Module $7$: Auto$-$Encoder Assignment

Problem Statement:

Consider yourself to be Bob, who is a Deep Learning PhD student. As part of your semester exam you

have to implement an LSTM encoder.

Brief information on LSTM Auto$-$Encoders:

LSTM Autoencoders can learn a compressed representation of sequence data and have been used on

video, text, audio, and time series sequence data.

For a given dataset of sequences, an encoder$-$decoder LSTM is configured to read the input sequence,

encode it$,$ decode it$,$ and recreate it$.$ The performance of the model is evaluated based on the model$$s

ability to recreate the input sequence.

Tasks to be Done:

A$.$ Start off by creating an input sequence of numbers: $[0\mathrm{.}1,0\mathrm{.}2,0\mathrm{.}3,0\mathrm{.}4,0\mathrm{.}5,0\mathrm{.}6,0\mathrm{.}7,0\mathrm{.}8,0\mathrm{.}9]$

a$.$ Build an LSTM RNN model on top of this data. Compile the model with $$adam$$ to be the optimizer and loss to be $$mse$$

b$.$ Fit the model on data and set the number of epochs to be $300$

c$.$ Predict the values and verify it with the input data