使用Tensorflow或Keras通过馈入``成对''样本来构建NN模型

Question

I'm trying to implement a NN model with pairwise samples. Details are shown in follows:

Original data:

• X_org with shape of (100, 50) for example, namely 100 samples with 50 features.
• Y_org with shape of (100, 1).

Processing these original data for real training:

Select 2 samples from X_org randomly (so we have 100*99/2 such combinations) to form a new 'pairwise' sample, and the prediction target, namely the new y label is the subtraction of the two corresponding y_org labels (Y_org_sample1 - Y_org_sample2). Now we have new X_train and Y_train .

I need a more a NN model (DNN, CNN, LSTM, whatever ...), with which I can pass the first sub_sample of one pairwise sample from X_train into the model and will get one result, same step for the second sub_sample. By calculating the subtraction of the two results, I can get the prediction of this pairwise sample. This prediction will be the one compared with the corresponding Y label from Y_train .

Overall, I need to train a model (update the weights) after feeding it a 'pairwise' sample (two successive sub samples). The reason why I don't choose a 'two-arm' model (eg merge two arms by xxx.sub() ) is that I will only feed one sub sample during test process. I will just use the model to predict one sub-sample finally.

So I will use the data from X_train during train step, while use X_org-like data format during test step. It looks a bit complex.

Looks like Tensorflow would be more feasible for this task, if keras also works, please kindly share your idea.

Answer 1

You can first create a model that will take only one X_org-like element:

#create a model the way you like it, it can be Functional API or Sequential, no problem
xOrgModel = createAModelForXOrgData(...)

Now, lets create a second model, this time necessarily functional API that works with both inputs:

from keras.models import Model
from keras.layers import Input, Subtract

input1 = Input(shapeOfInput)
input2 = Input(shapeOfInput)

output1 = xOrgModel(input1)
output2 = xOrgModel(input2)

output = Subtract()([output1,output2])

pairWiseModel = Model([input1,input2],output)

Now you have two models: xOrgModel and pairWiseModel . You can use any of them depending on the task you are doing at the moment.

Both models are sharing their weights. This means that you can train any of them and the other will be updated as well.

Using the pairwise model

First, organize your data in two separate arrays. (Because our model uses two inputs)

L = len(X_org)
x1 = []
x2 = []
y = []

for i in range(L):
    for j in range(i+1,L):
        x1.append(X_org[i])
        x2.append(X_org[j])
        y.append(Y_org[i] - Y_org[j])

x1 = np.array(x1) 
x2 = np.array(x2) 
y = np.array(y) 

Train and predict with a list of inputs:

pairWiseModel.fit([x1,x2],y,...)