<snapdata remixID="15123766"><project name="my project" app="Snap! 11.0.8, https://snap.berkeley.edu" version="2"><notes>Building kit for artificial neural networks. Use a single "Layer" sprite to make a classical Rosenblatt perceptron. Duplicate the Layer sprite - several times - and adjust the receivers in the broadcast blocks to create deep neural networks. User the setup script to customize the network&apos;s topology and learning rate.</notes><thumbnail>data:image/png;base64,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</thumbnail><scenes select="1"><scene name="my project"><notes>Building kit for artificial neural networks. Use a single "Layer" sprite to make a classical Rosenblatt perceptron. Duplicate the Layer sprite - several times - and adjust the receivers in the broadcast blocks to create deep neural networks. User the setup script to customize the network&apos;s topology and learning rate.</notes><hidden></hidden><headers></headers><code></code><blocks><block-definition s="plot bars %&apos;data&apos; fill %&apos;width&apos; center %&apos;switch&apos;" type="command" category="pen"><comment x="0" y="0" w="120" collapsed="false">draw a list of numbers as  vertical lines distributed evenly across the stage.</comment><header></header><code></code><translations>de:male Balken _ gefüllt _ zentriert _&#xD;</translations><inputs><input type="%l"></input><input type="%n">0.8<options>single=0.8&#xD;pan=1&#xD;overlap=1.2</options></input><input type="%b">false</input></inputs><script><block s="clear"></block><block s="doDeclareVariables"><list><l>slice</l><l>pos</l><l>pen size</l></list></block><block s="doSetVar"><l>pos</l><block s="getPosition"></block></block><block s="doSetVar"><l>slice</l><block s="reportQuotient"><block s="reportAttributeOf"><l><option>width</option></l><block s="reportGet"><l><option>stage</option></l></block></block><block s="reportListAttribute"><l><option>length</option></l><block var="data"/></block></block></block><block s="doSetVar"><l>pen size</l><block s="getPenAttribute"><l><option>size</option></l></block></block><block s="setSize"><block s="reportVariadicProduct"><list><block var="slice"/><block var="width"/></list></block></block><block s="setXPosition"><block s="reportVariadicSum"><list><block s="reportAttributeOf"><l><option>left</option></l><block s="reportGet"><l><option>stage</option></l></block></block><block s="reportQuotient"><block var="slice"/><l>2</l></block></list></block></block><block s="doWarp"><script><block s="doForEach"><l>item</l><block var="data"/><script><block s="setYPosition"><block s="reportIfElse"><block var="switch"/><block s="reportQuotient"><block var="item"/><l>-2</l></block><block s="reportAttributeOf"><l><option>bottom</option></l><block s="reportGet"><l><option>stage</option></l></block></block></block></block><block s="down"></block><block s="changeYPosition"><block var="item"/></block><block s="up"></block><block s="changeXPosition"><block var="slice"/></block></script></block></script></block><block s="doGotoObject"><block var="pos"/></block><block s="setSize"><block var="pen size"/></block></script></block-definition></blocks><primitives></primitives><stage name="Stage" width="480" height="360" costume="0" color="255,255,255,1" tempo="60" threadsafe="false" penlog="false" volume="100" pan="0" lines="round" ternary="false" hyperops="true" codify="false" inheritance="true" sublistIDs="false" 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</pentrails><costumes><list struct="atomic" id="93"></list></costumes><sounds><list struct="atomic" id="94"></list></sounds><variables></variables><blocks></blocks><scripts></scripts><sprites select="1"><sprite name="ANN" idx="1" x="-150.00000000621344" y="-137.99999998891298" heading="90" scale="1" volume="100" pan="0" rotation="1" draggable="true" hidden="true" costume="0" color="80,80,80,1" pen="tip" id="99"><costumes><list struct="atomic" id="100"></list></costumes><sounds><list struct="atomic" id="101"></list></sounds><blocks></blocks><variables><variable name="target"><l>1</l></variable><variable name="log" transient="true"/><variable name="errors"><l>0</l></variable></variables><scripts><comment x="20" y="19.999999999999943" w="399" collapsed="false">Sample Data with &quot;Rules&quot;:&#xD;the first 2 columns represent the data&apos;s &quot;features&quot;, the 3rd column is the expected result.