorboard --logdir={logdir}
Explanation: The logs show the progression of sparsity on a per-layer basis.
End of explanation
model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)
_, pruned_keras_file = tempfile.mkstemp('.h5')
tf.keras.models.save_model(model_for_export, pruned_keras_file, include_optimizer=False)
print('Saved pruned Keras model to:', pruned_keras_file)
Explanation: For non-Colab users, you can see the results of a previous run of this code block on TensorBoard.dev.
Create 3x smaller models from pruning
Both tfmot.sparsity.keras.strip_pruning and applying a standard compression algorithm (e.g. via gzip) are necessary to see the compression
benefits of pruning.
strip_pruning is necessary since it removes every tf.Variable that pruning only needs during training, which would otherwise add to model size during inference
Applying a standard compression algorithm is necessary since the serialized weight matrices are the same size as they were before pruning. However, pruning makes most of the weights zeros, which is
added redundancy that algorithms can utilize to further compress the model.
First, create a compressible model for TensorFlow.
End of explanation
converter = tf.lite.TFLiteConverter.from_keras_model(model_for_export)
pruned_tflite_model = converter.convert()
_, pruned_tflite_file = tempfile.mkstemp('.tflite')
with open(pruned_tflite_file, 'wb') as f:
  f.write(pruned_tflite_model)
print('Saved pruned TFLite model to:', pruned_tflite_file)
Explanation: Then, create a compressible model for TFLite.
End of explanation
def get_gzipped_model_size(file):
  # Returns size of gzipped model, in bytes.
  import os
  import zipfile
  _, 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)
Explanation: Define a helper function to actually compress the models via gzip and measure the zipped size.
End of explanation
print("Size of gzipped baseline Keras model: %.2f bytes" % (get_gzipped_model_size(keras_file)))
print("Size of gzipped pruned Keras model: %.2f bytes" % (get_gzipped_model_size(pruned_keras_file)))
print("Size of gzipped pruned TFlite model: %.2f bytes" % (get_gzipped_model_size(pruned_tflite_file)))
Explanation: Compare and see that the models are 3x smaller from pruning.
End of explanation
converter = tf.lite.TFLiteConverter.from_keras_model(model_for_export)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
quantized_and_pruned_tflite_model = converter.convert()
_, quantized_and_pruned_tflite_file = tempfile.mkstemp('.tflite')
with open(quantized_and_pruned_tflite_file, 'wb') as f:
  f.write(quantized_and_pruned_tflite_model)
print('Saved quantized and pruned TFLite model to:', quantized_and_pruned_tflite_file)
print("Size of gzipped baseline Keras model: %.2f bytes" % (get_gzipped_model_size(keras_file)))
print("Size of gzipped pruned and quantized TFlite model: %.2f bytes" % (get_gzipped_model_size(quantized_and_pruned_tflite_file)))
Explanation: Create a 10x smaller model from combining pruning and quantization
You can apply post-training quantization to the pruned model for additional benefits.
End of explanation
import numpy as np
def evaluate_model(interpreter):
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]
  # Run predictions on ever y image in the "test" dataset.
  prediction_digits = []
  for i, test_image in enumerate(test_images):
    if i % 1000 == 0:
      print('Evaluated on {n} results so far.'.format(n=i))
    # 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 persistence of accuracy from TF to TFLite
Define a helper function to evaluate the TF Lite model on the test dataset.
End of explanation
interpreter = tf.lite.Interpreter(model_content=quantized_and_pruned_tflite_model)
interpreter.allocate_tensors()
test_accuracy = evaluate_model(interpreter)
print('Pruned and quantized TFLite test_accuracy:', test_accuracy)
print('Pruned TF test accuracy:', model_for_pruning_accuracy)
Explanation: You evaluate the pruned and quantized model and see that the accuracy from TensorFlow persists to the TFLite backend.
End of explanation
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