Keras Neural Network applied several times with own loss function - r

Question

I am new to machine learning tools and have installed Keras in R. While considering simpler models, I want to use neural network now for more special purposes. Generally, the neural network should be a function Phi: R^d -> R, where the input is d-dimensional.
Given are d-dimensional data for n time points such that the neural network calculates for each time an 1-dimensional target value. Thereof, I have M samples, i.e. the input is somewhat (samples,times,input_dimension)=(M,n,d), on which the neural network is separately applied. The output should be of the form (samples,times)=(M,n), so that for each time, the predicted value of the neural network is compared with the desired target - and this for every sample. Just for information, the range of the numbers are around d=5, n=1000, M=100.
Based on this, one would suggest to run a "usual" neural network on M*n samples with d-dimensional input and 1-dimensional target. However, the problem is that the loss function depends on the previous evaluations of the neural networks in each time step, i.e. the loss is of the form
l(y_pred,y_target) = sum_{i=1}^n (y^i_pred-y^i_target+f_i(...))^2
where y^i_pred and y^i_target are the predicted and target values of the ith time step, respectively, and f_i is an additional function (that depends on the second derivative of the neural network, but that is another story, and on the previous losses).
So far I have the following code in order to illustrate my problem:
input <- array(data1,dim=c(M,n,d))
target <- array(data2,dim=c(M,n))
myloss <- function(f,y_true,y_pred) {
K <- backend()
return(K$sum((y_pred-y_true+f)^2))
}
library(keras)
NN <- keras_model_sequential()
NN %>%
layer_dense(units=20,activation='relu',input_shape=c(n,d)) %>%
layer_dense(units=20,activation='relu') %>%
layer_dense(units=1,activation='relu')
summary(NN)
NN %>% compile(
loss function(y_true,y_pred) myloss(f,y_true,y_pred),
optimizer = "adam",
metrics = "acc"
)
history <- NN %>% fit(
input, target,
epochs = 30, batch_size = 20,
validation_split = 0.1
)
I get various error messages (concerning dimension of target and custom loss function), therefore my question: Is it even possible to incorporate my problem into a Keras model? Or should I use convolutional neural networks? I looked also at recurrent networks, but in my case, only the loss is dependent on the "previous values". Perhaps somebody can give an advice, I would appreciate your help.