Receptive Field Calculator

We built a tool for calculating the receptive field of convolutional filters:  #deeplearning

  • A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly.
  • You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations.
  • For example, if the first convolutional layer has a receptive field of 3×3 then it’s effective receptive field is also 3×3 since it operates directly on the input.
  • However if the second layer of a convolutional network also has a 3×3 filter, then it’s (local) receptive field is 3×3, but it’s effective receptive field is 5×5.

A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly. You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations. For example, if the first convolutional layer has a receptive field of 3×3 then it’s effective receptive field is also 3×3 since it operates directly on the input.
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A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

Understanding Convolutional Neural Networks Part 1  via @kdnuggets #DataScience #deeplearning

  • The filters on the first layer convolve around the input image and “activate” (or compute high values) when the specific feature it is looking for is in the input volume.
  • Remember, what we have to do is multiply the values in the filter with the original pixel values of the image.
  • As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing element wise multiplications ).
  • Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image.
  • When a computer sees an image (takes an image as input), it will see an array of pixel values.


Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

Continue reading “A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1”

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

A Beginner’s Guide To Understanding Convolutional #NeuralNetworks Part 1  #DeepLearning

  • The filters on the first layer convolve around the input image and “activate” (or compute high values) when the specific feature it is looking for is in the input volume.
  • Remember, what we have to do is multiply the values in the filter with the original pixel values of the image.
  • As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing element wise multiplications ).
  • Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image.
  • When a computer sees an image (takes an image as input), it will see an array of pixel values.


Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

Continue reading “A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1”