supported networks, layers, boards, and tools -凯发k8网页登录

an image input layer inputs 2-d images to a network and applies data normalization. the normalization options zero-center and zscore can run on hardware if the compile method hardwarenormalization argument is enabled and the input data is of single data type. if the hardwarenormalization option is not enabled or the input data type is int8 the normalization runs in software. normalization specified using a function handle is not supported. see image input layer normalization hardware implementation. when the normalization property is set to none the activations function cannot be used for the imageinputlayer.

a 2-d convolutional layer applies sliding convolutional filters to the input.

when generating code for a network using this layer, these limitations apply:

yes

a 2-d grouped convolutional layer separates the input channels into groups and applies sliding convolutional filters. use grouped convolutional layers for channel-wise separable (also known as depth-wise separable) convolution.

code generation is now supported for a 2-d grouped convolution layer that has the numgroups property set as 'channel-wise'.

when generating code for a network using this layer, these limitations apply:

yes

a transposed 2-d convolution layer upsamples feature maps.

when generating code for a network using this layer, these limitations apply:

yes

fullyconnectedlayer

a fully connected layer multiplies the input by a weight matrix, and then adds a bias vector.

when generating code for a network using this layer, these limitations apply:

yes

a relu layer performs a threshold operation to each element of the input where any value less than zero is set to zero.

a relu layer is supported only when it is preceded by any of these layers:

yes

a leaky relu layer performs a threshold operation where any input value less than zero is multiplied by a fixed scalar.

a leaky relu layer is supported only when it is preceded by any of these layers:

yes

a clipped relu layer performs a threshold operation where any input value less than zero is set to zero and any value above the clipping ceiling is set to that clipping ceiling value.

a clipped relu layer is supported only when it is preceded by any of these layers:

yes

a hyperbolic tangent (tanh) activation layer applies the tanh function on the layer inputs.

a batch normalization layer normalizes each input channel across a mini-batch.

a batch normalization layer is supported when preceded by an image input layer or convolution layer.

yes

a channel-wise local response (cross-channel) normalization layer carries out channel-wise normalization.

the windowchannelsize must be in the range of 3-9 for code generation.

yes. runs as single datatype in hw.

a dropout layer randomly sets input elements to zero within a given probability.

yes

(image processing toolbox)

a 2-d resize layer resizes 2-d input by a scale factor, to a specified height and width, or to the size of a reference input feature map.

when generating code for a network using this layer, these limitations apply:

a max pooling layer performs downsampling by dividing the layer input into rectangular pooling regions and computing the maximum of each region.

when generating code for a network using this layer, these limitations apply:

hasunpoolingoutputs is supported. when this parameter is enabled, these limitations apply for code generation for this layer:

yes

no, when hasunpoolingoutputs is enabled.

a max unpooling layer unpools the output of a max pooling layer.

an average pooling layer performs downsampling by dividing the layer input into rectangular pooling regions and computing the average values of each region.

when generating code for a network using this layer, these limitations apply:

yes

a global average pooling layer performs downsampling by computing the mean of the height and width dimensions of the input.

when generating code for a network using this layer, these limitations apply:

yes

an addition layer adds inputs from multiple neural network layers element-wise.

you can now generated code for this layer with int8 data type when the layer is combined with a leaky relu or clipped relu layer.

when generating code for a network using this layer, these limitations apply:

a depth concatenation layer takes inputs that have the same height and width and concatenates them along the third dimension (the channel dimension).

when generating code for a network using this layer, these limitations apply:

yes

hw

an lstm layer learns long-term dependencies between time steps in time series and sequence data. the layer performs additive interactions, which can help improve gradient flow over long sequences during training.

when generating code for a network using this layer, these limitations apply:

no

hw

a gru layer is an rnn layer that learns dependencies between time steps in time series and sequence data.

when generating code for a network using this layer, these limitations apply:

a softmax layer applies a softmax function to the input.

if the softmax layer is implemented in hardware:

yes. runs as single datatype in sw.

a classification layer computes the cross-entropy loss for multiclass classification issues that have mutually exclusive classes.

yes

a regression layer computes the half mean squared error loss for regression problems.

yes

a sigmoid layer applies a sigmoid function to the input.

when the data type is single the sigmoid layer is implemented in the custom module of the deep learning processor configuration. when generating code for a network using this layer, with single data type these limitations apply:

runs as single datatype in sw.

yes. when the data type is int8 the sigmoid layer is implemented in the fully connected (fc) module of the deep learning processor configuration. when generating code for a network using this layer, with int8 data type these limitations apply:

flatten activations into 1-d layers assuming c-style (row-major) order.

a nnet.keras.layer.flattencstylelayer is supported only when it is followed by a fully connected layer.

yes

zero padding layer for 2-d input.

a nnet.keras.layer.zeropadding2dlayer is supported only when it is preceded by a convolution layer or a maxpool layer. zero padding layer is supported when followed by a grouped convolution layer.

yes