A LSTM, RNN based model that auto suggest the next character/word on the basis of previous text input.

Just like auto-suggest in phones while typing, this model was build in order to auto-suggest the next keyword on the basis of previous input text.

This model uses Long-Short-Term-Memory in order to remember the entire sequence of text.

This model uses the following Layers :

1. LSTM layer
2. Dropout Layer
3. Dense Layer

After tuning various hyperparameters the best performace was found at...

LSTM_layers=128, Dropout=0.2, Activation_function="Softmax", optimizer=RMSprop, learning-rate=0.01

The process terminated at 19 epoch using Earlystopping callback as with further epoch the model might start getting overfitted. It took around 20min to train on my machine without GPU.

The model shows accuracy of 56% and validation_accuracy of 52% with loss of 1.44, which is acceptable as we want the model to understand the text and not cram it completely.

The first line is input sequence of 40 words and the output resembles the top 5 suggestions predicted by the model.

Input: those who work honestly have the courage
Output: [' the ', ', ', '--they ', 'd ', 'ness ']

Input: the pain is temporary but the results ar
Output: ['e ', 'tists ', 'ound ', 'a ', 'id ']

Input: i'm not upset that you lied to me but th
Output: ['e ', 'at ', 'is ', 'ought ', 'rough ']

Input: and those who were seen dancing were the
Output: [' strength ', 're ', 'ir ', 'y ', 'mselves ']

Input: it is hard enough to remember my opinion
Output: [' of ', 's ', ', ', '. ', '--the ']

The model was trained on various other hyperparameters to obtain different results. The results can be found at the bottom of the lstm.py