Metadata-Version: 2.0
Name: phased-lstm-keras
Version: 1.0.1
Summary: Keras implementation of Phased LSTM
Home-page: https://github.com/fferroni/PhasedLSTM-Keras
Author: Francesco Ferroni
Author-email: francescoferroni1@gmail.com
License: GPLv3+
Keywords: machinelearning deeplearning development
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: keras (>=2.0.2)
Requires-Dist: matplotlib (>=2.0.0)
Requires-Dist: numpy (>=1.12.1)

PhasedLSTM-Keras
================

Keras implementation of Phased LSTM [https://arxiv.org/abs/1610.09513], from NIPS 2016.

Works both with Theano and Tensorflow backend (although Theano recommended, as 3x faster).

Classification performance compared to standard Keras LSTM for MNIST dataset:

.. image:: mnist_plstm_lstm_comparison_acc.png
   :height: 100px
   :width: 100px
   :alt: Accuracy [red: PLSTM, black: LSTM]
   :align: center

.. image:: mnist_plstm_lstm_comparison_loss.png
   :height: 100px
   :width: 100px
   :alt: Loss [red: PLSTM, black: LSTM]
   :align: center

____________________________________________________________________________________________________

Phased LSTM::
-------------

  Epoch 1/20
  60000/60000 [==============================] - 324s - loss: 2.0418 - acc: 0.2513     

  Epoch 2/20
  60000/60000 [==============================] - 319s - loss: 1.7242 - acc: 0.3654     

  Epoch 3/20
  60000/60000 [==============================] - 314s - loss: 1.7099 - acc: 0.3501     

  Epoch 4/20
  60000/60000 [==============================] - 314s - loss: 1.5299 - acc: 0.4254     

  Epoch 5/20
  60000/60000 [==============================] - 313s - loss: 1.2343 - acc: 0.5388     

  Epoch 6/20
  60000/60000 [==============================] - 314s - loss: 1.1064 - acc: 0.5926     

  Epoch 7/20
  60000/60000 [==============================] - 314s - loss: 1.0078 - acc: 0.6425     

  Epoch 8/20
  60000/60000 [==============================] - 314s - loss: 0.9120 - acc: 0.6825     

  Epoch 9/20
  60000/60000 [==============================] - 314s - loss: 0.8294 - acc: 0.7134     

  Epoch 10/20
  60000/60000 [==============================] - 311s - loss: 0.7552 - acc: 0.7434     

  Epoch 11/20
  60000/60000 [==============================] - 311s - loss: 0.6813 - acc: 0.7685     

  Epoch 12/20
  60000/60000 [==============================] - 311s - loss: 0.6143 - acc: 0.7901     

  Epoch 13/20
  60000/60000 [==============================] - 311s - loss: 0.5686 - acc: 0.8028     

  Epoch 14/20
  60000/60000 [==============================] - 311s - loss: 0.5320 - acc: 0.8156     

  Epoch 15/20
  60000/60000 [==============================] - 311s - loss: 0.5097 - acc: 0.8223     

  Epoch 16/20
  60000/60000 [==============================] - 311s - loss: 0.4750 - acc: 0.8353     

  Epoch 17/20
  60000/60000 [==============================] - 311s - loss: 0.4507 - acc: 0.8467     

  Epoch 18/20
  60000/60000 [==============================] - 312s - loss: 0.4354 - acc: 0.8538     

  Epoch 19/20
  60000/60000 [==============================] - 316s - loss: 0.4106 - acc: 0.8618     

  Epoch 20/20
  60000/60000 [==============================] - 316s - loss: 0.3934 - acc: 0.8695

LSTM::
------

  Epoch 1/20
  60000/60000 [==============================] - 157s - loss: 2.2945 - acc: 0.1216     

  Epoch 2/20
  60000/60000 [==============================] - 157s - loss: 2.0987 - acc: 0.2275     

  Epoch 3/20
  60000/60000 [==============================] - 157s - loss: 1.9601 - acc: 0.2926     

  Epoch 4/20
  60000/60000 [==============================] - 157s - loss: 1.8418 - acc: 0.3247     

  Epoch 5/20
  60000/60000 [==============================] - 157s - loss: 2.0860 - acc: 0.2619     

  Epoch 6/20
  60000/60000 [==============================] - 157s - loss: 2.1297 - acc: 0.2225     

  Epoch 7/20
  60000/60000 [==============================] - 157s - loss: 1.8556 - acc: 0.3287     

  Epoch 8/20
  60000/60000 [==============================] - 157s - loss: 1.8428 - acc: 0.3344     

  Epoch 9/20
  60000/60000 [==============================] - 158s - loss: 1.8119 - acc: 0.3219     

  Epoch 10/20
  60000/60000 [==============================] - 158s - loss: 1.8159 - acc: 0.3246     

  Epoch 11/20
  60000/60000 [==============================] - 158s - loss: 1.9290 - acc: 0.2554     

  Epoch 12/20
  60000/60000 [==============================] - 158s - loss: 1.7843 - acc: 0.3047     

  Epoch 13/20
  60000/60000 [==============================] - 158s - loss: 1.7623 - acc: 0.3371     

  Epoch 14/20
  60000/60000 [==============================] - 158s - loss: 1.6016 - acc: 0.4079     

  Epoch 15/20
  60000/60000 [==============================] - 158s - loss: 1.5954 - acc: 0.3985     

  Epoch 16/20
  60000/60000 [==============================] - 157s - loss: 1.6393 - acc: 0.3823     

  Epoch 17/20
  60000/60000 [==============================] - 157s - loss: 1.6186 - acc: 0.3939     

  Epoch 18/20
  60000/60000 [==============================] - 157s - loss: 1.6276 - acc: 0.3835     

  Epoch 19/20
  60000/60000 [==============================] - 157s - loss: 1.6557 - acc: 0.3684     

  Epoch 20/20
  60000/60000 [==============================] - 157s - loss: 1.8699 - acc: 0.3258



