在非训练数据上使用训练权重来设计新的损失函数

Question

我想在训练迭代中访问训练点，并通过使用未包含在训练集中的数据点将软约束合并到我的损失函数中。我将使用this post 作为参考。

import numpy as np
import keras.backend as K
from keras.layers import Dense, Input
from keras.models import Model

# Some random training data and labels
features = np.random.rand(100, 5)
labels = np.random.rand(100, 2)

# Simple neural net with three outputs
input_layer = Input((20,))
hidden_layer = Dense(16)(input_layer)
output_layer = Dense(3)(hidden_layer)


# Model
model = Model(inputs=input_layer, outputs=output_layer)


#each training point has another data pair. In the real example, I will have multiple 
#supporters. That is why I am using dict.

holder =  np.random.rand(100, 5)
iter = np.arange(start=1, stop=features.shape[0], step=1)
supporters = {}

for i,j in zip(iter, holder): #i represent the ith training data
    supporters[i]=j


# Write a custom loss function
def custom_loss(y_true, y_pred):
    # Normal MSE loss
    mse = K.mean(K.square(y_true-y_pred), axis=-1)
    new_constraint = .... 

       

    return(mse+new_constraint)


model.compile(loss=custom_loss, optimizer='sgd')
model.fit(features, labels, epochs=1, ,batch_size=1=1)

为简单起见，让我们假设我想通过使用固定的网络权重来最小化预测值与存储在supporters 中的对数据的预测之间的最小绝对值差异。另外，假设我每批都通过一个训练点。但是，我无法弄清楚如何执行此操作。我尝试了如下所示的方法，但显然它不正确。

new_constraint = K.sum(y_pred - model.fit(supporters))

Answer 1

拟合是训练评估模型的过程。我认为您的问题最好使用当前权重加载模型的新实例并评估批量损失以计算主模型的损失。

main_model = Model()  # This is your main training model 

def custom_loss_1(y_true, y_pred):  # Avoid recursive calls
    mse = K.mean(K.square(y_true-y_pred), axis=-1)
    return mse

def custom_loss(y_true, y_pred):
    support_model =  tf.keras.models.clone_model(main_model)  # You copy the main model but the weights are uninitialized
    support_model.build((20,)) # You build with inputs same as your support data
    support_model.compile(loss=custom_loss_1, optimizer='sgd') 
    support_model.set_weights(main_model.get_weights())  # You  load the weight of the main model

    mse = custom_loss_1(y_true, y_pred)
    # You just want to evaluate the model, not to train. If you have more
    # metrics than just loss the use support_model.evaluate(supporters)[0]
    new_constraint = K.sum(y_pred -  support_model.predict(supporters))  # predict to get the output, evaluate to get the metrics

    return(mse+new_constraint)