Comments (4)
Update, running it on the THEANO backend with an older version of Keras seems to bring much better results... I guess I will have to keep digging as to where the differences are coming from!
from py_crepe.
I seem to encounter the same problem, basically using the same code and fix a few minor place to be able to run on python 3, but the loss is not reducing and acc stays at 0.25
from py_crepe.
Saw discussion from another issue post, basically you seem to have to use RandomNormal as mentioned here https://keras.io/initializers/, instead of using the default glorot_uniform
Just to add it in all the convolution layers like this
initializer = RandomNormal(mean=0.0, stddev=0.05, seed=None)
...
conv = Convolution1D(nb_filter=nb_filter, filter_length=filter_kernels[0], kernel_initializer=initializer,
border_mode='valid', activation='relu',
input_shape=(maxlen, vocab_size))(inputs)
Initialization matters !!!
from py_crepe.
I finished running the neural network with ag_news set, using Keras version 2.1.5, Python 3.6.5, and I couldn't get Theano version, it seems it isn't installed. Doing it without any initializer in convolution layer, my network managed to get between 87%~92% of accuracy
Epoch: 8
Step: 100
Loss: 0.2301556259393692. Accuracy: 0.9221250146627427
Step: 200
Loss: 0.23537617575377226. Accuracy: 0.9201875126361847
Step: 300
Loss: 0.23534504453341165. Accuracy: 0.9205833458900452
Step: 400
Loss: 0.23604939280077816. Accuracy: 0.9202500122785569
Step: 500
Loss: 0.23330221936106682. Accuracy: 0.9211000119447708
Step: 600
Loss: 0.2331778311356902. Accuracy: 0.9213541778922081
Step: 700
Loss: 0.2310873696208. Accuracy: 0.9223928680590221
Step: 800
Loss: 0.23309540571644902. Accuracy: 0.9215625112503767
Step: 900
Loss: 0.23280121087200112. Accuracy: 0.9213472335868411
Step: 1000
Loss: 0.234288040317595. Accuracy: 0.9208625118732452
Step: 1100
Loss: 0.23586858349090273. Accuracy: 0.9205227390744469
Step: 1200
Loss: 0.2381015682592988. Accuracy: 0.9195833456019561
Step: 1300
Loss: 0.2377806937465301. Accuracy: 0.9194711661338806
Step: 1400
Loss: 0.2373836720788053. Accuracy: 0.9197500126702445
Step: 1500
Loss: 0.23760153172910214. Accuracy: 0.9196000128587087
Epoch 8. Loss: 0.3596184071741606. Accuracy: 0.8811842416462146
Epoch time: 0:07:20.014854. Total time: 1:08:38.125534
Epoch: 9
Step: 100
Loss: 0.21075385928153992. Accuracy: 0.9301250070333481
Step: 200
Loss: 0.21289151340723036. Accuracy: 0.9299375078082085
Step: 300
Loss: 0.21473157433172066. Accuracy: 0.9292916737000148
Step: 400
Loss: 0.21344165759161116. Accuracy: 0.9293750070035458
Step: 500
Loss: 0.21545475232601166. Accuracy: 0.9288500069379807
Step: 600
Loss: 0.21487088636805615. Accuracy: 0.9284791740775108
Step: 700
Loss: 0.21380332414593015. Accuracy: 0.9287678642783846
Step: 800
Loss: 0.21491042390465737. Accuracy: 0.9276875076442956
Step: 900
Loss: 0.21594439257350234. Accuracy: 0.9273194525639216
Step: 1000
Loss: 0.21687575853615998. Accuracy: 0.9271750084161758
Step: 1100
Loss: 0.21711503727869555. Accuracy: 0.9269545538858934
Step: 1200
Loss: 0.21854913759355743. Accuracy: 0.9265833417574565
Step: 1300
Loss: 0.21921507142484187. Accuracy: 0.9262211626768112
Step: 1400
Loss: 0.21991488710578. Accuracy: 0.9260446515253612
Step: 1500
Loss: 0.21934504335621993. Accuracy: 0.9262916754086812
Epoch 9. Loss: 0.3565275182849483. Accuracy: 0.8784210826221265
Epoch time: 0:07:19.129080. Total time: 1:16:13.310350
from py_crepe.
Related Issues (9)
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 py_crepe.