Deep Learning Quick Reference

Input shape

Keras expects the input for our LSTM to be a three-dimensional tensor that looks like:

The first dimension is obviously the number of observations we have, and we would expect that.

The second dimension corresponds to the number of lags we've chosen when using the lag_dataframe function. This is the number of time steps we're going to give Keras in order to make a prediction.

The third dimension is the number of features present in that time step. In our example, we'll be using one, because we only have one feature per time step, that time step's bitcoin price.

Before reading on, consider carefully the power that defining a three ...