Question

根据上一个问题，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 <- 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*/

但是，我想问一下如何为trainr（）定义多个变量的输入向量X.我的数据类型是：

Y = 1 

      T = 0          T = 1       T = 3        T = 4      T = 5
X1 =      1             0            1            0          1
X2 =      1             1            1            0          1
X3 =      0             0            0            0          1

X1 =      1             0            1            0          1
X2 =      0             1            1            1          1
X3 =      0             0            0            0          1
...

Y = 2 
      T = 0         T = 1        T = 3        T = 4      T = 5
X1 =      0             0            1            0          1
X2 =      1             1            0            0          1
X3 =      0             1            0            0          1

X1 =      1             0            1            0          1
X2 =      1             1            1            0          1
X3 =      0             1            0            0          1
...

Y = 3
      T = 0         T = 1        T = 3        T = 4      T = 5
X1 =      1             0            1            0          1
X2 =      1             0            1            0          1
X3 =      0             1            0            0          1

X1 =      1             1            1            0          1
X2 =      1             0            1            0          1
X3 =      0             1            0            0          0
...

为了理解包的细节，我尝试了以下情况：

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) )

然而，直方图显示正态分布，中心在-250。我认为分配不合理。可以详细说明结果吗？

实际上，我想知道如何创建所有时间的输入向量X以进行与Y值对应的分析。感谢您的回复

在R中使用具有多个输入变量的RNN

0 个答案: