# Training Network stopping automatically after 3 iteration without showing any error.

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Aravind Mallemputi on 30 Nov 2021
Commented: Image Analyst on 5 Apr 2022
tspan = 0:0.001:10;
y0 = 0;
[t,y] = ode45(@(t,y) t^2+2, tspan, y0);
T=t(1:0.9*end)
Y=y(1:0.9*end)
x=t(0.9*end+1:end)
v=y(1+0.9*end:end)
layer = functionLayer((@(X) X./(1 -X^2)))
layers = [
sequenceInputLayer(1)
fullyConnectedLayer(1)
tanhLayer
functionLayer(((@(t) t./(1 -t.^2))),Description="softsign")
fullyConnectedLayer(1)
tanhLayer
functionLayer(((@(t) t./(1 -t.^2))),Description="softsign")
regressionLayer]
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',5, ...
'miniBatchSize',20,.....
'VerboseFrequency',1,...
'ValidationPatience',Inf,...
'MaxEpochs',100, ...
'Plots','training-progress')
net = trainNetwork(T',Y',layers,options);
ypre=predict(net,tspan);
plot(ypre)
plot(y)

Prateek Rai on 22 Feb 2022
Hi,
Training of the network stopped because training loss is NaN. This implies that the predictions using the output network might contain NaN values.
On analyzing network, I found that size of the all the layers is 1*1*1 which is why NaN values are coming.
You might want to recheck the dimension of the layers of the network using:
analyzeNetwork(layers)
Image Analyst on 5 Apr 2022
I get the same error trying to train on 448 images and my layers are not 1*1*1 -- they're 227x227x3

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