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I'm writing a neural network for prediction of elements in a time series x + sin(x^2) in R, using the neuralnet package. This is how training data is being generated, assuming a window of 4 elements, and that the last one is the one that has to be predicted:

nntr0 <- ((1:25) + sin((1:25)^2))
nntr1 <- ((2:26) + sin((2:26)^2))
nntr2 <- ((3:27) + sin((3:27)^2))
nntr3 <- ((4:28) + sin((4:28)^2))
nntr4 <- ((5:29) + sin((5:29)^2))

Then, I turn these into a data.frame:

nntr <- data.frame(nntr0, nntr1, nntr2, nntr3, nntr4)

Then, I proceed to train the NN:

net.sinp <- neuralnet(nntr4 ~ nntr0 + nntr1 + nntr2 + nntr3, data=nntr, hidden=10, threshold=0.04, act.fct="tanh", linear.output=TRUE, stepmax=100000)

Which, after a while, gives me the message 

Warning message:
algorithm did not converge in 1 of 1 repetition(s) within the stepmax 
Call: neuralnet(formula = nntr4 ~ nntr0 + nntr1 + nntr2 + nntr3, data = nntr,     hidden = 10, threshold = 0.04, stepmax = 100000, act.fct = "tanh", linear.output = TRUE)

Can anyone help me figure out why it is not converging? Many thanks

Bill the Lizard
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bkpenator
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2 Answers2

4

Warning message: algorithm did not converge in 1 of 1 repetition(s) within the stepmaxmeans your algorithm reached the limited steps before it is converged. If you type ?neuralnet and see the definition for stepmax it says,

the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process.

For your problem, I recommend you to increase your stepmax value to 1e7 and see what happens.

The code will be,

net.sinp <- neuralnet(nntr4 ~ nntr0 + nntr1 + nntr2 + nntr3, data=nntr, hidden=10, threshold=0.04, act.fct="tanh", linear.output=TRUE, stepmax=1e7)

boyaronur
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3

With tanh as an activation function (it is bounded), it is very difficult to reproduce the linear trend in your signal.

You can use linear activation functions instead, or try to detrend the signal.

# Data
dx <- 1
n <- 25
x <- seq(0,by=dx,length=n+4)
y <- x + sin(x^2)
y0 <- y[1:n]
y1 <- y[1 + 1:n]
y2 <- y[2 + 1:n]
y3 <- y[3 + 1:n]
y4 <- y[4 + 1:n]
d <- data.frame(y0, y1, y2, y3, y4)
library(neuralnet)

# Linear activation functions
r <- neuralnet(y4 ~ y0 + y1 + y2 + y3, data=d, hidden=10)
plot(y4, compute(r, d[,-5])$net.result)

# No trend
d2 <- data.frame(
  y0 = y0 - x[1:n], 
  y1 = y1 - x[1 + 1:n], 
  y2 = y2 - x[2 + 1:n], 
  y3 = y3 - x[3 + 1:n], 
  y4 = y4 - x[4 + 1:n]
)
r <- neuralnet(y4 ~ y0 + y1 + y2 + y3, data=d2, hidden=10, act.fct="tanh" )
plot(d2$y4, compute(r, d2[,-5])$net.result)
Vincent Zoonekynd
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