在 3 个合并的深度神经网络模型中交叉验证而不是训练和测试

如何解决在 3 个合并的深度神经网络模型中交叉验证而不是训练和测试

如何在这个深度神经网络代码中使用交叉验证方法而不是 train_test split 实际上，我正在合并3个深度神经网络模型......首先我合并了3个深度神经网络模型，然后进行分类。 .由于准确性问题，我想使用交叉验证方法而不是训练测试拆分，请指导我如何在此深度神经网络代码中使用交叉验证方法而不是 train_ 测试拆分...

这是我的代码，如果有人可以将此代码训练测试更改为交叉验证，那么我将非常感激

from keras.layers import Dense
from sklearn.model_selection import train_test_split
from keras.models import Model
from keras.layers import Input 
import tensorflow as tf
import os,numpy


# random seed for reproducibility

numpy.random.seed(101)
tf.random.set_seed(101)
os.environ['TF_CUDNN_DETERMINISTIC'] = '1'

# loading load pima indians diabetes dataset,past 5 years of medical history
dataset = numpy.loadtxt('https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv',delimiter=",")
# split into input (X) and output (Y) variables,splitting csv data
X = dataset[:,0:8]
Y = dataset[:,8]
x_train,x_validation,y_train,y_validation = train_test_split(
    X,Y,test_size=0.20,random_state=5)

# Define Model A 
input_layer = Input(shape=(8,))
A2 = Dense(10,activation='relu')(input_layer)
A3 = Dense(50,activation='relu')(A2)
A4 = Dense(50,activation='relu')(A3)
A5 = Dense(50,activation='relu')(A4)
A6 = Dense(50,activation='relu')(A5)
A7 = Dense(50,activation='relu')(A6)
A8 = Dense(10,activation='relu')(A7)
A9 = Dense(5,activation='relu')(A8)
model_a = Model(inputs=input_layer,outputs=A9,name="ModelA")

# Define Model B 
input_layer = Input(shape=(8,))
B2 = Dense(13,activation='relu')(input_layer)
B3 = Dense(12,activation='relu')(B2)
B4 = Dense(16,activation='relu')(B3)
B5 = Dense(16,activation='relu')(B4)
B6 = Dense(14,activation='relu')(B5)
B7 = Dense(5,activation='relu')(B6)
model_b = Model(inputs=input_layer,outputs=B7,name="ModelB")

# Define Model C
input_layer = Input(shape=(8,))
C2 = Dense(33,activation='relu')(input_layer)
C3 = Dense(23,activation='relu')(C2)
C4 = Dense(17,activation='relu')(C3)
C5 = Dense(9,activation='relu')(C4)
C6 = Dense(17,activation='relu')(C5)
C7 = Dense(13,activation='relu')(C6)
C8 = Dense(9,activation='relu')(C7)
C9 = Dense(9,activation='relu')(C8)
C10 = Dense(5,activation='relu')(C9)
model_c = Model(inputs=input_layer,outputs=C10,name="ModelC")
all_three_models = [model_a,model_b,model_c]
all_three_models_input = Input(shape=all_three_models[0].input_shape[1:])


models_output = [model(all_three_models_input) for model in all_three_models]
Concat           = tf.keras.layers.concatenate(models_output,name="Concatenate")
final_out     = Dense(1,activation='sigmoid')(Concat)

final_model   = Model(inputs=all_three_models_input,outputs=final_out,name='Ensemble')

# call the function to fit to the data (training the network)
final_model.compile(loss="binary_crossentropy",optimizer="adam",metrics=['accuracy'])

model_save = tf.keras.callbacks.ModelCheckpoint(
                'merge_best.h5',monitor="val_accuracy",verbose=0,save_best_only=True,save_weights_only=True,mode="max",save_freq="epoch"
            )

# call the function to fit to the data (training the network)
final_model.fit(x_train,epochs=1000,batch_size=256,callbacks=[model_save],validation_data=(x_validation,y_validation))


# evaluate the model
final_model.load_weights('merge_best.h5')
scores = final_model.evaluate(x_validation,y_validation)
print("\n%s: %.2f%%" % (final_model.metrics_names[1],scores[1] * 100))
final_model.save('diabetes_risk_nn.h5')