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Language Model based onThe Unreasonable Effectiveness of Recurrent Neural Networks fromAndrej Karapathy's blog.
General Tensorflow implementation of LSTM based Character Level Language Model to model the probability distribution of the next character in the sequence given a sequence of previous characters.
The above image is taken from the mentioned blog.
For the complete details of the dataset, preprocessing, network architecture and implementation, refer to thisWiki.

• Python 2.7
• Tensorflow
• tqdm (for displaying progress of training)

This implementation will,

• provide support for arbitrary length input sequences by training the Recurent Network usingTruncated Backpropagation Through Time (TBPTT). It reduces the problem of vanishing gradients for very long input sequences.

• provide support for stacked LSTM layers with residual connections for efficient training of the network.

• provide support for introducing different types of *random mutations in the input sequence for simulating real world data like,

  1. dropping characters in the input sequence
  2. introducing additional white spaces between two words

• the input pipeline is based on Tensorflow primitive readers and queuerunners which prefetch the data making training upto 1.5-2X faster on hardware accelarators. Prefetching data reduces the total stall time of the hardware accelarators thus making their efficient use.

*Random mutations in the input sequence improve the robustness of the trained model against real world data.

• tf.train.SequenceExample for storing and reading input sequence lengths of arbitrary length

• tf.contrib.training.batch_sequences_with_states for splitting and batching input sequences forTBPTT while maintaining the state of the recurrent network for each input example

• tf.nn.dynamic_rnn for dynamic unrolling of each input example upto its actual length and not for the padding at the end. This is more correctness than for efficiency