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构建深度神经网络

使用 MATLAB® 代码或以交互方式使用深度网络设计器为序列和表格数据构建网络

通过从头开始定义网络架构,为分类、回归和预测等任务创建新的深度网络。使用 MATLAB 或以交互方式使用深度网络设计器构建网络。

对于大多数任务,您可以使用内置层。如果没有您的任务所需的内置层,则可以定义您自己的自定义层。您可以使用自定义输出层指定自定义损失函数,并定义具有可学习参数和状态参数的自定义层。定义自定义层后,您可以检查该层是否有效,是否与 GPU 兼容,以及是否输出正确定义的梯度。要查看支持的层的列表,请参阅深度学习层列表

对于层图不支持的模型,您可以将自定义模型定义为函数。要了解详细信息,请参阅定义自定义训练循环、损失函数和网络

App

深度网络设计器设计、可视化和训练深度学习网络

函数

全部展开

输入层

sequenceInputLayerSequence input layer
featureInputLayerFeature input layer (自 R2020b 起)

循环层

lstmLayerLong short-term memory (LSTM) layer for recurrent neural network (RNN)
bilstmLayerBidirectional long short-term memory (BiLSTM) layer for recurrent neural network (RNN)
gruLayerGated recurrent unit (GRU) layer for recurrent neural network (RNN) (自 R2020a 起)
lstmProjectedLayerLong short-term memory (LSTM) projected layer for recurrent neural network (RNN) (自 R2022b 起)
gruProjectedLayerGated recurrent unit (GRU) projected layer for recurrent neural network (RNN) (自 R2023b 起)

变换器层

selfAttentionLayerSelf-attention layer (自 R2023a 起)
positionEmbeddingLayerPosition embedding layer (自 R2023b 起)
sinusoidalPositionEncodingLayerSinusoidal position encoding layer (自 R2023b 起)
embeddingConcatenationLayerEmbedding concatenation layer (自 R2023b 起)
indexing1dLayer1-D indexing layer (自 R2023b 起)

神经 ODE 层

neuralODELayerNeural ODE layer (自 R2023b 起)

卷积层、注意力层和全连接层

convolution1dLayer1-D convolutional layer (自 R2021b 起)
transposedConv1dLayerTransposed 1-D convolution layer (自 R2022a 起)
selfAttentionLayerSelf-attention layer (自 R2023a 起)
fullyConnectedLayerFully connected layer

激活层和丢弃层

reluLayer修正线性单元 (ReLU) 层
leakyReluLayerLeaky Rectified Linear Unit (ReLU) layer
clippedReluLayerClipped Rectified Linear Unit (ReLU) layer
eluLayerExponential linear unit (ELU) layer
tanhLayer双曲正切 (tanh) 层
swishLayerSwish layer (自 R2021a 起)
geluLayerGaussian error linear unit (GELU) layer (自 R2022b 起)
sigmoidLayerSigmoid layer (自 R2020b 起)
softmaxLayerSoftmax 层
dropoutLayer丢弃层
functionLayerFunction layer (自 R2021b 起)

归一化层

batchNormalizationLayerBatch normalization layer
groupNormalizationLayerGroup normalization layer (自 R2020b 起)
instanceNormalizationLayerInstance normalization layer (自 R2021a 起)
layerNormalizationLayerLayer normalization layer (自 R2021a 起)
crossChannelNormalizationLayer Channel-wise local response normalization layer

池化层

maxPooling1dLayer1-D max pooling layer (自 R2021b 起)
averagePooling1dLayer1-D average pooling layer (自 R2021b 起)
globalMaxPooling1dLayer1-D global max pooling layer (自 R2021b 起)
globalAveragePooling1dLayer1-D global average pooling layer (自 R2021b 起)

组合层

additionLayerAddition layer
multiplicationLayerMultiplication layer (自 R2020b 起)
concatenationLayerConcatenation layer
depthConcatenationLayerDepth concatenation layer

数据操作

sequenceFoldingLayer(Not recommended) Sequence folding layer
sequenceUnfoldingLayer(Not recommended) Sequence unfolding layer
flattenLayerFlatten layer

输出层

classificationLayer分类输出层
regressionLayer回归输出层
layerGraph(Not recommended) Graph of network layers for deep learning
plot绘制神经网络架构
addLayersAdd layers to neural network
removeLayersRemove layers from neural network
replaceLayerReplace layer in neural network
connectLayersConnect layers in neural network
disconnectLayersDisconnect layers in neural network
DAGNetwork用于深度学习的有向无环图 (DAG) 网络
isequalCheck equality of neural networks (自 R2021a 起)
isequalnCheck equality of neural networks ignoring NaN values (自 R2021a 起)
analyzeNetworkAnalyze deep learning network architecture
dlnetworkDeep learning neural network (自 R2019b 起)
addInputLayerAdd input layer to network (自 R2022b 起)
summaryPrint network summary (自 R2022b 起)
initializeInitialize learnable and state parameters of a dlnetwork (自 R2021a 起)
networkDataLayoutDeep learning network data layout for learnable parameter initialization (自 R2022b 起)
checkLayerCheck validity of custom or function layer

主题

内置层

自定义层