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我读过，你不能与Keras进行交叉验证，当你也想使用模型回调，但然后this post表明它是可能的。但是，我很难将其融入到我的背景中。Keras回调时运行交叉验证
为了更详细地了解这一点，我正在关注machinelearningmastery blog，并使用the iris dataset。
这是一个三类分类问题，我试图使用多层感知器（现在为测试层）。我现在的目标是在模型回调中工作，以便保存最佳模型的权重。下面，我尝试在我的部分network_mlp。为了表明模型没有回调，我还包括network_mlp_no_callbacks。
你应该能够复制/粘贴到python会话并运行它，没问题。要复制我看到的错误，请取消注释最后一行。
错误：RuntimeError: Cannot clone object <keras.wrappers.scikit_learn.KerasClassifier object at 0x7f7e1c9d2290>, as the constructor does not seem to set parameter callbacks
编码：第一部分中的数据读出;其次是具有回调的模型，这是不工作的;第三是没有回调的模型，它可以工作（提供上下文）。
#!/usr/bin/env python
import numpy as np
import pandas, math, sys, keras
from keras.models import Sequential
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from keras.utils import np_utils
from keras.utils.np_utils import to_categorical
from sklearn.preprocessing import LabelEncoder
def read_data_mlp(train_file):
train_data = pandas.read_csv("iris.csv", header=None)
train_data = train_data.values
X = train_data[:,0:4].astype(float)
Y = train_data[:,4]
X = X.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)
X_train_s = scaler.fit_transform(X)
return (X_train_s, dummy_y)
def network_mlp(X, Y, out_dim=10, b_size=30, num_classes=3, epochs=10):
#out_dim is the dimensionality of the hidden layer;
#b_size is the batch size. There are 150 examples total.
filepath="weights_mlp.hdf5"
def mlp_model():
model = Sequential()
model.add(Dense(out_dim, input_dim=4, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
estimator = KerasClassifier(build_fn=mlp_model, epochs=epochs, batch_size=b_size, verbose=0, callbacks=callbacks_list)
kfold = KFold(n_splits=10, shuffle=True, random_state=7)
results = cross_val_score(estimator, X, Y, cv=kfold)
print("MLP: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
return 0
def network_mlp_no_callbacks(X, Y, out_dim=10, b_size=30, num_classes=3, epochs=10):
def mlp_model():
model = Sequential()
model.add(Dense(out_dim, input_dim=4, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
estimator = KerasClassifier(build_fn=mlp_model, epochs=epochs, batch_size=b_size, verbose=0)
kfold = KFold(n_splits=10, shuffle=True, random_state=7)
results = cross_val_score(estimator, X, Y, cv=kfold)
print("MLP: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
return 0
if __name__=='__main__':
X, Y = read_data_mlp('iris.csv')
network_mlp_no_callbacks(X, Y, out_dim=10, b_size=30, num_classes=3, epochs = 10)
#network_mlp(X, Y, out_dim=10, b_size=30, num_classes=3, epochs = 10)
问题：如何将模型回调合并到KerasClassifier中？