在具有多个输入变量的R中使用RNN
根据前面的问题，有一个简单的例子：在具有多个输入变量的R中使用RNN,r,recurrent-neural-network,R,Recurrent Neural Network,根据前面的问题，有一个简单的例子： /* Example*/ library('rnn') # create training numbers set.seed(1) X1 = sample(0:127, 7000, replace=TRUE) X2 = sample(0:127, 7000, replace=TRUE) # create training response numbers Y <- X1 + X2 # convert to binary X1 <- int2
/* Example*/
library('rnn')
# create training numbers
set.seed(1)
X1 = sample(0:127, 7000, replace=TRUE)
X2 = sample(0:127, 7000, replace=TRUE)
# create training response numbers
Y <- X1 + X2
# convert to binary
X1 <- int2bin(X1, length=8)
X2 <- int2bin(X2, length=8)
Y <- int2bin(Y, length=8)
# create 3d array: dim 1: samples; dim 2: time; dim 3: variables
X <- array( c(X1,X2), dim=c(dim(X1),2) )
# train the model
model <- trainr(Y=Y,
X=X,
learningrate = 0.1,
hidden_dim = 10,
start_from_end = TRUE )
/* End of Example*/
library(rnn)
set.seed(1)
X1 = sample(0:127, 5000, replace=TRUE)
X2 = sample(0:127, 5000, replace=TRUE)
X3 = sample(0:127, 5000, replace=TRUE)
X4 = sample(0:127, 5000, replace=TRUE)
Y <- X1 + X2 + X3 + X4
X1 <- int2bin(X1)
X2 <- int2bin(X2)
X3 <- int2bin(X3)
X4 <- int2bin(X4)
Y <- int2bin(Y)
X <- array( c(X1,X2,X3,X4), dim=c(dim(X1),4) )
Y <- array( Y, dim=c(dim(Y),1) )
model <- trainr(Y=Y,
X=X,
learningrate = 0.1,
hidden_dim = 10,
start_from_end = TRUE )
plot(colMeans(model$error),type='l',
xlab='epoch',
ylab='errors' )
A1 = int2bin( sample(0:127, 7000, replace=TRUE) )
A2 = int2bin( sample(0:127, 7000, replace=TRUE) )
A3 = int2bin( sample(0:127, 7000, replace=TRUE) )
A4 = int2bin( sample(0:127, 7000, replace=TRUE) )
A <- array( c(A1,A2,A3,A4), dim=c(dim(A1),4) )
B <- predictr(model,
A )
A1 <- bin2int(A1)
A2 <- bin2int(A2)
A3 <- bin2int(A3)
A4 <- bin2int(A4)
B <- bin2int(B)
hist( B-(A1+A2+A3+A4) )
库（rnn）
种子（1）
X1=样本（0:1275000，替换=真）
X2=样本（0:1275000，替换=真）
X3=样本（0:1275000，替换=真）
X4=样本（0:1275000，替换=真）
Y设置int2bin长度后，结果现在处于合理范围内。例如X1
library(rnn)
set.seed(1)
X1 = sample(0:127, 5000, replace=TRUE)
X2 = sample(0:127, 5000, replace=TRUE)
X3 = sample(0:127, 5000, replace=TRUE)
X4 = sample(0:127, 5000, replace=TRUE)
Y <- X1 + X2 + X3 + X4
X1 <- int2bin(X1)
X2 <- int2bin(X2)
X3 <- int2bin(X3)
X4 <- int2bin(X4)
Y <- int2bin(Y)
X <- array( c(X1,X2,X3,X4), dim=c(dim(X1),4) )
Y <- array( Y, dim=c(dim(Y),1) )
model <- trainr(Y=Y,
X=X,
learningrate = 0.1,
hidden_dim = 10,
start_from_end = TRUE )
plot(colMeans(model$error),type='l',
xlab='epoch',
ylab='errors' )
A1 = int2bin( sample(0:127, 7000, replace=TRUE) )
A2 = int2bin( sample(0:127, 7000, replace=TRUE) )
A3 = int2bin( sample(0:127, 7000, replace=TRUE) )
A4 = int2bin( sample(0:127, 7000, replace=TRUE) )
A <- array( c(A1,A2,A3,A4), dim=c(dim(A1),4) )
B <- predictr(model,
A )
A1 <- bin2int(A1)
A2 <- bin2int(A2)
A3 <- bin2int(A3)
A4 <- bin2int(A4)
B <- bin2int(B)
hist( B-(A1+A2+A3+A4) )