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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 without pruning
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 prune the whole pre-trained model to demonstrate and observe its effectiveness in reducing the model size when applying zip, while maintaining accuracy. For how best to use the API to achieve the best compression rate while maintaining your target accuracy, refer to the pruning comprehensive guide.
Define the model and apply the sparsity API
The model needs to be pre-trained before using the sparsity API.
Step5:  Fine-tune the model 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 of the model.
Step7:  Check that the model was correctly pruned. We need to strip the pruning wrapper first.
Step8:  For this example, there is minimal loss in test accuracy after pruning, compared to the baseline.
Step9:  Apply QAT and PQAT and check effect on model sparsity in both cases
Next, we apply both QAT and pruning-preserving QAT (PQAT) on the pruned model and observe that PQAT preserves sparsity on your pruned model. Note that we stripped pruning wrappers from your pruned model with tfmot.sparsity.keras.strip_pruning before applying PQAT API.
Step10:  See compression benefits of PQAT model
Define helper function to get zipped model file.
Step11:  Since this is a small model, the difference between the two models isn't very noticeable. Applying pruning and PQAT to a bigger production model would yield a more significant compression.
Step12:  See the persistence of accuracy from TF to TFLite
Define a helper function to evaluate the TFLite model on the test dataset.
Step13:  You evaluate the model, which has been pruned and quantized, and then see the accuracy from TensorFlow persists in the TFLite backend.
Step14:  Apply post-training quantization and compare to PQAT model
Next, we use normal post-training quantization (no fine-tuning) on the pruned model and check its accuracy against the PQAT model. This demonstrates why you would need to use PQAT to improve the quantized model's accuracy.
First, define a generator for the callibration dataset from the first 1000 training images.
Step15:  Quantize the model and compare accuracy to the previously acquired PQAT model. Note that the model quantized with fine-tuning achieves higher accuracy.
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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
#
# 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">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/model_optimization/guide/combine/pqat_example"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
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  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/combine/pqat_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/pqat_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/pqat_example.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
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</table>
Pruning preserving quantization aware training (PQAT) Keras example
Overview
This is an end to end example showing the usage of the pruning preserving quantization aware training (PQAT) 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, using the sparsity API, and see the accuracy.
Apply QAT and observe the loss of sparsity.
Apply PQAT and observe that the sparsity applied earlier has been preserved.
Generate a TFLite model and observe the effects of applying PQAT on it.
Compare the achieved PQAT model accuracy with a model quantized using post-training quantization.
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)
])
# 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,
    validation_split=0.1,
    epochs=10
)
Explanation: Train a tf.keras 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')
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'])
pruned_model.summary()
Explanation: Prune and fine-tune the model to 50% sparsity
Apply the prune_low_magnitude() API to prune the whole pre-trained model to demonstrate and observe its effectiveness in reducing the model size when applying zip, while maintaining accuracy. For how best to use the API to achieve the best compression rate while maintaining your target accuracy, refer to the pruning comprehensive guide.
Define the model and apply the sparsity API
The model needs to be pre-trained before using the sparsity API.
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 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:
            # ignore auxiliary quantization weights
            if "quantize_layer" 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})",
            )
Explanation: Define helper functions to calculate and print the sparsity of the model.
End of explanation
stripped_pruned_model = tfmot.sparsity.keras.strip_pruning(pruned_model)
print_model_weights_sparsity(stripped_pruned_model)
Explanation: Check that the model was correctly pruned. We need to strip the pruning wrapper first.
End of explanation
_, pruned_model_accuracy = pruned_model.evaluate(
  test_images, test_labels, verbose=0)
print('Baseline test accuracy:', baseline_model_accuracy)
print('Pruned test accuracy:', pruned_model_accuracy)
Explanation: For this example, there is minimal loss in test accuracy after pruning, compared to the baseline.
End of explanation
# QAT
qat_model = tfmot.quantization.keras.quantize_model(stripped_pruned_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)
# PQAT
quant_aware_annotate_model = tfmot.quantization.keras.quantize_annotate_model(
              stripped_pruned_model)
pqat_model = tfmot.quantization.keras.quantize_apply(
              quant_aware_annotate_model,
              tfmot.experimental.combine.Default8BitPrunePreserveQuantizeScheme())
pqat_model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
print('Train pqat model:')
pqat_model.fit(train_images, train_labels, batch_size=128, epochs=1, validation_split=0.1)
print("QAT Model sparsity:")
print_model_weights_sparsity(qat_model)
print("PQAT Model sparsity:")
print_model_weights_sparsity(pqat_model)
Explanation: Apply QAT and PQAT and check effect on model sparsity in both cases
Next, we apply both QAT and pruning-preserving QAT (PQAT) on the pruned model and observe that PQAT preserves sparsity on your pruned model. Note that we stripped pruning wrappers from your pruned model with tfmot.sparsity.keras.strip_pruning before applying PQAT 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 PQAT 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)
    
# PQAT model
converter = tf.lite.TFLiteConverter.from_keras_model(pqat_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
pqat_tflite_model = converter.convert()
pqat_model_file = 'pqat_model.tflite'
# Save the model.
with open(pqat_model_file, 'wb') as f:
    f.write(pqat_tflite_model)
    
print("QAT model size: ", get_gzipped_model_size(qat_model_file), ' KB')
print("PQAT model size: ", get_gzipped_model_size(pqat_model_file), ' KB')
Explanation: Since this is a small model, the difference between the two models isn't very noticeable. Applying pruning and PQAT to a bigger production model would yield a more significant compression.
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(pqat_model_file)
interpreter.allocate_tensors()
pqat_test_accuracy = eval_model(interpreter)
print('Pruned and quantized TFLite test_accuracy:', pqat_test_accuracy)
print('Pruned TF test accuracy:', pruned_model_accuracy)
Explanation: You evaluate the model, which has been pruned and quantized, and then see the accuracy from TensorFlow persists in the TFLite backend.
End of explanation
def mnist_representative_data_gen():
  for image in train_images[:1000]:  
    image = np.expand_dims(image, axis=0).astype(np.float32)
    yield [image]
Explanation: Apply post-training quantization and compare to PQAT model
Next, we use normal post-training quantization (no fine-tuning) on the pruned model and check its accuracy against the PQAT model. This demonstrates why you would need to use PQAT to improve the quantized model's accuracy.
First, define a generator for the callibration dataset from the first 1000 training images.
End of explanation
converter = tf.lite.TFLiteConverter.from_keras_model(stripped_pruned_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = mnist_representative_data_gen
post_training_tflite_model = converter.convert()
post_training_model_file = 'post_training_model.tflite'
# Save the model.
with open(post_training_model_file, 'wb') as f:
    f.write(post_training_tflite_model)
    
# Compare accuracy
interpreter = tf.lite.Interpreter(post_training_model_file)
interpreter.allocate_tensors()
post_training_test_accuracy = eval_model(interpreter)
print('PQAT TFLite test_accuracy:', pqat_test_accuracy)
print('Post-training (no fine-tuning) TF test accuracy:', post_training_test_accuracy)
Explanation: Quantize the model and compare accuracy to the previously acquired PQAT model. Note that the model quantized with fine-tuning achieves higher accuracy.
End of explanation
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