I’m trying to train an LSTM model to predict number of real roots of polynomials. x_train and y_train include array of arrays such as [[-204, 20, 13, 1, 0]] which are coefficients of polynomials. x_test and y_test include number of real roots of each polynomial such 1,2,5… I’m stuck in this error for days. I’m trying to give input to LSTM properly but it seems I cannot do it. I’m trying to implement an LSTM model for forecasting like on this site: How to Develop LSTM Models for Time Series Forecasting

Here is my code:

```
using Flux: @epochs, throttle
using Flux
function input()
## x_train
lines = Tuple(readlines("/home/user/Desktop/train_x_data.txt"))
x_train = []
for i in lines
push!(x_train, convert(Vector{Float32},eval(Meta.parse(i))))
end
x_train = Vector{Vector{Float32}}(x_train)
## y_train
lines = Tuple(readlines("/home/user/Desktop/train_y_data.txt"))
y_train = []
for i in lines
push!(y_train, eval(Meta.parse(i)))
end
y_train = convert(Vector{Float32}, y_train)
## x_test
lines = Tuple(readlines("/home/user/Desktop/test_x_data.txt"))
x_test = []
for i in lines
push!(x_test, convert(Vector{Float32},eval(Meta.parse(i))))
end
x_test = Vector{Vector{Float32}}(x_test)
## y_test
lines = Tuple(readlines("/home/user/Desktop/test_y_data.txt"))
y_test = []
for i in lines
push!(y_test, eval(Meta.parse(i)))
end
y_test = convert(Vector{Float32}, y_test)
return x_train, x_test, y_train, y_test
end
function LSTM_model(N,num_of_classes)
model = Chain(LSTM(N,200),
Dropout(0.2),
LSTM(200,200),
Dropout(0.1),
Dense(200,101),
Dropout(0.1),
Dense(101,num_of_classes))
return model
end
function eval_model(x, model)
output = [model(x) for x in x][end]
reset!(model)
output
end
function main()
num_of_classes = 101
num_epochs = 50
x_train, x_test, y_train, y_test = input()
N = size(x_train)[1]
model = LSTM_model(N,num_of_classes)
loss(x, y) = sum(Flux.Losses.binarycrossentropy(eval_model(x,model), y))
ps = Flux.params(model)
# use the ADAM optimizer. It's a pretty good one!
opt = Flux.ADAM(0.001)
evalcb = () -> @show testloss()
@info("Training...")
# callback function during training
evalcb() = @show(sum(loss.(x_test, y_test)))
# I get error at below line
@epochs num_epochs Flux.train!(loss, ps, zip(x_train, y_train), opt, cb = Flux.throttle(evalcb, 1))
# after training, evaluate the loss
println("Test loss after = ", sum(loss.(x_test, y_test)))
end
main()
```

**Error Message I Get**

```
ERROR: LoadError: MethodError: no method matching (::Flux.LSTMCell{Matrix{Float32}, Vector{Float32}, Tuple{Matrix{Float32}, Matrix{Float32}}})(::Tuple{Matrix{Float32}, Matrix{Float32}}, ::Float32)
Closest candidates are:
(::Flux.LSTMCell{A, V, <:Tuple{AbstractMatrix{T}, AbstractMatrix{T}}})(::Any, ::Union{AbstractVector{T}, AbstractMatrix{T}, Flux.OneHotArray}) where {A, V, T} at ~/.julia/packages/Flux/BPPNj/src/layers/recurrent.jl:157
```

Thanks in advance!