主流的序列转换方案还是基于RNN,或者CNN来模拟RNN,包含编码器和解码器。 性能最佳的模型:通过注意力机制,链接编码器和解码器。 本文提出一种简单的网络架构-Transformer:完全基于注意力机制,省去了RNN和卷积操作。 Transformer优点:效果好,并行度高。 注:论文提出的应用主要是机器翻译领域。 1. 介绍 RNN,LSTM,GRU在...
For example, in usual RNN you can adjust the time-decay of a channel from say 0.8 to 0.5 (these are called "gates"), while in RWKV you simply move the information from a W-0.8-channel to a W-0.5-channel to achieve the same effect. Moreover, you can fine-tune RWKV into a non...
LSTM is a popular RNN architecture, which was introduced by Sepp Hochreiter and Juergen Schmidhuber as a solution to the vanishing gradient problem. This work addressed the problem of long-term dependencies. That is, if the previous state that is influencing the current prediction is not in the...
From this system, NLP has experienced years of development, from the N-gram-based feature in 1994, to the NN-based system, and then to the rise of language models such as RNN and LSTM generated by the design of the internal framework of the NN system. , ELMO was born in 2017, and ...
There are also options within RNNs. For example, the long short-term memory (LSTM) network is superior to simple RNNs by learning and acting on longer-term dependencies. However, RNNs tend to run into two basic problems, known as exploding gradients and vanishing gradients. These issues are...
The first argument of LSTM class, the word “units”, is quite misleading and its expanded description “dimensionality of the output space” even sounds mysterious, at least for me. At first glance I thought it was the number of LSTM units in an RNN model or network due to my strong pe...
RNNs solve difficult tasks that deal with context and sequences, such as natural language processing, and are also used for contextual sequence recommendations. What distinguishes sequence learning from other tasks is the need to use models with an active data memory, such as LSTMs (Long Short-...
I'm also facing same issue. I'm using, ubuntu 16.04 tensorflow==1.12.0 cuda-9.0 cudnn=7.0.5 GPU Tesla C2075 InvalidArgumentError (see above for traceback): No OpKernel was registered to support Op 'CudnnRNN' with these attrs. Registered devices: [CPU,XLA_CPU,XLA_GPU], Registered ker...
然而,就我们所知,Transformer是第一个完全依赖于self-attention来计算其输入和输出表示的转换模型,而不使用序列对齐的RNNs或卷积。 In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9]. ...
D3D12 - Metacommands - Query Metacommand LSTM D3D12 - Metacommands - Query Metacommand MVN D3D12 - Metacommands - Query Metacommand Normalization D3D12 - Metacommands - Query Metacommand Pooling D3D12 - Metacommands - Query Metacommand RNN D3D12 - Metacommands - Query Metacommand Redu...