In this tutorial, we discussed the difference of LSTM and GRU performance and tell you which network you should choose for deep learning programming.
LSTM peephole conncections is one of improvements for classic LSTM network. In this tutorial, we will introduce the difference between LSTM peephole conncections and classic LSTM.
In this tutorial, we discuss how LSTM Weight and Bias are initialized when initializer is None in TensorFlow. We can modify our custom lstm to make its performace same to tensorflow LSTM network.
In this tutorial, we introduce why we should add a forget bias for lstm forget gate and add a forget bias for our custom lstm network.
In this tutorial, we will use our custom GRU network to classify MNIST handwritten digits, which aims to evaluate the effectiveness of our custom GRU.
In this tutorial, we will introduce how to build our custom GRU network using tensorflow, which is very similar to create a custom lstm network.
There are many models that have improved LSTM, GRU (Gated Recurrent Unit) is one of them. In this tutorial, we will introduce GRU and compare it with LSTM.
As to GRU, there is a reset gate in it. Can we remove this reset gate in GRU? If we remove it, the performance of GRU will decreased? The answer is we can remove the reset gate.
In this tutorial, we will introduce you how to build your own BiLSTM model using tensorflow, you can modify our code and build a customized model.
We have created a customized lstm model (lstm.py) using tensorflow. In this tutorial, we will use this customized lstm model to train mnist set and classify handwritten digits.