In this tutorial, we will introduce the way to convert a tensor to a tensorarray object in tensorflow, which is very useful when you are bulding a custom lstm or bilstm.
Nested LSTM network is one of improved LSTM model, which has better performance than classic LSTM. In this tutorial, we will introduce it for lstm network beginners.
LSTM network contains three gates: input gate, forget gate and output gate. In this tutorial, we will discuss the effect of each gate in LSTM.
In this tutorial, we discussed the difference of LSTM and GRU performance and tell you which network you should choose for deep learning programming.
Sometimes, we have to crawl all resources in only a site. In that situation, we will have to get domain or subdomain of this site by url. In this tutorial, we will introduce you how to do in python.
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 will discuss how tf.get_variable() initialize a tensor when its initializer is None.
In order to improve the performance of our mdoel, we can use Xavier method to initialize weights. In this tutorial, we will introduce how to intialize tensorflow weights using Xavier.
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.