将sklearn.svm SVC分类器转换为Keras实现 [英] Convert sklearn.svm SVC classifier to Keras implementation

问题描述

我正在尝试将一些旧代码从使用sklearn转换为Keras实现.由于维持相同的操作方式至关重要，因此我想了解自己是否正确执行操作.

I'm trying to convert some old code from using sklearn to Keras implementation. Since it is crucial to maintain the same way of operation, I want to understand if I'm doing it correctly.

我已经转换了大多数代码，但是sklearn.svm SVC分类器转换遇到了麻烦.这是现在的样子:

I've converted most of the code already, however I'm having trouble with sklearn.svm SVC classifier conversion. Here is how it looks right now:

from sklearn.svm import SVC
model = SVC(kernel='linear', probability=True)
model.fit(X, Y_labels)

超级简单，对.但是，我在Keras中找不到SVC分类器的类似物.所以，我尝试过的是这样:

Super easy, right. However, I couldn't find the analog of SVC classifier in Keras. So, what I've tried is this:

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='softmax'))
model.compile(loss='squared_hinge',
              optimizer='adadelta',
              metrics=['accuracy'])
model.fit(X, Y_labels)

但是，我认为这绝对是不正确的.您能帮我从Keras的sklearn中找到SVC分类器的替代方法吗?

But, I think that it is not correct by any means. Could you, please, help me find an alternative of the SVC classifier from sklearn in Keras?

谢谢.

推荐答案

如果要进行分类，则需要squared_hinge和regularizer，以获得完整的SVM丢失功能，如

If you are making a classifier, you need squared_hinge and regularizer, to get the complete SVM loss function as can be seen here. So you will also need to break your last layer to add regularization parameter before performing activation, I have added the code here.

这些更改应该为您提供输出

These changes should give you the output

from keras.regularizers import l2
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(64, activation='relu'))
model.add(Dense(1), W_regularizer=l2(0.01))
model.add(activation('softmax'))
model.compile(loss='squared_hinge',
              optimizer='adadelta',
              metrics=['accuracy'])
model.fit(X, Y_labels)

hinge也是在keras中实现的，用于二进制分类，因此，如果您正在使用二进制分类模型，请使用下面的代码.

Also hinge is implemented in keras for binary classification, so if you are working on a binary classification model, use the code below.

from keras.regularizers import l2
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(64, activation='relu'))
model.add(Dense(1), W_regularizer=l2(0.01))
model.add(activation('linear'))
model.compile(loss='hinge',
              optimizer='adadelta',
              metrics=['accuracy'])
model.fit(X, Y_labels)

如果您无法理解本文或对代码有疑问，请随时发表评论. 我前一段时间也遇到过同样的问题，这个GitHub线程帮助我理解了，也许也经历了，这里的一些想法直接来自这里

If you cannot understand the article or have issues with the code, feel free to comment. I had this same issue a while back, and this GitHub thread helped me understand, maybe go through it too, some of the ideas here are directly from here https://github.com/keras-team/keras/issues/2588

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