Github 项目推荐 | 兼容 Scikit-Learn 的 PyTorch 神经网络库 —— skorch

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发布于 2018-07-26 11:18:08

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发布于 2018-07-26 11:18:08

文章被收录于专栏：AI研习社AI研习社

Skorch 是一个兼容 Scikit-Learn 的 PyTorch 神经网络库。

资源

文档：

https://skorch.readthedocs.io/en/latest/?badge=latest

源代码

https://github.com/dnouri/skorch/

示例

更详细的例子，请查看此链接：

https://github.com/dnouri/skorch/tree/master/notebooks/README.md

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)
y = y.astype(np.int64)

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), dim=-1)
        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_)

安装

pip 安装

pip install -U skorch

建议使用虚拟环境。

源代码安装

如果你想使用 skorch 最新的案例或者开发帮助，请使用源代码安装

用 conda

如果你需要一个工作conda安装，从这里为的的系统获取正确的 miniconda：

https://conda.io/miniconda.html

如果你只是使用 skorch：

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

如果你只想帮助开发，运行：

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

用 pip

如果你只是使用 skorch：

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

如果你想使用帮助开发：

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

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原始发表：2018-05-09，如有侵权请联系 cloudcommunity@tencent.com 删除

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