Machine Learning Pruning Techniques: Pruning Convolutional Neural Networks

1. Introduction

Convolutional neural networks (CNNs) are powerful models for image recognition, natural language processing, and other computer vision tasks. However, CNNs can also be very large and complex, requiring a lot of computational resources and memory to train and run. This can limit their deployment on resource-constrained devices such as mobile phones, IoT sensors, or edge servers.

How can we reduce the size and complexity of CNNs without sacrificing their performance? One possible solution is pruning, a technique that removes unnecessary or redundant parts of the network, such as filters, channels, or layers. Pruning can help us achieve several benefits, such as:

• Reducing the number of parameters and operations, which can speed up the training and inference time.
• Reducing the memory footprint and storage requirements, which can enable the deployment of CNNs on devices with limited resources.
• Improving the generalization and robustness of the network, which can prevent overfitting and enhance the accuracy.

In this blog, we will learn how to prune convolutional neural networks to reduce the number of filters and channels. We will cover the following topics:

1. What is pruning and why is it important?
2. Pruning strategies for convolutional neural networks.
3. Pruning algorithms and tools.
4. Conclusion and future directions.

By the end of this blog, you will have a better understanding of how pruning works and how to apply it to your own CNN models. Let’s get started!

2. What is Pruning and Why is it Important?

Pruning is a technique that removes parts of a neural network that are not essential for its functionality. Pruning can be applied to different levels of the network, such as weights, neurons, filters, channels, or layers. The goal of pruning is to reduce the size and complexity of the network, while preserving or even improving its performance.

Why is pruning important for convolutional neural networks? CNNs are composed of multiple convolutional layers, each containing a set of filters and channels. Filters are the kernels that slide over the input image and produce feature maps. Channels are the dimensions of the feature maps, corresponding to the number of filters in the layer. Filters and channels are the main sources of parameters and operations in CNNs, which can make them very large and expensive to train and run.

However, not all filters and channels are equally important or useful for the network. Some of them may be redundant, irrelevant, or noisy, and contribute little or nothing to the output. Pruning these filters and channels can help us achieve several benefits, such as:

• Reducing the number of parameters and operations, which can speed up the training and inference time.
• Reducing the memory footprint and storage requirements, which can enable the deployment of CNNs on devices with limited resources.
• Improving the generalization and robustness of the network, which can prevent overfitting and enhance the accuracy.

How can we prune convolutional neural networks effectively? What are the criteria and methods for selecting which filters and channels to prune? How can we evaluate the impact of pruning on the network performance? These are some of the questions that we will answer in the next sections. Stay tuned!

3. Pruning Strategies for Convolutional Neural Networks

Pruning strategies for convolutional neural networks can be classified into two main categories: structured pruning and unstructured pruning. Structured pruning removes entire filters, channels, or layers from the network, resulting in a smaller and sparser network. Unstructured pruning removes individual weights from the network, resulting in a sparse matrix of weights. In this section, we will focus on structured pruning, as it is more effective and efficient for CNNs.

Structured pruning can be further divided into three subcategories: filter pruning, channel pruning, and layer pruning. Filter pruning removes filters from each convolutional layer, reducing the number of feature maps and the output channels of the layer. Channel pruning removes channels from each feature map, reducing the input channels of the next layer. Layer pruning removes entire convolutional layers from the network, reducing the depth of the network. Each of these pruning strategies has its own advantages and disadvantages, which we will discuss in the following subsections.

How can we decide which filters, channels, or layers to prune? There are different criteria and methods for selecting the pruning targets, such as the magnitude, the gradient, the activation, the redundancy, or the importance of the filters, channels, or layers. We will also introduce some of these methods in the next subsections. However, there is no universal or optimal method for pruning CNNs, as different methods may have different effects on different networks and tasks. Therefore, it is important to experiment with different pruning strategies and methods, and evaluate their impact on the network performance.

