如何将参数传递给 Scikit-Learn Keras 模型函数 [英] How to pass a parameter to Scikit-Learn Keras model function

问题描述

我有以下代码，使用 Keras Scikit-Learn Wrapper，效果很好:

I have the following code, using Keras Scikit-Learn Wrapper, which work fine:

from keras.models import Sequential
from keras.layers import Dense
from sklearn import datasets
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy as np


def create_model():
    # create model
    model = Sequential()
    model.add(Dense(12, input_dim=4, init='uniform', activation='relu'))
    model.add(Dense(6, init='uniform', activation='relu'))
    model.add(Dense(1, init='uniform', activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


def main():
    """
    Description of main
    """


    iris = datasets.load_iris()
    X, y = iris.data, iris.target

    NOF_ROW, NOF_COL =  X.shape

    # evaluate using 10-fold cross validation
    seed = 7
    np.random.seed(seed)
    model = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10, verbose=0)
    kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
    results = cross_val_score(model, X, y, cv=kfold)

    print(results.mean())
    # 0.666666666667


if __name__ == '__main__':
    main()

pima-indians-diabetes.data 可以下载 此处.

The pima-indians-diabetes.data can be downloaded here.

现在我想要做的是通过以下方式将值NOF_COL传递给create_model()函数的参数

Now what I want to do is to pass a value NOF_COL into a parameter of create_model() function the following way

model = KerasClassifier(build_fn=create_model(input_dim=NOF_COL), nb_epoch=150, batch_size=10, verbose=0)

使用如下所示的 create_model() 函数:

With the create_model() function that looks like this:

def create_model(input_dim=None):
    # create model
    model = Sequential()
    model.add(Dense(12, input_dim=input_dim, init='uniform', activation='relu'))
    model.add(Dense(6, init='uniform', activation='relu'))
    model.add(Dense(1, init='uniform', activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

但它没有给出这个错误:

But it fails giving this error:

TypeError: __call__() takes at least 2 arguments (1 given)

正确的做法是什么?

推荐答案

您可以在 KerasClassifier 构造函数中添加一个 input_dim 关键字参数:

You can add an input_dim keyword argument to the KerasClassifier constructor:

model = KerasClassifier(build_fn=create_model, input_dim=5, nb_epoch=150, batch_size=10, verbose=0)

这篇关于如何将参数传递给 Scikit-Learn Keras 模型函数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！