问向Tensorflow添加自定义正则化

EN

train_x ,train_y are the training data, training targets respectively.
test_x  , test_y are the testing data, testing targets respectively.

#Sum of Squared Errs. Cost.
def costfunc(predicted,actual):
    return tf.reduce_sum(tf.square(predicted - actual))

#Mean Squared Error Calc.
def prediction(sess,X,y_,test_x,test_y):
    pred_y = sess.run(y_,feed_dict={X:test_x})
    mymse = tf.reduce_mean(tf.square(pred_y - test_y))
    mseval=sess.run(mymse)

    return mseval,pred_y


with tf.Session() as sess:

    X = tf.placeholder(tf.float32,[None,num_feat])  #Training Data 
    Y = tf.placeholder(tf.float32,[None,1]) #  Target Values 
    W = tf.Variable(tf.ones([num_feat,1]),name="weights")  

    init = tf.global_variables_initializer()

    sess.run(init)

    #Tensorflow ops and cost function definitions.
    y_ = tf.matmul(X,W) 
    cost_history = np.empty(shape=[1],dtype=float)
    out_of_sample_cost_history = np.empty(shape=[1],dtype=float)
    cost=costfunc(y_,Y) 
    learning_rate = 0.000001
    training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)


    for epoch in range(training_epochs):
        sess.run(training_step,feed_dict={X:train_x,Y:train_y})
        cost_history = np.append(cost_history,sess.run(cost,feed_dict={X: train_x,Y: train_y}))
        out_of_sample_cost_history = np.append(out_of_sample_cost_history,sess.run(cost,feed_dict={X:test_x,Y:test_y}))


    MSETest,pred_test = prediction(sess,X,y_,test_x,test_y) #Predict on full testing set.