3.1. Filter Pruning

Filter pruning is a structured pruning strategy that removes filters from each convolutional layer, reducing the number of feature maps and the output channels of the layer. Filter pruning can be applied to any convolutional layer in the network, regardless of its position or type. Filter pruning can significantly reduce the size and complexity of the network, as each filter can have hundreds or thousands of parameters and operations.

How can we select which filters to prune? There are different methods and criteria for choosing the pruning targets, such as the magnitude, the gradient, the activation, the redundancy, or the importance of the filters. Here are some examples of filter pruning methods:

• Magnitude-based filter pruning: This method prunes the filters with the smallest L1 or L2 norm, assuming that they have the least contribution to the output. This method is simple and effective, but it may ignore the dependencies and interactions between filters.
• Gradient-based filter pruning: This method prunes the filters with the smallest absolute value of the gradient, assuming that they have the least sensitivity to the loss function. This method can capture the importance of the filters for the network performance, but it may require more computation and memory.
• Activation-based filter pruning: This method prunes the filters with the lowest average percentage of zero activations (APoZ), assuming that they have the least activation and influence on the output. This method can measure the redundancy of the filters, but it may depend on the input data and the activation function.
• Redundancy-based filter pruning: This method prunes the filters with the highest similarity or correlation with other filters, assuming that they have the most redundancy and can be replaced by other filters. This method can reduce the redundancy of the network, but it may require more computation and memory.
• Importance-based filter pruning: This method prunes the filters with the lowest importance score, which can be calculated by different methods, such as the Taylor expansion, the Fisher information, or the network slimming. This method can estimate the importance of the filters for the network performance, but it may require more computation and memory.

How can we evaluate the impact of filter pruning on the network performance? There are different metrics and methods for measuring the performance of the pruned network, such as the accuracy, the compression ratio, the speedup ratio, or the energy efficiency. Here are some examples of performance evaluation methods:

• Accuracy: This metric measures the percentage of correct predictions made by the network on the test data. This metric can reflect the quality of the network output, but it may not capture the trade-off between the accuracy and the complexity of the network.
• Compression ratio: This metric measures the ratio of the number of parameters or operations in the original network to the number of parameters or operations in the pruned network. This metric can reflect the size and complexity reduction of the network, but it may not capture the actual speedup or energy efficiency of the network.
• Speedup ratio: This metric measures the ratio of the training or inference time of the original network to the training or inference time of the pruned network. This metric can reflect the speed improvement of the network, but it may depend on the hardware and software implementation of the network.
• Energy efficiency: This metric measures the ratio of the energy consumption of the original network to the energy consumption of the pruned network. This metric can reflect the energy saving of the network, but it may depend on the hardware and software implementation of the network.

In this subsection, we have introduced the concept and methods of filter pruning, as well as the performance evaluation metrics. In the next subsections, we will introduce the other pruning strategies for convolutional neural networks: channel pruning and layer pruning. Keep reading!

3.2. Channel Pruning

Channel pruning is another structured pruning strategy that removes channels from each feature map, reducing the input channels of the next layer. Channel pruning can be applied to any convolutional layer in the network, regardless of its position or type. Channel pruning can also reduce the size and complexity of the network, as each channel can have hundreds or thousands of parameters and operations.

How can we select which channels to prune? There are different methods and criteria for choosing the pruning targets, such as the magnitude, the gradient, the activation, the redundancy, or the importance of the channels. Here are some examples of channel pruning methods:

• Magnitude-based channel pruning: This method prunes the channels with the smallest L1 or L2 norm of the corresponding filter weights, assuming that they have the least contribution to the output. This method is similar to magnitude-based filter pruning, but it operates on the channel level instead of the filter level.
• Gradient-based channel pruning: This method prunes the channels with the smallest absolute value of the gradient of the corresponding filter weights, assuming that they have the least sensitivity to the loss function. This method is similar to gradient-based filter pruning, but it operates on the channel level instead of the filter level.
• Activation-based channel pruning: This method prunes the channels with the lowest average percentage of zero activations (APoZ), assuming that they have the least activation and influence on the output. This method is similar to activation-based filter pruning, but it operates on the channel level instead of the filter level.
• Redundancy-based channel pruning: This method prunes the channels with the highest similarity or correlation with other channels, assuming that they have the most redundancy and can be replaced by other channels. This method is similar to redundancy-based filter pruning, but it operates on the channel level instead of the filter level.
• Importance-based channel pruning: This method prunes the channels with the lowest importance score, which can be calculated by different methods, such as the Taylor expansion, the Fisher information, or the network slimming. This method is similar to importance-based filter pruning, but it operates on the channel level instead of the filter level.

