TensorFlow中的自定义损失函数用于对训练数据进行加权

Question

I want to weight the training data based on a column in the training data set. Thereby giving more importance to certain training items than others. The weighting column should not be included as a feature for the input layer.

The Tensorflow documentation holds an example how to use the label of the item to assign a custom loss and thereby assigning weight:

# Ensures that the loss for examples whose ground truth class is `3` is 5x
# higher than the loss for all other examples.
weight = tf.multiply(4, tf.cast(tf.equal(labels, 3), tf.float32)) + 1

onehot_labels = tf.one_hot(labels, num_classes=5)
tf.contrib.losses.softmax_cross_entropy(logits, onehot_labels, weight=weight)

I am using this in a custom DNN with three hidden layers. In theory i simply need to replace labels in the example above with a tensor containing the weight column.

I am aware that there are several threads that already discuss similar problems eg defined loss function in tensorflow?

For some reason i am running into a lot of problems trying to bring my weight column in. It's probably two easy lines of code or maybe there is an easier way to achieve the same result.

Answer 1

I believe i found the answer:

  weight_tf = tf.range(features.get_shape()[0]-1, features.get_shape()[0])
  loss = tf.losses.softmax_cross_entropy(target, logits, weights=weight_tf)

The weight is the last column in the features tensorflow.