Comments (3)
I am currently using this:
class NeuralNetFix(NeuralNet):
def train_test_split(self, X, y, eval_size):
assert eval_size is None
X_train = X
y_train = y
X_valid = self.X_valid
y_valid = self.y_valid
if not self.regression and self.use_label_encoder:
y_valid = self.enc_.transform(y_valid).astype(np.int32)
return X_train, X_valid, y_train, y_valid
I then handle the train_test_split
myself.
UPDATE: I no longer read X_valid
and y_valid
from self
.
from nolearn.
@cancan101 To answer your first question: You'll notice that NeuralNet.train_test_split
uses StratifiedKFold
for classification tasks. That's different to what sklearn's train_test_split
would give you.
But maybe the stratified split isn't all that important, and we can just use an overridable train_test_split (component) by default.
from nolearn.
I think we can merge this discussion with #42, and close this one. Also consider #45 when thinking about a better way to override the train_test_split
method.
from nolearn.
Related Issues (20)
- RememberBestWeights does not honor the verbose parameter HOT 2
- A replayable fit() method - diff/patch attached HOT 1
- remove('trainable') Lasagne's command doesn't work in nolearn HOT 6
- flip_filters and pad parameter not used by NeuralNet's class HOT 5
- OSError: could not read bytes when trying to fetch mldata HOT 2
- CUDA error, possibly related to network size? HOT 2
- Trained on GPU, inference on CPU doesn't make sense
- Install nolearn with Lasagne dependance not working HOT 2
- Bug in calculating average scores
- nolearn is not installing
- Bug when using Lasagne `mask_input` parameter
- 'NeuralNet' object has no attribute 'layers_' HOT 1
- Weights sum up to zero
- Future issue with sklearn.cross_validation
- Dependency on both backends in requirements.txt switches off GPU support HOT 3
- Enable to reproduce the last value of trainning when predicting CNN
- enable to reproduce loss value of training when predicting CNN HOT 1
- python 3 support not working with Lasagne? HOT 12
- TypeError: Failed to instantiate <class 'lasagne.layers.pool.MaxPool2DLayer'> with args {'name': 'pool1', 'ds': (2, 2), 'incoming': <lasagne.layers.conv.Conv2DLayer object at 0x7ff765fa29e8>}. Maybe parameter names have changed?
- nolearn now on conda-forge HOT 1
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 nolearn.