</comment><script x="20" y="85.99999999999989"><block s="reportNewList"><list><block s="reportNewList"><list><l>0</l><l>0</l><l>0</l></list></block><block s="reportNewList"><list><l>1</l><l>0</l><l>0</l></list></block><block s="reportNewList"><list><l>0</l><l>1</l><l>0</l></list></block><block s="reportNewList"><list><l>1</l><l>1</l><l>1</l></list></block></list><comment w="90" collapsed="true">&quot;all&quot; -- AND</comment></block></script><script x="20" y="123"><block s="reportNewList"><list><block s="reportNewList"><list><l>0</l><l>0</l><l>1</l></list></block><block s="reportNewList"><list><l>1</l><l>0</l><l>0</l></list></block><block s="reportNewList"><list><l>0</l><l>1</l><l>0</l></list></block><block s="reportNewList"><list><l>1</l><l>1</l><l>1</l></list></block></list><comment w="116" collapsed="true">&quot;all er nuthin&quot; - XNOR</comment></block></script><script x="20" y="159.99999999999963"><block s="reportNewList"><list><block s="reportNewList"><list><l>0</l><l>0</l><l>0</l></list></block><block s="reportNewList"><list><l>1</l><l>0</l><l>1</l></list></block><block s="reportNewList"><list><l>0</l><l>1</l><l>1</l></list></block><block s="reportNewList"><list><l>1</l><l>1</l><l>1</l></list></block></list><comment w="90" collapsed="true">&quot;some&quot; - OR</comment></block></script><script x="20" y="196.99999999999955"><block s="doSetVar"><l>data</l><block s="reportNewList"><list><block s="reportNewList"><list><l>0</l><l>0</l><l>0</l></list></block><block s="reportNewList"><list><l>1</l><l>0</l><l>1</l></list></block><block s="reportNewList"><list><l>0</l><l>1</l><l>1</l></list></block><block s="reportNewList"><list><l>1</l><l>1</l><l>0</l></list></block></list><comment w="90" collapsed="true">&quot;either&quot; - XOR</comment></block></block></script><script x="20" y="259"><block s="doDeclareVariables"><list><l>setup</l></list></block><block s="doSetVar"><l>setup</l><block s="reportNewList"><list><l>2</l><l>2</l><l>1</l></list></block><comment w="259.4826171874997" collapsed="true">layer sizes - # of neurons not counting bias</comment></block><block s="doFor"><l>i</l><l>1</l><block s="reportDifference"><block s="reportListAttribute"><l><option>length</option></l><block var="setup"/></block><l>1</l></block><script><block s="doBroadcast"><l>initialize</l><list><block s="reportJoinWords"><list><l>Layer</l><block var="i"/></list></block><block s="reportListItem"><block s="reportNewList"><list><block s="reportVariadicSum"><list><block var="i"/><l>1</l></list></block><block var="i"/></list></block><block var="setup"/></block></list></block></script></block><block s="doSetVar"><l>learning rate</l><l>0.5</l></block></script><script x="20" y="441.49999999999966"><block s="doSetVar"><l>log</l><block s="reportNewList"><list></list></block></block><block s="doRepeat"><l>2000</l><script><block s="doSetVar"><l>errors</l><l>0</l></block><block s="doForEach"><l>input</l><block s="reportListAttribute"><l><option>shuffled</option></l><block var="data"/></block><script><block s="doSetVar"><l>target</l><block s="reportListItem"><l><option>last</option></l><block var="input"/></block></block><block s="doBroadcastAndWait"><l>forward</l><list><l>Layer1</l><block var="input"/></list></block></script></block><block s="doAddToList"><block var="errors"/><block var="log"/></block><custom-block s="plot bars %l fill %n center %b"><block s="reportVariadicProduct"><list><block var="log"/><l>100</l></list></block><l>0.8</l><l><bool>false</bool></l></custom-block></script></block></script><script x="20" y="664.9999999999989"><block s="receiveMessage"><l>forward</l><list><l>output</l></list></block><block s="doDeclareVariables"><list><l>error</l></list></block><block s="doSetVar"><l>error</l><block s="reportDifference"><block var="target"/><block