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Create 2-D convolutional layer

`convlayer = convolution2dLayer(filterSize,numFilters)`

`convlayer = convolution2dLayer(filterSize,numFilters,Name,Value)`

returns
a layer for 2-D
convolution.`convlayer`

= convolution2dLayer(`filterSize`

,`numFilters`

)

returns
the convolutional layer, with additional options specified by one
or more `convlayer`

= convolution2dLayer(`filterSize`

,`numFilters`

,`Name,Value`

)`Name,Value`

pair arguments.

The default for the initial weights is a Gaussian distribution with mean 0 and standard deviation 0.01. The default for the initial bias is 0. You can manually change the initialization for the weights and bias. See Specify Initial Weight and Biases in Convolutional Layer.

[1] LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard,
R.E., Hubbard, W., Jackel, L.D., et al. ''Handwritten Digit Recognition
with a Back-propagation Network.'' In *Advances of Neural
Information Processing Systems*, 1990.

[2] LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. ''Gradient-based
Learning Applied to Document Recognition.'' *Proceedings
of the IEEE.* Vol 86, pp. 2278–2324, 1998.

[3] Murphy, K. P. *Machine Learning: A Probabilistic
Perspective*. Cambridge, Massachusetts: The MIT Press,
2012.

`averagePooling2dLayer`

| `Convolution2DLayer`

| `maxPooling2dLayer`

| `reluLayer`

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