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sumedh/
lstm-seq2seq

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Keras Implementation of Character-level recurrent sequence-to-sequence model

This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.

Full credits to : fchollet

Model reproduced by : Sumedh

Intended uses & limitations

This model implements a basic character-level recurrent sequence-to-sequence network for translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain. It works best on text of length <= 15 characters.

Training and evaluation data

English to French translation data from https://www.manythings.org/anki/

Training procedure

We start with input sequences from a domain (e.g. English sentences) and corresponding target sequences from another domain (e.g. French sentences).
An encoder LSTM turns input sequences to 2 state vectors (we keep the last LSTM state and discard the outputs).
A decoder LSTM is trained to turn the target sequences into the same sequence but offset by one timestep in the future, a training process called "teacher forcing" in this context. It uses as initial state the state vectors from the encoder. Effectively, the decoder learns to generate targets[t+1...] given targets[...t], conditioned on the input sequence.
In inference mode, when we want to decode unknown input sequences, we: - Encode the input sequence into state vectors - Start with a target sequence of size 1 (just the start-of-sequence character) - Feed the state vectors and 1-char target sequence to the decoder to produce predictions for the next character - Sample the next character using these predictions (we simply use argmax). - Append the sampled character to the target sequence - Repeat until we generate the end-of-sequence character or we hit the character limit.

Training hyperparameters

The following hyperparameters were used during training:

name	learning_rate	decay	rho	momentum	epsilon	centered	training_precision
RMSprop	0.0010000000474974513	0.0	0.8999999761581421	0.0	1e-07	False	float32

python

batch_size = 64  # Batch size for training.
epochs = 100  # Number of epochs to train for.
latent_dim = 256  # Latent dimensionality of the encoding space.
num_samples = 10000  # Number of samples to train on.

Model Plot

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sumedh/lstm-seq2seq