Sequence Classification with LSTM RNN in Python with Keras

In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.


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What will you learn

  • Understanding the problem statement

  • Importing the problem statement

  • Installing Keras and LSTM

  • Installing Tensorflow

  • Importing the necessary libraries for applying Neural Networks

  • What are Recurrent Neural Networks and how do they work

  • Understanding basics of NLP

  • Performing basic EDA and checking for the null values

  • Making your own Neural Network from scratch

  • Applying LSTM without dropout and evaluating the result

  • Applying LSTM with dropout and evaluating the result

  • Creating the model with double drop out, drop out between layers and drop out within layers of LSTM

  • Introducing the concept of the Fully connected network to optimize the model further

  • Finally evaluating the model

  • Making predictions for the test Dataset

Project Description

A sequence to sequence prediction for developing a classification system is of very much required in developing applications. Standard approaches for developing applications won't help in providing accuracy. Hence, as an example let's take an IMDB movie review dataset and create some benchmarks by using RNN, RNN with LSTM and drop out rate, RNN with CNN, and RNN with CNN plus drop out rate to make a composite sequence to sequence classification work. We can compare the model accuracy as well.

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Curriculum For This Mini Project

  Import Libraries
  Sequential Model in Keras
  Load Data Set - Top words
  Truncate and Pad input sequences
  Create a Model
  Evaluate the Model
  LSTM with Dropout
  LSTM and Convolutional Neural Network
  LSTM and Flatten
  Testing Predictions