I’m new to deep learning and I trying to model a univariate time series using the sliding window approach with a LSTM model. My training dataset takes 16 values to predict the next 16. My code is writing in R.
I am getting a warning and I cannot understand what I am doing wrong. I think that the problem is when specifying the model.
I am totally new to this. So if you could help me would be great.
Bellow is the whole code. I got the warning at the very end, after predicting
train_sliding = create_dataset(data = kt_train_male_scaled, n_input = 16, n_out = 16)
X_train = train_sliding[[1]] #97, 16
y_train = train_sliding[[2]] #97, 16
#Array transformation to Keras LSTM
dim(X_train) = c(dim(X_train), 1)
dim(X_train) # 97, 16, 1
I think the problem should be in this chunk of code. I think I am building the model wrong
#Model in Keras
X_shape2 = dim(X_train)[2] #16
X_shape3 = dim(X_train)[3] #1
batch_size = 1
model <- keras_model_sequential()
model%>%
layer_lstm(units = 64, activation = “relu”, batch_size = batch_size, input_shape = c(dim(X_train)[2], dim(X_train)[3]),stateful= TRUE)%>%
#layer_lstm(units = 5, activation = “relu”, stateful= TRUE) %>%
layer_dense(units = 1)
summary(model)
model %>% compile(
loss = ‘mse’,
optimizer = optimizer_adam(lr= 0.01, decay = 1e-6 ),
metrics = c(‘mae’)
)
Epochs = 100
for(i in 1:Epochs ){
model %>% fit(X_train, y_train, epochs=1, batch_size=batch_size, verbose=1, shuffle=FALSE)
model %>% reset_states()
}
L = length(kt_test_male_scaled)
scaler = Scaled$scaler
predictions = numeric(L)
I get the warning after running this part. Also, all my 16 predictions have the same value. I also tried to use dim(X) = c(1,16,1) but it did not work
for(i in 1:L){
X = kt_test_male_scaled[i]
dim(X) = c(1,1,1)
yhat = model %>% predict(X, batch_size=batch_size)
# invert scaling
yhat = invert_scaling(yhat, scaler, c(-1, 1))
# invert differencing
#yhat = yhat + kt_male[(n+i)]
# store
predictions[i] <- yhat
}
Model was constructed with shape (1, 16, 1) for input KerasTensor(type_spec=TensorSpec(shape=(1, 16, 1), dtype=tf.float32, name=’lstm_107_input’), name=’lstm_107_input’, description=”created by layer ‘lstm_107_input'”), but it was called on an input with incompatible shape (1, 1, 1)
