An implementation of classical GRU (Cho, el at. 2014) along with Optimized versions (Dey, Rahul. 2017) on TensorFlow that outperforms Native tf.keras.layers.GRU(units) implementation of Keras.
The original paper [Dey, Rahul, et al. 2017] used MNIST Dataset for the training and inference purpose, we'll be comparing them on np.random.rand(batch_size, time_width, 500) in our implementation (this ensures that bias ~ 0, following random noise cancel each others.)
The whole code has two files - CustomGRU.py and Notebook.ipynb as different implementations of Gated Recurrent Units and an short tutorial on usage in form of Python Notebooks respectively. We compare GRU0 - Classical GRU, GRU1/GRU2/GRU3 as optimized GRU models and GRU4 as Native TensorFlow implementation of GRU in form of tf.keras.layers.GRU(units). GRUs are special RNNs which are very lighter alternative to LSTM with very few parameters and almost similar performance [Cho, et al. 2014]. We optimize classical GRUs to the versions in [Dey, Rahul. 2017] for our analysis. We found that all four manual implementations outperform the Native TensorFlow GRU implementation, which almost stopped converging after a certain epochs. Our models had slower initial convergence compared to TensorFlow's native implementation which was much sharper in the beginning but totally stopped after few epochs whereas our Models continued it. Training time was almost same for all the five models, though, GRU2/GRU3 had much faster training time and GRU4 outperformed all in training duration.
(Trained on MNIST Dataset)
(Same Models, trained on Random Noise)
(Blue line refers to TensorFlow Keras Native GRU Implementation)
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