How can we evaluate the impact of channel pruning on the network performance? There are different metrics and methods for measuring the performance of the pruned network, such as the accuracy, the compression ratio, the speedup ratio, or the energy efficiency. Here are some examples of performance evaluation methods:

• Accuracy: This metric measures the percentage of correct predictions made by the network on the test data. This metric can reflect the quality of the network output, but it may not capture the trade-off between the accuracy and the complexity of the network.
• Compression ratio: This metric measures the ratio of the number of parameters or operations in the original network to the number of parameters or operations in the pruned network. This metric can reflect the size and complexity reduction of the network, but it may not capture the actual speedup or energy efficiency of the network.
• Speedup ratio: This metric measures the ratio of the training or inference time of the original network to the training or inference time of the pruned network. This metric can reflect the speed improvement of the network, but it may depend on the hardware and software implementation of the network.
• Energy efficiency: This metric measures the ratio of the energy consumption of the original network to the energy consumption of the pruned network. This metric can reflect the energy saving of the network, but it may depend on the hardware and software implementation of the network.

In this subsection, we have introduced the concept and methods of channel pruning, as well as the performance evaluation metrics. In the next subsection, we will introduce the other pruning strategy for convolutional neural networks: layer pruning. Keep reading!

3.3. Layer Pruning

Layer pruning is the last structured pruning strategy that we will introduce in this blog. Layer pruning removes entire convolutional layers from the network, reducing the depth of the network. Layer pruning can be applied to any convolutional layer in the network, regardless of its position or type. Layer pruning can also reduce the size and complexity of the network, as each layer can have millions of parameters and operations.

How can we select which layers to prune? There are different methods and criteria for choosing the pruning targets, such as the magnitude, the gradient, the activation, the redundancy, or the importance of the layers. Here are some examples of layer pruning methods:

• Magnitude-based layer pruning: This method prunes the layers with the smallest L1 or L2 norm of the filter weights, assuming that they have the least contribution to the output. This method is similar to magnitude-based filter pruning, but it operates on the layer level instead of the filter level.
• Gradient-based layer pruning: This method prunes the layers with the smallest absolute value of the gradient of the filter weights, assuming that they have the least sensitivity to the loss function. This method is similar to gradient-based filter pruning, but it operates on the layer level instead of the filter level.
• Activation-based layer pruning: This method prunes the layers with the lowest average percentage of zero activations (APoZ), assuming that they have the least activation and influence on the output. This method is similar to activation-based filter pruning, but it operates on the layer level instead of the filter level.
• Redundancy-based layer pruning: This method prunes the layers with the highest similarity or correlation with other layers, assuming that they have the most redundancy and can be replaced by other layers. This method is similar to redundancy-based filter pruning, but it operates on the layer level instead of the filter level.
• Importance-based layer pruning: This method prunes the layers with the lowest importance score, which can be calculated by different methods, such as the Taylor expansion, the Fisher information, or the network slimming. This method is similar to importance-based filter pruning, but it operates on the layer level instead of the filter level.

How can we evaluate the impact of layer pruning on the network performance? There are different metrics and methods for measuring the performance of the pruned network, such as the accuracy, the compression ratio, the speedup ratio, or the energy efficiency. Here are some examples of performance evaluation methods:

• Accuracy: This metric measures the percentage of correct predictions made by the network on the test data. This metric can reflect the quality of the network output, but it may not capture the trade-off between the accuracy and the complexity of the network.
• Compression ratio: This metric measures the ratio of the number of parameters or operations in the original network to the number of parameters or operations in the pruned network. This metric can reflect the size and complexity reduction of the network, but it may not capture the actual speedup or energy efficiency of the network.
• Speedup ratio: This metric measures the ratio of the training or inference time of the original network to the training or inference time of the pruned network. This metric can reflect the speed improvement of the network, but it may depend on the hardware and software implementation of the network.
• Energy efficiency: This metric measures the ratio of the energy consumption of the original network to the energy consumption of the pruned network. This metric can reflect the energy saving of the network, but it may depend on the hardware and software implementation of the network.

