A scikit-learn compatible neural network library that wraps pytorch.
Resources:
To see a more elaborate example, look here.
import numpy as np
from sklearn.datasets import make_classification
import torch
from torch import nn
import torch.nn.functional as F
from skorch.net import NeuralNetClassifier
X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X = X.astype(np.float32)
class MyModule(nn.Module):
def __init__(self, num_units=10, nonlin=F.relu):
super(MyModule, self).__init__()
self.dense0 = nn.Linear(20, num_units)
self.nonlin = nonlin
self.dropout = nn.Dropout(0.5)
self.dense1 = nn.Linear(num_units, 10)
self.output = nn.Linear(10, 2)
def forward(self, X, **kwargs):
X = self.nonlin(self.dense0(X))
X = self.dropout(X)
X = F.relu(self.dense1(X))
X = F.softmax(self.output(X))
return X
net = NeuralNetClassifier(
MyModule,
max_epochs=10,
lr=0.1,
)
net.fit(X, y)
y_proba = net.predict_proba(X)
In an sklearn Pipeline:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
pipe = Pipeline([
('scale', StandardScaler()),
('net', net),
])
pipe.fit(X, y)
y_proba = pipe.predict_proba(X)
With grid search
from sklearn.model_selection import GridSearchCV
params = {
'lr': [0.01, 0.02],
'max_epochs': [10, 20],
'module__num_units': [10, 20],
}
gs = GridSearchCV(net, params, refit=False, cv=3, scoring='accuracy')
gs.fit(X, y)
print(gs.best_score_, gs.best_params_)
To install with pip, run:
pip install -U skorch
We recommend to use a virtual environment for this.
If you would like to use the must recent additions to skorch
or
help development, you should install skorch
from source.
You need a working conda installation. Get the correct miniconda for your system from here.
If you just want to use skorch
, use:
git clone https://github.com/dnouri/skorch.git
cd skorch
conda env create
source activate skorch
# install pytorch version for your system (see below)
python setup.py install
If you want to help developing, run:
git clone https://github.com/dnouri/skorch.git
cd skorch
conda env create
source activate skorch
# install pytorch version for your system (see below)
conda install --file requirements-dev.txt
python setup.py develop
py.test # unit tests
pylint skorch # static code checks
If you just want to use skorch
, use:
git clone https://github.com/dnouri/skorch.git
cd skorch
# create and activate a virtual environment
pip install -r requirements.txt
# install pytorch version for your system (see below)
python setup.py install
If you want to help developing, run:
git clone https://github.com/dnouri/skorch.git
cd skorch
# create and activate a virtual environment
pip install -r requirements.txt
# install pytorch version for your system (see below)
pip install -r requirements-dev.txt
python setup.py develop
py.test # unit tests
pylint skorch # static code checks
pytorch
is not covered by the dependencies, since the pytorch
version you need is dependent on your system. For installation
instructions for pytorch
, visit the pytorch website.
In general, this should work:
# using conda:
conda install pytorch cuda80 -c soumith
# using pip
pip install http://download.pytorch.org/whl/cu80/torch-0.2.0.post3-cp36-cp36m-manylinux1_x86_64.whl