Keras LSTM and multiple input feature: how to define parameters - r

Question

I am discoveting Keras in R and the LSTM. Following this blog post, I want to predict time series, and I would like to use various past time point (t-1, t-2) to predict the t point.
Here is what I tried so far:
library(data.table)
library(tensorflow)
library(keras)
Serie <- c(5.66333333333333, 5.51916666666667, 5.43416666666667, 5.33833333333333,
5.44916666666667, 6.2025, 6.57916666666667, 6.70666666666667,
6.95083333333333, 8.1775, 8.55083333333333, 8.42166666666667,
8.01333333333333, 8.99833333333333, 11.0025, 10.3116666666667,
10.51, 10.9916666666667, 10.6116666666667, 10.8475, 13.7841666666667,
16.2916666666667, 15.9975, 14.3683333333333, 13.4041666666667,
11.8666666666667, 9.11916666666667, 9.47862416666667, 9.08404666666667,
8.79606166666667, 9.93211091666667, 9.03834041666667, 8.58787275,
6.77499383333333, 7.21377583333333, 7.53497175, 6.31212966666667,
5.5825105, 4.64021041666667, 4.608787, 5.39446983333333, 4.93945983333333,
4.8612215, 4.13088808333333, 4.09916575, 3.40943183333333, 3.79573258333333,
4.30319966666667, 4.23431266666667, 3.64880758333333, 3.11700716666667,
3.321058, 2.53599408333333, 2.20433991666667, 1.66643905833333,
0.84187275, 0.467880658333333, 0.810507858333333, 0.795)
Npoints <- 2 # number of previous point to take into account
I then create a data frame with the lagged time series, and create a test and train set:
supervised <- data.table(x = diff(Serie, differences = 1))
supervised[,c(paste0("x-",1:Npoints)) := lapply(1:Npoints,function(i){c(rep(NA,i),x[1:(.N-i)])})] # create shifted versions
# take the non NA
supervised <- supervised[!is.na(get(paste0("x-",Npoints)))]
head(supervised)
# Split dataset into training and testing sets
N = nrow(supervised)
n = round(N *0.7, digits = 0)
train = supervised[1:n, ]
test = supervised[(n+1):N, ]
I rescale the data
scale_data = function(train, test, feature_range = c(0, 1)) {
x = train
fr_min = feature_range[1]
fr_max = feature_range[2]
std_train = ((x - min(x,na.rm = T) ) / (max(x,na.rm = T) - min(x,na.rm = T) ))
std_test = ((test - min(x,na.rm = T) ) / (max(x,na.rm = T) - min(x,na.rm = T) ))
scaled_train = std_train *(fr_max -fr_min) + fr_min
scaled_test = std_test *(fr_max -fr_min) + fr_min
return( list(scaled_train = as.vector(scaled_train), scaled_test = as.vector(scaled_test) ,scaler= c(min =min(x,na.rm = T), max = max(x,na.rm = T))) )
}
Scaled = scale_data(train, test, c(-1, 1))
# define x and y train
y_train = as.vector(Scaled$scaled_train[, 1])
x_train = Scaled$scaled_train[, -1]
And following this post I reshape the data in 3D
x_train_reshaped <- array(NA,dim= c(1,dim(x_train)))
x_train_reshaped[1,,] <- as.matrix(x_train)
I do the following model and try to start the learning :
model <- keras_model_sequential()
model%>%
layer_lstm(units = 1, batch_size = 1, input_shape = dim(x_train), stateful= TRUE)%>%
layer_dense(units = 1)
# compile model ####
model %>% compile(
loss = 'mean_squared_error',
optimizer = optimizer_adam( lr= 0.02, decay = 1e-6 ),
metrics = c('accuracy')
)
# make a test
model %>% fit(x_train_reshaped, y_train, epochs=1, batch_size=1, verbose=1, shuffle=FALSE)
but I get the following error:
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: No data provided for "dense_11". Need data for each key in: ['dense_11']
Trying to reshape the data differently didn't help.
What I am doing wrong ?

Answer 1

Keras and tensorflow in R cannot recognise the size of your input/target data when they are data frames.
y_train is both a data.table and a data.frame:
class(y_train)
[1] "data.table" "data.frame"
The keras fit documentation states: "y: Vector, matrix, or array of target (label) data (or list if the model has multiple outputs)." Similarly, for x.
Unfortunately, there still appears to be an input and/or target dimensionality mismatch when y_train is cast to a matrix:
model %>%
fit(x_train_reshaped, as.matrix(y_train), epochs=1, batch_size=1, verbose=1, shuffle=FALSE)
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Input arrays should have the same number of samples as target arrays.
Found 1 input samples and 39 target samples.
Hope this answer helps you, or someone else, make further progress.