In this subsection, we have introduced the concept and methods of layer pruning, as well as the performance evaluation metrics. In the next section, we will introduce some pruning algorithms and tools that can help you implement and apply pruning to your own CNN models. Don’t miss it!

4. Pruning Algorithms and Tools

Now that we have learned about the different pruning strategies for convolutional neural networks, let’s see how we can implement them in practice. In this section, we will introduce some of the most popular and effective pruning algorithms and tools that can help us prune our CNN models.

One of the simplest and most widely used pruning algorithms is magnitude-based pruning. This algorithm prunes the filters or channels with the smallest magnitude, which are assumed to be the least important or influential for the network. The magnitude can be measured by different criteria, such as the L1 or L2 norm, the absolute value, or the standard deviation. Magnitude-based pruning can be applied iteratively, by pruning a certain percentage of filters or channels at each iteration, and fine-tuning the network after each pruning step.

Another common pruning algorithm is gradient-based pruning. This algorithm prunes the filters or channels with the smallest gradient, which are assumed to be the least sensitive or relevant for the network. The gradient can be computed by different methods, such as the Taylor expansion, the first-order approximation, or the second-order approximation. Gradient-based pruning can also be applied iteratively, by pruning a certain percentage of filters or channels at each iteration, and fine-tuning the network after each pruning step.

A more recent and novel pruning algorithm is the lottery ticket hypothesis. This algorithm proposes that a randomly initialized neural network contains a subnetwork, called the winning ticket, that can achieve the same or better performance than the original network when trained in isolation. The goal of this algorithm is to find the winning ticket by pruning the network progressively, and resetting the remaining weights to their initial values after each pruning step. The lottery ticket hypothesis can potentially reduce the training time and improve the generalization of the network.

One of the most popular and powerful tools for pruning convolutional neural networks is the TensorFlow Model Optimization Toolkit. This is an open-source library that provides a comprehensive set of tools and APIs for model optimization, including pruning, quantization, sparsity, and clustering. The toolkit supports various pruning methods, such as magnitude-based pruning, gradient-based pruning, and structured pruning. The toolkit also supports various pruning schedules, such as constant, polynomial, and exponential. The toolkit can help us easily prune our CNN models with minimal code changes and achieve significant improvements in model size, speed, and accuracy.

In the next section, we will conclude this blog and summarize the main points that we have learned. We will also provide some resources and references for further reading and learning. Stay tuned!

4.1. Magnitude-based Pruning

Magnitude-based pruning is one of the simplest and most widely used pruning algorithms for convolutional neural networks. It works by removing the filters or channels with the smallest magnitude, which are assumed to be the least important or influential for the network. The magnitude can be measured by different criteria, such as the L1 or L2 norm, the absolute value, or the standard deviation.

How does magnitude-based pruning work? The basic steps are as follows:

1. Train the network normally until it reaches a desired performance level.
2. Compute the magnitude of each filter or channel in the network using the chosen criterion.
3. Rank the filters or channels by their magnitude and select the ones with the lowest values.
4. Remove the selected filters or channels from the network and adjust the connections accordingly.
5. Fine-tune the network with a lower learning rate to recover the performance loss.
6. Repeat steps 2-5 until the desired network size or complexity is achieved.

Magnitude-based pruning can be applied iteratively, by pruning a certain percentage of filters or channels at each iteration, and fine-tuning the network after each pruning step. The percentage can be fixed or variable, depending on the pruning schedule. For example, we can prune 10% of the filters or channels at each iteration, or we can prune more filters or channels at the beginning and less at the end.

