rnn (version 0.5.0)

predictr: Recurrent Neural Network

Description

predict the output of a RNN model

Usage

predictr(model, X, hidden = FALSE, ...)

Arguments

model

output of the trainr function

X

array of input values, dim 1: samples, dim 2: time, dim 3: variables (could be 1 or more, if a matrix, will be coerce to array)

hidden

should the function output the hidden units states

...

arguments to pass on to sigmoid function

Value

array or matrix of predicted values

Examples

# create training numbers
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)
X2 <- int2bin(X2)
Y  <- int2bin(Y)

# 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 )
             
# create test inputs
A1 = int2bin( sample(0:127, 7000, replace=TRUE) )
A2 = int2bin( sample(0:127, 7000, replace=TRUE) )

# create 3d array: dim 1: samples; dim 2: time; dim 3: variables
A <- array( c(A1,A2), dim=c(dim(A1),2) )
    
# predict
B  <- predictr(model,
               A     )
 
# convert back to integers
A1 <- bin2int(A1)
A2 <- bin2int(A2)
B  <- bin2int(B)
 
# inspect the differences              
table( B-(A1+A2) )

# plot the difference
hist(  B-(A1+A2) )