Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (3653, 3)

I am trying to learn LSTM with keras in R. I am not being able to fully understand the conventions used in keras.

I have dataset that looks like below, with the first 3 columns considered as input and the last one as output.

Based on this, I am trying to build a stateless LSTM as follows:

model %>%
  layer_lstm(units = 1024, input_shape = c(1, 3), return_sequences = T ) %>%  
  layer_lstm(units = 1024, return_sequences = F) %>% 
  # using linear activation on last layer, as output is needed in real number
  layer_dense(units = 1, activation = "linear")

model %>% compile(loss = 'mse', optimizer = 'rmsprop')

The model looks like below

Layer (type)       Output Shape       Param #             
=====================================================
lstm_1 (LSTM)      (None, 1, 1024)    4210688             
_____________________________________________________
lstm_2 (LSTM)      (None, 1024)       8392704             
_____________________________________________________
dense_3 (Dense)    (None, 1)          1025                
=====================================================
Total params: 12,604,417
Trainable params: 12,604,417
Non-trainable params: 0    
_____________________________________________________

I am trying to train the model as follows:

history <- model %>% fit(dt[,1:3], dt[,4], epochs=50, shuffle=F)

However, i am getting the following error when I try to execute the code.

Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (3653, 3)

Not sure what I am missing here.

Update: After looking around in internet, it seems that I need to reshape the dataset into a 3 dimensional (batchsize, timestep, #features) array. However, I am not using any batch, thus not sure how to reshape my data.

Update on 29.01.2018: This is what worked for me. I used input_shape = c(1, 3) in my first LSTM layer, as I have 3 features and I am not using any batch. Thus, I also ended up reshaping my data using the following function:

reshapeDt <- function(data){ # data is the original train matrix (training dataset)
  rows <- nrow(data)
  cols <- ncol(data)-1

  dt <- array(dim=c(rows, 1, cols))
  for(i in 1:rows){
    dt[i,1,] <- data[i,1:cols]
  }
  dt
}

This means that the call to fit looks like below:

model %>% fit(reshapeDt(dt), dt[,4], epochs=50, shuffle=F)

This means that dim(reshapeDt(dt)) returns number_of_rows_in_dt 1 3.