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  • Created almost 10 years ago
  • Updated about 8 years ago

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Repository Details

LSTM implementation on Caffe

LSTM Implementation in Caffe

Note that the master branch of Caffe supports LSTM now. (Jeff Donahue's implementation has been merged.)
This repo is no longer maintained.

Speed comparison (Titan X, 3-layer LSTM with 2048 units)

Jeff's code is more modularized, whereas this code is optimized for LSTM.
This code computes gradient w.r.t. recurrent weights with a single matrix computation.

  • Batch size = 20, Length = 100
Code Forward(ms) Backward(ms) Total (ms)
This code 248 291 539
Jeff's code 264 462 726
  • Batch size = 4, Length = 100
Code Forward(ms) Backward(ms) Total (ms)
This code 131 118 249
Jeff's code 140 290 430
  • Batch size = 20, Length = 20
Code Forward(ms) Backward(ms) Total (ms)
This code 49 59 108
Jeff's code 52 92 144
  • Batch size = 4, Length = 20
Code Forward(ms) Backward(ms) Total (ms)
This code 29 26 55
Jeff's code 30 61 91

Example

An example code is in /examples/lstm_sequence/.
In this code, LSTM network is trained to generate a predefined sequence without any inputs.
This experiment was introduced by Clockwork RNN.
Four different LSTM networks and shell scripts(.sh) for training are provided.
Each script generates a log file containing the predicted sequence and the true sequence.
You can use plot_result.m to visualize the result.
The result of four LSTM networks will be as follows:

  • 1-layer LSTM with 15 hidden units for short sequence Diagram
  • 1-layer LSTM with 50 hidden units for long sequence Diagram
  • 3-layer deep LSTM with 7 hidden units for short sequence Diagram
  • 3-layer deep LSTM with 23 hidden units for long sequence Diagram