43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
|
import tensorflow as tf
|
||
|
|
|
||
|
|
# DISCLAIMER:
|
||
|
|
# Parts of this code file were originally forked from
|
||
|
|
# https://github.com/tkipf/gcn
|
||
|
|
# which itself was very inspired by the keras package
|
||
|
|
# (A full license with de-anonymized attributions will be provided in the
|
||
|
|
# public repo of this code base)
|
||
|
|
|
||
|
|
def masked_logit_cross_entropy(preds, labels, mask):
|
||
|
|
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=preds, labels=labels)
|
||
|
|
loss = tf.reduce_sum(loss, axis=1)
|
||
|
|
mask = tf.cast(mask, dtype=tf.float32)
|
||
|
|
mask /= tf.maximum(tf.reduce_sum(mask), tf.constant([1.]))
|
||
|
|
loss *= mask
|
||
|
|
return tf.reduce_mean(loss)
|
||
|
|
|
||
|
|
def masked_softmax_cross_entropy(preds, labels, mask):
|
||
|
|
loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels)
|
||
|
|
# loss = tf.reduce_sum(loss, axis=1)
|
||
|
|
mask = tf.cast(mask, dtype=tf.float32)
|
||
|
|
mask /= tf.maximum(tf.reduce_sum(mask), tf.constant([1.]))
|
||
|
|
loss *= mask
|
||
|
|
return tf.reduce_mean(loss)
|
||
|
|
|
||
|
|
|
||
|
|
def masked_l2(preds, actuals, mask):
|
||
|
|
"""Softmax cross-entropy loss with masking."""
|
||
|
|
loss = tf.nn.l2(preds, actuals)
|
||
|
|
mask = tf.cast(mask, dtype=tf.float32)
|
||
|
|
mask /= tf.reduce_mean(mask)
|
||
|
|
loss *= mask
|
||
|
|
return tf.reduce_mean(loss)
|
||
|
|
|
||
|
|
def masked_accuracy(preds, labels, mask):
|
||
|
|
"""Accuracy with masking."""
|
||
|
|
correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(labels, 1))
|
||
|
|
accuracy_all = tf.cast(correct_prediction, tf.float32)
|
||
|
|
mask = tf.cast(mask, dtype=tf.float32)
|
||
|
|
mask /= tf.reduce_mean(mask)
|
||
|
|
accuracy_all *= mask
|
||
|
|
return tf.reduce_mean(accuracy_all)
|