dnCNNLayers

获取卷积神经网络层

在页面中崩溃

句法

层= dncnnlayers

layers = dncnnlayers（'NetworkDepth'，networkDepth）

描述

例子

层= dnCNNLayers为灰度图像的Denoising卷积神经网络（DNCNN）的返回层。

This function requires that you have Deep Learning Toolbox™.

层= dnCNNLayers('NetworkDepth',networkDepth）与networkDepth卷积层的数量。

例子

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Get Layers of Image Denoising Network

此示例使用：

Open Live Script

Get layers of the image denoising convolutional neural network, 'DnCNN'. Request the default number of layers, which returns 20 convolution layers.

层= dncnnlayers

层= 1 x59层阵列层:1 ' InputLayer' Image Input 50x50x1 images 2 'Conv1' Convolution 64 3x3x1 convolutions with stride [1 1] and padding [1 1 1 1] 3 'ReLU1' ReLU ReLU 4 'Conv2' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 5 'BNorm2' Batch Normalization Batch normalization with 64 channels 6 'ReLU2' ReLU ReLU 7 'Conv3' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 8 'BNorm3' Batch Normalization Batch normalization with 64 channels 9 'ReLU3' ReLU ReLU 10 'Conv4' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 11 'BNorm4' Batch Normalization Batch normalization with 64 channels 12 'ReLU4' ReLU ReLU 13 'Conv5' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 14 'BNorm5' Batch Normalization Batch normalization with 64 channels 15 'ReLU5' ReLU ReLU 16 'Conv6' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 17 'BNorm6' Batch Normalization Batch normalization with 64 channels 18 'ReLU6' ReLU ReLU 19 'Conv7' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 20 'BNorm7' Batch Normalization Batch normalization with 64 channels 21 'ReLU7' ReLU ReLU 22 'Conv8' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 23 'BNorm8' Batch Normalization Batch normalization with 64 channels 24 'ReLU8' ReLU ReLU 25 'Conv9' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 26 'BNorm9' Batch Normalization Batch normalization with 64 channels 27 'ReLU9' ReLU ReLU 28 'Conv10' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 29 'BNorm10' Batch Normalization Batch normalization with 64 channels 30 'ReLU10' ReLU ReLU 31 'Conv11' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 32 'BNorm11' Batch Normalization Batch normalization with 64 channels 33 'ReLU11' ReLU ReLU 34 'Conv12' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 35 'BNorm12' Batch Normalization Batch normalization with 64 channels 36 'ReLU12' ReLU ReLU 37 'Conv13' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 38 'BNorm13' Batch Normalization Batch normalization with 64 channels 39 'ReLU13' ReLU ReLU 40 'Conv14' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 41 'BNorm14' Batch Normalization Batch normalization with 64 channels 42 'ReLU14' ReLU ReLU 43 'Conv15' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 44 'BNorm15' Batch Normalization Batch normalization with 64 channels 45 'ReLU15' ReLU ReLU 46 'Conv16' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 47 'BNorm16' Batch Normalization Batch normalization with 64 channels 48 'ReLU16' ReLU ReLU 49 'Conv17' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 50 'BNorm17' Batch Normalization Batch normalization with 64 channels 51 'ReLU17' ReLU ReLU 52 'Conv18' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 53 'BNorm18' Batch Normalization Batch normalization with 64 channels 54 'ReLU18' ReLU ReLU 55 'Conv19' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 56 'BNorm19' Batch Normalization Batch normalization with 64 channels 57 'ReLU19' ReLU ReLU 58 'Conv20' Convolution 1 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1] 59 'FinalRegressionLayer' Regression Output mean-squared-error

您可以通过提供这些图层和DeNoisingImagedAtatastore至火车网（深度学习工具箱）。

输入参数

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`networkDepth`-卷积层的数量
`20`（默认）|正整数

卷积层的数量，指定为一个正值大于或等于3的正整数。

数据类型：单身的|双倍的|int8|int16|int32|UINT8|uint16|UINT32

输出参数

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`层`-Network layers
vector of`Layer`对象

Denoising convolutional neural network layers, returned as a vector ofLayer（深度学习工具箱）对象。

Tips

The DnCNN network can detect noise and other high-frequency image artifacts. For example, you can train the DnCNN network to increase image resolution or remove JPEG compression artifacts. The exampleJPEG图像使用深度学习显示如何训练DNCNN以减少图像中的JPEG压缩工件。