R: Recurrent Neural Network

最近有一個R PACKAGE - rnn，可以拿來做Recurrent Neural Network (RNN)。雖然現在它只能用CPU，速度很慢，不過他語法簡單，拿來做入門練習是不錯的選擇。

#install.packages('rnn')
rm(list=ls())
require(rnn)

Loading required package: rnn
package 愼㸱愼㸵rnn愼㸱愼㸶 was built under R version 3.3.2

set.seed(10)
f <- 5
w <- 2*pi*f

# Create sequences
t <- seq(0.005,2,by=0.005)
x <- sin(t*w) + rnorm(200, 0, 0.25)
y <- cos(t*w)
# Samples of 20 time series
X <- matrix(x, nrow = 40)
Y <- matrix(y, nrow = 40)
# Plot noisy waves
plot(as.vector(X), col='blue', type='l', ylab = "X,Y",
     main = "Noisy waves")
lines(as.vector(Y), col = "red")

legend("topright", c("X", "Y"), col = c("blue","red"),
       lty = c(1,1), lwd = c(1,1))

# Standardize in the interval 0 - 1
X <- (X - min(X)) / (max(X) - min(X))
Y <- (Y - min(Y)) / (max(Y) - min(Y))
# Transpose
X <- t(X)
Y <- t(Y)
# Training-testing sets
train <- 1:8
test <- 9:10

# Train model. Keep out the last two sequences.
model <- trainr(Y = Y[train,],
                X = X[train,],
                learningrate = 0.05,
                hidden_dim = 16,
                numepochs = 1500)

# Predicted values
Yp <- predictr(model, X)
# Plot predicted vs actual. Training set + testing set
plot(as.vector(t(Y)), col = 'red', type = 'l',
     main = "Actual vs predicted", ylab = "Y,Yp")
lines(as.vector(t(Yp)), type = 'l', col = 'blue')

legend("topright", c("Predicted", "Real"),
       col = c("blue","red"), lty = c(1,1), lwd = c(1,1))

# Plot predicted vs actual. Testing set only.
plot(as.vector(t(Y[test,])), col = 'red', type='l',
     main = "Actual vs predicted: testing set", ylab = "Y,Yp")
lines(as.vector(t(Yp[test,])), type = 'l', col = 'blue')

legend("topright", c("Predicted", "Real"), col = c("blue","red"),
       lty = c(1,1), lwd = c(1,1))