Python 用keras和顺序训练并行模型

我有下面的代码，它并行地使用不同的数据集训练相同的模型。我想知道我在这段代码中得到的结果与我使用相应的数据集一个接一个地训练模型的结果有什么不同。非常感谢您的帮助

from keras.layers import *
from keras.models import Model, Sequential
import numpy as np
import random
import test
index=[]
for i in range(10):
    index.append(random.sample(range(0, 201), 10))
x_tr=[]
y_tr=[]
x_te=[]
y_te=[]

aa = test.train_data(index[0])
X_train0, Y_train0, X_Test0, Y_Test0 = test.ddata(aa)
aa = test.train_data(index[1])
X_train1, Y_train1, X_Test1, Y_Test1 = test.ddata(aa)
aa = test.train_data(index[2])
X_train2, Y_train2, X_Test2, Y_Test2 = test.ddata(aa)
aa = test.train_data(index[3])
X_train3, Y_train3, X_Test3, Y_Test3 = test.ddata(aa)
aa = test.train_data(index[4])
X_train4, Y_train4, X_Test4, Y_Test4 = test.ddata(aa)
aa = test.train_data(index[5])
X_train5, Y_train5, X_Test5, Y_Test5 = test.ddata(aa)
aa = test.train_data(index[6])
X_train6, Y_train6, X_Test6,Y_Test6 = test.ddata(aa)
aa = test.train_data(index[7])
X_train7, Y_train7, X_Test7, Y_Test7 = test.ddata(aa)
aa = test.train_data(index[8])
X_train8, Y_train8, X_Test8, Y_Test8 = test.ddata(aa)
aa = test.train_data(index[9])
X_train9, Y_train9, X_Test9, Y_Test9 = test.ddata(aa)


m=test.get_model()
inp0=Input((5,10,10,1))
inp1=Input((5,10,10,1))
inp2=Input((5,10,10,1))
inp3=Input((5,10,10,1))
inp4=Input((5,10,10,1))
inp5=Input((5,10,10,1))
inp6=Input((5,10,10,1))
inp7=Input((5,10,10,1))
inp8=Input((5,10,10,1))
inp9=Input((5,10,10,1))
out0=m(inp0)
out1=m(inp1)
out2=m(inp2)
out3=m(inp3)
out4=m(inp4)
out5=m(inp5)
out6=m(inp6)
out7=m(inp7)
out8=m(inp8)
out9=m(inp9)





model = Model([inp0,inp1,inp2,inp3,inp4,inp5,inp6,inp7,inp8,inp9],[out0,out1,out2,out3,out4,out5,out6,out7,out8,out9])
model.compile(optimizer='adam', loss='mse')
model.fit([X_train0,X_train1,X_train2,X_train3,X_train4,X_train5,X_train6,X_train7,X_train8,X_train9],[Y_train0,Y_train1,Y_train2,Y_train3,Y_train4,Y_train5,Y_train6,Y_train7,Y_train8,Y_train9], epochs = 50)
ypred0,ypred1,ypred2,ypred3,ypred4,ypred5,ypred6,ypred7,ypred8,ypred9 = model.predict([X_Test0,X_Test1,X_Test2,X_Test3,X_Test4,X_Test5,X_Test6,X_Test7,X_Test8,X_Test9])
print(ypred0.shape)

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FYI测试是我获取数据的另一个途径。

最好是并行或一次性训练，而不是顺序训练

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如果按顺序进行训练，可能会在训练模型的最后一个数据集上过度拟合。

如果我有完全不同的数据集，并行训练是否仍然有效。例如，cat vs apple的图像。这两个模型的架构是相同的。是的，它会比顺序训练提供更好的结果