[FIXED] Why do we need to reshape the input for LSTM?

Issue

I read this article about LSTM:

https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/

The first basic example is about "Vanilla LSTM": predict next time series

Where the input = [10, 20, 30, 40, 50, 60, 70, 80, 90]

In the article the writer split the input (sequence) into matrix:

X,              y
10, 20, 30      40
20, 30, 40      50
30, 40, 50      60
...

I can't understand why the input need to reshape:

reshape from [samples, timesteps] into [samples, timesteps, features]

1. Why do we need this ?

In addition if my input is like (the basic example + ID columns):

ID    X,                y
1     10, 20, 30        40
1     20, 30, 40        50
2     30, 40, 50        60
2     40, 50, 60,       70
...

2. how we reshapes it ? what we will be the new dimensional ?

Solution

I think this link will help you understand why.

You always have to give a three-dimensional array as an input to your LSTM network. Where the first dimension represents the batch size, the second dimension represents the number of time-steps you are feeding a sequence. And the third dimension represents the number of units in one input sequence. For example, input shape looks like (batch_size, time_steps, seq_len)

Lets take your example sequence: [10, 20, 30, 40, 50, 60, 70, 80, 90]

Once we do split_sequence as stated in your article, we get a 2 dimensional feature vector X of shape (6, 3). Where 6 is number of samples and 3 is number of steps.

but given that the model only takes in a 3-D vector we must reshape our 2-d vector to 3-d.

so from (6, 3) --> (6, 3, 1).

To answer your second question, you can simply reshape your 2-d feature vector X by doing the following:

# Given that X is a numpy array
samples = X.shape[0]
steps = X.shape[1]
X = X.reshape(samples, steps, 1)

Answered By - Aakash Dusane