gamma>0使得减少易分类样本的损失。使得更关注于困难的、错分的样本。
加入平衡因子alpha，用来平衡正负样本本身的比例不均：文中alpha取0.25，即正样本要比负样本占比小，这是因为负例易分。
- https://www.cnblogs.com/king-lps/p/9497836.html
- https://blog.csdn.net/u014380165/article/details/77019084
# focal loss with multi label
def focal_loss(classes_num, gamma=2., alpha=.25, e=0.1):
# classes_num contains sample number of each classes
def focal_loss_fixed(target_tensor, prediction_tensor):
'''
prediction_tensor is the output tensor with shape [None, 100], where 100 is the number of classes
target_tensor is the label tensor, same shape as predcition_tensor
'''
import tensorflow as tf
from tensorflow.python.ops import array_ops
from keras import backend as K
#1# get focal loss with no balanced weight which presented in paper function (4)
zeros = array_ops.zeros_like(prediction_tensor, dtype=prediction_tensor.dtype)
one_minus_p = array_ops.where(tf.greater(target_tensor,zeros), target_tensor - prediction_tensor, zeros)
FT = -1 * (one_minus_p ** gamma) * tf.log(tf.clip_by_value(prediction_tensor, 1e-8, 1.0))
#2# get balanced weight alpha
classes_weight = array_ops.zeros_like(prediction_tensor, dtype=prediction_tensor.dtype)
total_num = float(sum(classes_num))
classes_w_t1 = [ total_num / ff for ff in classes_num ]
sum_ = sum(classes_w_t1)
classes_w_t2 = [ ff/sum_ for ff in classes_w_t1 ] #scale
classes_w_tensor = tf.convert_to_tensor(classes_w_t2, dtype=prediction_tensor.dtype)
classes_weight += classes_w_tensor
alpha = array_ops.where(tf.greater(target_tensor, zeros), classes_weight, zeros)
#3# get balanced focal loss
balanced_fl = alpha * FT
balanced_fl = tf.reduce_mean(balanced_fl)
#4# add other op to prevent overfit
# reference : https://spaces.ac.cn/archives/4493
nb_classes = len(classes_num)
fianal_loss = (1-e) * balanced_fl + e * K.categorical_crossentropy(K.ones_like(prediction_tensor)/nb_classes, prediction_tensor)
return fianal_loss
return focal_loss_fixed
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