Python 如何评估层状褶皱模型

我想通过分层折叠来训练[x_火车]，[y_火车] 并通过[x_检验]，[y_检验] 我该怎么办？

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我试过了。但我认为这是不合适的。

以分层的方式在培训和测试之间划分，您可以使用：

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

    x_train = dataset[0:700,:-1]
    y_train = dataset[0:700,-1]
    x_test = dataset[700:,:-1]
    y_test = dataset[700:,-1]

    def create_model():
    model = Sequential()
    model.add(Dense(12, input_dim=8, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

    model = KerasClassifier(build_fn=create_model, epochs=100, batch_size=64)
    skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed) 

    scores = cross_val_score(model, x_train, y_train, cv=skf)
    predictions = cross_val_predict(model, x_test, y_test, cv=skf)

检查：

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这个怎么样？这是一项工作，但我不知道它是否正确。

你的意思是你想以分层的方式进行训练和测试吗？是的，完全正确。我想以分层的方式进行训练和测试。train（x_train，y_train）和test（x_test，y_test）请检查我的答案。为什么循环3次，每次都创建一个新模型（覆盖），而不在循环中使用迭代变量

train

？

from sklearn.model_selection import train_test_split
dataset_train, dataset_test = train_test_split(dataset,
                                                stratify=dataset[:,-1], 
                                                test_size=0.2)

#split both datasets into X,y

skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)
accuracy=[]
for train in skf.split(x_train, y_train):
    model = Sequential()
    model.add(Dense(12, input_dim=8, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])