在 keras 中训练多类输入 output

Question

I am trying to train 16-bit binary input and 16-bit binary output for ANN using Keras. the problem is traing accuracy merely reaches 15%. What could be the best way to train datatypes like

Xtrain                       Ytrain
1,0,1,0,1,1,1,0,0,0,0,0=1,0,1,0,1,0,1,1,1,1
1,1,1,0,0,0,0,1,1,1,1,1=0,0,0,0,1,1,1,1,1,1

Xtest
1,1,1,1,0,1,0,1,0,1,0,1

X = dataset[:,0:16]
Y = dataset[:,16:32]



x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size = 0.15, random_state = 0)

clf_ann_ad = Sequential()
clf_ann_ad.add(Dense(32, activation = 'relu', input_dim = 16))
clf_ann_ad.add(Dense(16,activation='relu'))
clf_ann_ad.add(Dense(16,activation='sigmoid'))
clf_ann_ad.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])
history = clf_ann_ad.fit(x_train,y_train,batch_size = 100, nb_epoch = 200, validation_split = 0.1)
print(history.history.keys())

Answer 1

First One:

I'm thinking that your problem is a Sequence to a Sequence problem so i Think you need Recurrent Neural Network with uni Directional RNN to get the pattern how we encoding number and convert it:
Binaray Classification Using Recurrent

Second point: you use mean_square_error error function in classification problem that's not right that function used for telling you how close a regression line is to a set of points. so i you would use binary cross entropy which loss function used on problems involving yes/no (binary) decisions.

I apologize for the lack of an explicit and clear answer about your problem, I can not identify because you did not give us enough information