What are the advantages and disadvantages of magnitude-based pruning? Some of the advantages are:

• It is simple and easy to implement, requiring minimal code changes and computational overhead.
• It is effective and efficient, achieving significant reductions in model size and complexity with minimal performance loss.
• It is general and flexible, applicable to different network architectures and layers, and compatible with different magnitude criteria and pruning schedules.

Some of the disadvantages are:

• It is heuristic and empirical, relying on the assumption that the magnitude of a filter or channel reflects its importance or relevance for the network, which may not always be true.
• It is greedy and irreversible, removing filters or channels permanently without considering their potential contribution or interaction with other parts of the network.
• It is sensitive and unstable, requiring careful tuning of the pruning percentage, the magnitude criterion, and the fine-tuning parameters to avoid performance degradation or collapse.

In the next section, we will introduce another pruning algorithm, gradient-based pruning, which uses a different criterion to select which filters or channels to prune. Stay tuned!

4.2. Gradient-based Pruning

Gradient-based pruning is another common and effective pruning algorithm for convolutional neural networks. It works by removing the filters or channels with the smallest gradient, which are assumed to be the least sensitive or relevant for the network. The gradient can be computed by different methods, such as the Taylor expansion, the first-order approximation, or the second-order approximation.

How does gradient-based pruning work? The basic steps are as follows:

1. Train the network normally until it reaches a desired performance level.
2. Compute the gradient of each filter or channel in the network using the chosen method.
3. Rank the filters or channels by their gradient and select the ones with the lowest values.
4. Remove the selected filters or channels from the network and adjust the connections accordingly.
5. Fine-tune the network with a lower learning rate to recover the performance loss.
6. Repeat steps 2-5 until the desired network size or complexity is achieved.

Gradient-based pruning can also be applied iteratively, by pruning a certain percentage of filters or channels at each iteration, and fine-tuning the network after each pruning step. The percentage can be fixed or variable, depending on the pruning schedule. For example, we can prune 10% of the filters or channels at each iteration, or we can prune more filters or channels at the beginning and less at the end.

What are the advantages and disadvantages of gradient-based pruning? Some of the advantages are:

• It is more accurate and reliable, using the gradient as a measure of the sensitivity or relevance of a filter or channel for the network, which can better reflect its importance or influence.
• It is more adaptive and dynamic, adjusting the pruning criterion according to the network state and the data distribution, which can better cope with the changes and variations in the network and the data.
• It is more compatible and scalable, working well with different network architectures and layers, and supporting different gradient methods and pruning schedules.

Some of the disadvantages are:

• It is more complex and costly, requiring more code changes and computational resources to calculate the gradient of each filter or channel in the network, which can increase the pruning time and overhead.
• It is more dependent and unstable, requiring careful tuning of the gradient method, the pruning percentage, and the fine-tuning parameters to avoid performance degradation or collapse.
• It is more prone and vulnerable, potentially removing filters or channels that are important for the network but have low gradients due to vanishing or exploding gradients, which can harm the network performance or functionality.

In the next section, we will introduce a more recent and novel pruning algorithm, the lottery ticket hypothesis, which proposes that a randomly initialized neural network contains a subnetwork that can achieve the same or better performance than the original network. Stay tuned!

4.3. Lottery Ticket Hypothesis

The lottery ticket hypothesis is a more recent and novel pruning algorithm for convolutional neural networks. It proposes that a randomly initialized neural network contains a subnetwork, called the winning ticket, that can achieve the same or better performance than the original network when trained in isolation. The goal of this algorithm is to find the winning ticket by pruning the network progressively, and resetting the remaining weights to their initial values after each pruning step.

How does the lottery ticket hypothesis work? The basic steps are as follows:

1. Randomly initialize the network and save the initial weights.
2. Train the network normally until it reaches a desired performance level.
3. Prune the network using any pruning criterion, such as magnitude-based or gradient-based.
4. Reset the remaining weights to their initial values.
5. Train the pruned network from scratch with the same hyperparameters as the original network.
6. Repeat steps 3-5 until the pruned network achieves the same or better performance than the original network.