var="output"/></block></block><block s="doChangeVar"><l>errors</l><block s="reportVariadicSum"><block s="reportMonadic"><l><option>abs</option></l><block var="error"/></block></block></block><block s="doBroadcastAndWait"><l>backpropagate</l><list><l>Layer2</l><block var="error"/></list></block></script></scripts></sprite><sprite name="Layer1" idx="2" x="-66" y="-143" heading="90" scale="1" volume="100" pan="0" rotation="1" draggable="true" hidden="true" costume="0" color="155.54999999999998,9.333000000000007,0,1" pen="tip" id="359"><costumes><list struct="atomic" id="360"></list></costumes><sounds><list struct="atomic" id="361"></list></sounds><blocks></blocks><variables><variable name="weights"><list id="364"><item><list struct="atomic" id="365">6.135686620606759,-4.193357547838136,-4.194463605545711</list></item><item><list struct="atomic" id="366">2.3776718865235766,-6.18887849037937,-6.281324501515148</list></item></list></variable><variable name="inputs"><list struct="atomic" id="367">0,1,1</list></variable></variables><scripts><script x="20" y="20"><block s="receiveMessage"><l>initialize</l><list><l>shape</l></list></block><block s="doSetVar"><l>weights</l><block s="reportRandom"><l>-1.0</l><block s="reportReshape"><l>1</l><list><block s="reportListItem"><l>1</l><block var="shape"/></block><block s="reportVariadicSum"><list><block s="reportListItem"><l>2</l><block var="shape"/></block><l>1</l></list></block></list></block></block></block></script><script x="20" y="142.66666666666669"><block s="receiveMessage"><l>forward</l><list><l>sample</l></list></block><block s="doSetVar"><l>inputs</l><block var="sample"/></block><block s="doBroadcastAndWait"><l>forward</l><list><l>Layer2</l><block s="reportMap"><block s="reifyReporter"><autolambda><block s="reportMonadic"><l><option>sigmoid</option></l><block s="reportVariadicSum"><block s="reportVariadicProduct"><list><block s="reportCONS"><l>1</l><block var="inputs"/></block><l></l></list></block></block></block></autolambda><list></list></block><block var="weights"/></block></list></block></script><script x="20" y="324"><block s="receiveMessage"><l>backpropagate</l><list><l>delta</l></list></block><block s="doBroadcastAndWait"><l>backpropagate</l><list><l>ANN</l><block s="reportVariadicSum"><block s="reportVariadicProduct"><list><block s="reportMap"><block s="reifyReporter"><autolambda><block s="reportVariadicProduct"><list><block var="inputs"/><block s="reportDifference"><l>1</l><block var="inputs"/></block><l></l></list></block></autolambda><list></list></block><block var="delta"/></block><block s="reportMap"><block s="reifyReporter"><autolambda><block s="reportCDR"><l/></block></autolambda><list></list></block><block var="weights"/></block></list></block></block></list></block><block s="doChangeVar"><l>weights</l><block s="reportMap"><block s="reifyReporter"><autolambda><block s="reportVariadicProduct"><list><block s="reportCONS"><l>1</l><block var="inputs"/></block><block var="learning rate"/><l></l></list></block></autolambda><list></list></block><block var="delta"/></block></block></script></scripts></sprite><sprite name="Layer2" idx="3" x="-23" y="-144" heading="90" scale="1" volume="100" pan="0" rotation="1" draggable="true" hidden="true" costume="0" color="155.54999999999998,9.333000000000007,0,1" pen="tip" id="499"><costumes><list struct="atomic" id="500"></list></costumes><sounds><list struct="atomic" id="501"></list></sounds><blocks></blocks><variables><variable name="weights"><list id="504"><item><list struct="atomic" id="505">-3.502623569906163,7.9611294648188835,-8.764424385323942</list></item></list></variable><variable name="inputs"><list struct="atomic" id="506">0.870667936368916,0.019896967093023164</list></variable></variables><scripts><script x="20" y="20"><block s="receiveMessage"><l>initialize</l><list><l>shape</l></list></block><block s="doSetVar"><l>weights</l><block s="reportRandom"><l>-1.0</l><block s="reportReshape"><l>1</l><list><block s="reportListItem"><l>1</l><block var="shape"/></block><block 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