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global_loss = 0.
for i, (global_out, label) in enumerate(zip(global_outs, labels)):
global_label = label * tf.to_float(tf.greater(tf.reshape(valids, (-1, 1, 1, cfg.nr_skeleton)), 1.1))
global_loss += tf.reduce_mean(tf.square(global_out - global_label)) / len(labels)
global_loss /= 2.
QAQ can somebody help me
why / len(labels) and global_loss /= 2 ？
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