Stride

Definition

In the context of convolutional neural networks (CNNs), stride refers to the number of pixels by which the filter or kernel moves across the input image during convolution. This movement affects how the output dimensions are calculated and influences the level of feature extraction from the input data. A larger stride results in downsampling of the feature map, while a smaller stride retains more spatial information from the input.

5 Must Know Facts For Your Next Test

1. Stride can be set to various values, typically 1 or 2, depending on how much downsampling is desired during convolution.
2. Using a larger stride reduces the size of the output feature map, which can help minimize computational load in deep networks.
3. When stride is set to 1, the filter moves one pixel at a time, capturing fine details in the input.
4. The choice of stride impacts the receptive field of neurons in deeper layers, affecting how much context each neuron considers.
5. Strides are applied both horizontally and vertically, meaning that a stride of (2,2) means moving 2 pixels down and 2 pixels right at each step.

Review Questions

• How does changing the stride value affect the output feature map in a convolutional neural network?
  • Changing the stride value directly influences the dimensions of the output feature map. A larger stride means that the filter covers more ground and therefore produces a smaller output size, effectively downsampling the feature map. Conversely, using a smaller stride captures more spatial information but results in a larger output size. This trade-off affects not only computational efficiency but also the amount of detail retained from the input image.
• Evaluate how different stride settings could impact model performance and generalization in a convolutional neural network.
  • Different stride settings can significantly impact model performance and generalization. A small stride captures detailed features but may lead to overfitting due to increased complexity and larger feature maps. In contrast, a larger stride simplifies the model by reducing dimensions, which can improve generalization but may result in loss of crucial information. Thus, finding an optimal balance in stride settings is essential for effective feature extraction without compromising performance.
• Synthesize how strides interact with other architectural choices in convolutional neural networks to affect overall model design.
  • Strides interact with several architectural choices such as filter size, pooling layers, and overall depth of the network. When designing a CNN, choosing an appropriate stride can complement filter sizes; for instance, using large filters with large strides may quickly reduce spatial dimensions, potentially leading to loss of relevant information. Additionally, how strides work alongside pooling layers affects feature abstraction levels. Balancing these components can lead to a well-structured model that efficiently captures important features while maintaining manageable complexity.

Related terms

Convolution:

A mathematical operation that combines two functions to produce a third function, often used in CNNs to extract features from input data.

Pooling:

A technique used in CNNs to reduce the spatial size of the feature maps, helping to decrease computation and control overfitting.

Kernel: A small matrix used in convolution operations that slides over the input image to detect features like edges or textures.