The lottery ticket hypothesis can potentially reduce the training time and improve the generalization of the network, as the winning ticket may have better initialization and regularization properties than the original network.

What are the advantages and disadvantages of the lottery ticket hypothesis? Some of the advantages are:

• It is innovative and intriguing, challenging the conventional wisdom of neural network training and pruning, and offering new insights and perspectives on the network structure and dynamics.
• It is effective and efficient, finding subnetworks that can achieve the same or better performance than the original network with fewer parameters and operations, and less training time.
• It is general and flexible, applicable to different network architectures and layers, and compatible with different pruning criteria and schedules.

Some of the disadvantages are:

• It is complex and costly, requiring multiple rounds of training and pruning, and saving and restoring the initial weights, which can increase the computational and memory overhead.
• It is dependent and unstable, requiring careful tuning of the pruning criterion, the pruning percentage, and the training hyperparameters to find the winning ticket, which may not exist or be unique for every network and dataset.
• It is empirical and heuristic, lacking a theoretical explanation or justification for why the winning ticket exists and how to find it, and relying on empirical observations and experiments.

In the next section, we will introduce one of the most popular and powerful tools for pruning convolutional neural networks, the TensorFlow Model Optimization Toolkit, which provides a comprehensive set of tools and APIs for model optimization, including pruning, quantization, sparsity, and clustering. Stay tuned!

4.4. TensorFlow Model Optimization Toolkit

If you are using TensorFlow as your deep learning framework, you can use the TensorFlow Model Optimization Toolkit to apply pruning and other techniques to your convolutional neural networks. The toolkit provides a set of APIs and tools that allow you to easily implement and customize pruning methods, as well as evaluate and export the pruned models.

The toolkit supports two types of pruning: weight pruning and model pruning. Weight pruning removes individual weights or connections in the network, while model pruning removes entire filters or channels. Both types of pruning can reduce the number of parameters and operations, but model pruning can also reduce the memory footprint and storage requirements.

To use the toolkit, you need to follow these steps:

1. Define your model using the Keras API and compile it with the optimizer and loss function of your choice.
2. Apply the pruning API to your model. You can use the predefined pruning schedules or create your own. You can also specify the pruning parameters, such as the sparsity level, the pruning frequency, and the pruning granularity.
3. Train your model as usual. The pruning API will automatically apply the pruning method during the training process.
4. Evaluate your model on the validation or test data. You can use the provided metrics to measure the sparsity and the performance of your model.
5. Export your model to a standard TensorFlow format or a TensorFlow Lite format. You can also use the provided tools to strip the pruning wrappers and apply further compression techniques.

The toolkit also provides examples and tutorials on how to use pruning and other optimization techniques on various models and tasks, such as image classification, object detection, and natural language processing. You can find them here.

Pruning is a powerful technique that can help you reduce the size and complexity of your convolutional neural networks, while preserving or even improving their performance. By using the TensorFlow Model Optimization Toolkit, you can easily apply pruning and other optimization techniques to your models and deploy them on devices with limited resources. Try it out and see the results for yourself!

5. Conclusion

In this blog, we have learned how to prune convolutional neural networks to reduce the number of filters and channels. We have covered the following topics:

• What is pruning and why is it important?
• Pruning strategies for convolutional neural networks.
• Pruning algorithms and tools.

We have seen that pruning can help us achieve several benefits, such as speeding up the training and inference time, reducing the memory footprint and storage requirements, and improving the generalization and robustness of the network. We have also learned how to apply different pruning methods, such as filter pruning, channel pruning, and layer pruning, and how to use the TensorFlow Model Optimization Toolkit to implement and customize pruning techniques.

Pruning is a powerful technique that can help you optimize your convolutional neural networks and make them more efficient and effective. However, pruning is not the only technique that you can use to improve your models. There are other techniques, such as quantization, distillation, and knowledge transfer, that can also help you reduce the size and complexity of your models, while preserving or even enhancing their performance. You can learn more about these techniques in our next blog. Stay tuned!