Comments (3)
AFAIR Cell is/was a template/factory, not a layer or parameter container
from hierarchical-attention-networks.
I think I am getting you. BNLSTMCell is just a declaration of a class. Still when you say call BNLSTMCell.call(), it will still call same set of parameters which are already defined in the graph at word level as they fall in same namescope of defined parameters. So treated as one unique set of parameters, not two (word and sentence).
I am seeing a big performance difference by making this change. ( i am trying in a little more complex problem of multi-class multi-label )
cell_word = BNLSTMCell(40, is_training) # h-h batchnorm LSTMCell
cell = GRUCell(30)
cell_word = MultiRNNCell([cell]*5)
cell_sent = BNLSTMCell(40, is_training) # h-h batchnorm LSTMCell
cell = GRUCell(30)
cell_sent = MultiRNNCell([cell]*5)
Similarly if you expect a different cell for forward and backward then you should define two more cells.
please correct me if I am wrong.
Other small side thing, according to my understanding, it's a general practice to not to dropout at eval time but it's happening as defined in the code here.
from hierarchical-attention-networks.
You might be right, my memory of TF's conventions is quite vague at this point.
from hierarchical-attention-networks.
Related Issues (20)
- some error in yelp_prepare.py HOT 4
- ValueError in running worker.py HOT 12
- How to make `TensorBoard Projector` work.
- Why use orthogonal_initializer ?
- Error While Running yelp_prepare.py HOT 3
- Is the embedding initialized with a pre-trained one? HOT 2
- GRU VS LSTM HOT 1
- Are uw and us global weights? just to conform. HOT 1
- Mask for attention weight
- Getting same sentence level outputs for very different documents. Can someone please help.
- Embeddings for special tokens/padding?
- dev accuracy: nan???
- en-core-web-sm needs to be installed beforehand
- Performance on the paper's dataset HOT 6
- Won't the code leads to different input shape for different batch?
- Visualize word and sentence attention weight as color coded in the paper HOT 1
- Performance on Yelp 15
- Implementation using tf.contrib.seq2seq. HOT 7
- Attention layer output HOT 5
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from hierarchical-attention-networks.