I am trying to draw some sort of neural network with additional nodes and connections. I have the following code so far, which I mostly copied from around here.

\documentclass[border=0.125cm]{standalone} \usepackage{tikz}

\tikzset{every neuron/.style={ circle, draw, minimum size=1cm },
neuron missing/.style={ draw=none, scale=3, text height=0.2cm, execute
at begin node=\color{black}$\vdots$ }, }

\begin{tikzpicture}[x=1.5cm, y=1.5cm, >=stealth]

\foreach \m/\l [count=\y] in {1,2,missing,3} \node [every
neuron/.try, neuron \m/.try] (input-\m) at (0,2.5-\y) {};

\foreach \m [count=\y] in {1,2,missing,3} \node [every neuron/.try,
neuron \m/.try ] (hidden-\m) at (2,2.5-\y) {};

\foreach \m [count=\y] in {1,2,missing,3} \node [every neuron/.try,
neuron \m/.try ] (output-\m) at (4,2.5-\y) {};

\foreach \l [count=\i] in {1,2,K} \draw [<-] (input-\i) -- ++(-1,0)
node [above, midway] {$x_\l$};

\foreach \l [count=\i] in {1,2,K} \node [above] at (input-\i.north)

\foreach \l [count=\i] in {1,2,L} \node [above] at (hidden-\i.north)

\foreach \l [count=\i] in {1,2,M} \node [above] at (output-\i.north)

\foreach \l [count=\i] in {1,2,M} \draw [->] (output-\i) -- ++(1,0) node
[above, midway] {$w_\l$};

\foreach \i in {1,...,3} \foreach \j in {1,...,3} \draw [->]
(input-\i) -- (hidden-\j);

\foreach \i in {1,...,3} \foreach \j in {1,...,3} \draw [->]
(hidden-\i) -- (output-\j);

\foreach \l [count=\x from 0] in {Input, Hidden, Softmax} \node
[align=center, above] at (\x*2,2.5) {\l \\ layer};



This leads to the following image:

Code so far

But in the end I would like to achieve the following (or something similar):

Neuro fuzzy architecture

I am not married to the exact design, I just need a well-arranged and comprehensible visual model of the network architecture outlined above.


Like this:

enter image description here

For exercise I rewrote your MWE in more concise form and add missing parts frame around x inputs and L outputs as well nodes L and output neuron. for orientation in nodes see its comments:

\documentclass[tikz, border=0.125cm]{standalone}
\usetikzlibrary{calc, fit, positioning, quotes}% new libraries

  every neuron/.style={circle, draw, minimum size=8mm},
neuron missing/.style={draw=none, scale=3, text height=0.2cm, 
                       execute at begin node=\color{black}$\vdots$}, 
  layer labels/.style={above, align=center}
    \begin{tikzpicture}[x=16mm, y=16mm, >=stealth]
% neuron nodes with part of labels
\foreach \m [count=\y] in {1,2,missing,3}
\foreach \j/\l in {0/I, 2/H, 4/S}
\node [every neuron/.try, neuron \m/.try,
       label=$\l_\y$] (n\j\m) at (\j,2.5-\y) {};
\node [every neuron/.try, neuron \m/.try] (n\j\m) at (\j,2.5-\y) {};
% neuron labels not included in neuron nodes
\foreach \l/\k in {I_K/0, H_L/2, S_M/4}
\node [above] at (n\k3.north) {$\l$};
% inputs
\foreach \l [count=\i] in {1,2,K} 
\draw [<-] (n0\i.west) -- ++(-1.1,0) node (in\i) [above, midway] {$x_\l$};
\node (input) [draw, inner ysep=2mm, yshift=-2mm, fit=(in1) (in3)] {};
% w and L outputs
\foreach \l [count=\i] in {1,2,M}
\draw [->] (n4\i.east) -- ++(1.6,0) 
    node (wout\i) [above, midway] {$x_\l$}
    node (Lout\i) [right, draw, minimum size=8mm, label=$L_\i$] {};% Local Model
\node [neuron missing] at ($(Lout2)!0.5!(Lout3)$) {};
\node (woutput) [draw, inner ysep=2mm, yshift=-2mm, fit=(wout1) (wout3)] {};
% output
\node (output) [every neuron,right=16mm] at ($(Lout1.east)!0.5!(Lout3.east)$) {};
\draw [->] (output.east) to["$\hat{y}$"] ++(1.1,0);
% neurons interconection
 \foreach \i in {1,2,3}
 \foreach \j in {1,2,3}
\draw [->] (n0\i) -- (n2\j);
\draw [->] (n2\i) -- (n4\j);
\foreach \j [count=\i] in {1,2,M}
\draw [->] (Lout\i.east) to ["$\hat{y}_\j$"] (output);
% neuron layers labels
\foreach \l [count=\x from 0] in {Input, Hidden, Softmax} 
\node [layer labels] at (\x*2,2.2) {\l \\ layer};
\node [layer labels] at (6,2.2)    {Local \\ Model};
\node [layer labels] at (8,2.2)    {Output \\ Agregation};
% x-fit L-fit conections
\draw[dashed,->] (input.south)   -- ++ (0,-1)   -| (Lout3)  
                        node [pos=0.25,above] {$\underline{x}$};
\draw[dashed,->] (woutput.south) -- ++ (0,-0.5) -| (output) 
                        node [pos=0.75,right] {$\underline{w}$};
  • The second box x's should be w's and the hidden and softmax layer neurons should be labeled H_1, H_2, ... , H_L, (not I_L) and S_1, S_2, ... , S_M (not I_M). And there is a minor type, it should be "Output aggregation". – mwater Apr 8 '17 at 10:23
  • 1
    @mwater: sorry, I overlooked this. Now is corrected. – Zarko Apr 8 '17 at 10:30

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.