在TensorFlow中修改成本函数

Question

I want to modify the following cost function in a way that it adds extra weight to the samples where the prediction is higher than the true output!

cost = tf.reduce_sum(tf.pow(logits-Y, 2))/(2*batch_size)

I found it to be tricky in Tensorflow operations! I want to use Tensorflow operations to do the following codes (written by numpy):

batch_szie = 100
label = np.random.normal(size=batch_szie)
cost = (np.sum(np.power((2*label [label >=0]),2)) + np.sum(np.power((2*label [label <0]),2)))/batch_szie 

Please note that the first two lines are just for simulating the label = logits-Y .

Any help/suggestion? Thanks :)

Answer 1

Here I found an answer to this question. However, I think there should be easier and more concise ways.

batch_size = 4
labels = tf.constant ([1,-1,2,1])
pos_index = tf.where(tf.greater_equal(labels, 0))
pos_index = tf.reshape(pos_index, [-1])
pos_label = 5 * tf.gather(labels, pos_index)
neg_index = tf.where(tf.less_equal(labels, 0))
neg_index = tf.reshape(neg_index, [-1])
neg_label = tf.gather(labels, neg_index)
cost = (tf.reduce_sum(tf.pow(pos_label, 2)) + tf.reduce_sum(tf.pow(neg_label, 2)))/(2*batch_size)
with tf.Session() as sess:
     print(sess.run(cost))