gensim

(To be removed) Generate Simulink block for shallow neural network simulation

gensim will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

gensim(net,st)

Description

gensim(net,st) creates a Simulink^® system containing a block that simulates neural network net with a sampling time of st.

If net has no input or layer delays (net.numInputDelays and net.numLayerDelays are both 0), you can use –1 for st to get a network that samples continuously.

gensim does not support deep learning networks such as convolutional or LSTM networks. For more information on code generation for deep learning, see Generate Code and Deploy Deep Neural Networks.

For more information on gensim, at the MATLAB command prompt, enter help network/gensim.

Examples

Generate a Simulink Block for a Feedforward Network

This example shows how to generate a Simulink block for a feedforward network.

Create a feedforward network using the data from the simple fit data set and generate the Simulink block.

[x,t] = simplefit_dataset;
net = feedforwardnet(10);
net = train(net,x,t)
gensim(net)

Input Arguments

net — Input network

network

Input network, specified as a network object. To create a network object, use for example, feedforwardnet or narxnet.

st — Sample time

-1 (default) | scalar | vector

Specify the sample time as a value other than -1. For more information, see Specify Sample Time (Simulink).

More About

Neural Network Simulink Block Library

The Deep Learning Toolbox™ product provides a set of blocks that you can use to build neural networks using Simulink, or that the function gensim can use to generate the Simulink version of any network you have created using MATLAB^®.

To open the Deep Learning Toolbox block library, at the MATLAB Command Window, enter:

neural

This command opens a library window that contains five blocks. Each of these blocks contains additional blocks.

Transfer Function Blocks

Double-click the Transfer Functions block in the Neural library window to open a window that contains several transfer function blocks.

Each of these blocks takes a net input vector and generates a corresponding output vector whose dimensions are the same as the input vector.

Net Input Blocks

Double-click the Net Input Functions block in the Neural library window to open a window that contains two net-input function blocks.

Each of these blocks takes any number of weighted input vectors, weight layer output vectors, and bias vectors, and returns a net-input vector.

Weight Blocks

Double-click the Weight Functions block in the Neural library window to open a window that contains four weight function blocks.

Each of these blocks takes a neuron weight vector and applies it to an input vector (or a layer output vector) to get a weighted input value for a neuron.

These blocks expect the neuron weight vector to be a column vector. This is because Simulink signals can be column vectors, but cannot be matrices or row vectors.

As a result of this limitation, to implement a weight matrix going to a layer with S neurons, you must create S weight function blocks (one for each row).

This contrasts with the other two kinds of blocks. Only one net input function and one transfer function block are required for each layer.

Processing Blocks

Double-click the Processing Functions block in the Neural library window to open a window that contains processing blocks and their corresponding reverse-processing blocks.

You can use each of these blocks to preprocess inputs and postprocess outputs.

Version History

Introduced before R2006a

R2026a: gensim will be removed

gensim will be removed in a future release.

To learn more about Simulink and code generation for neural networks, see these examples and topics:

• List of Deep Learning Layer Blocks and Subsystems

• Implement Unsupported Deep Learning Layer Blocks

• Time Series Prediction in Simulink Using Deep Learning Network

• Acceleration for Simulink Deep Learning Models

• Code Generation for Deep Learning Networks

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.