revise 5-5

5-5,损失函数losses.md

@@ -100,7 +100,7 @@ _________________________________________________________________
 
 它有两个可调参数，alpha参数和gamma参数。其中alpha参数主要用于衰减负样本的权重，gamma参数主要用于衰减容易训练样本的权重。
 
-从而让模型更加聚焦在正样本和困难样本上。
+从而让模型更加聚焦在正样本和困难样本上。这就是为什么这个损失函数叫做Focal Loss。
 
 详见《5分钟理解Focal Loss与GHM——解决样本不平衡利器》
 

english/Chapter5-5.md

@@ -96,18 +96,23 @@ It is also possible to customize loss function through inheriting from the base
 
 Here is an example of customized implementation to the Focal Loss, which is an improvement of `binary_crossentropy` loss function.
 
-Focal Loss results better comparing to the binary cross entropy, given the condition of unbalanced category and difficulties in training.
+Focal Loss results better comparing to the binary cross entropy, given the condition of unbalanced category and many easy samples in training data.
 
-You may refer to the following article for details of this topic: [How to comment the "Focal Loss for Dense Object Detection" by Kaiming ?](https://www.zhihu.com/question/63581984)
+It has two adjustable parameters，alpha and gamma. The aim of alpha is to decay the weight of negative samples，and gamma to decay the weight of the easy samples. 
+
+So the model will then focal its weight on the positive samples and hard samples. This is why the loss is called focal loss. 
+
+You may refer to the following article for details of this topic: [Understand Focal Loss and GHM in 5 minutes](https://www.zhihu.com/question/63581984)
 
 ```python
-def focal_loss(gamma=2., alpha=0.25):
+def focal_loss(gamma=2., alpha=0.75):
 
     def focal_loss_fixed(y_true, y_pred):
-        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
-        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
-        loss = -tf.reduce_sum(alpha * tf.pow(1. - pt_1, gamma) * tf.log(1e-07+pt_1)) \
-           -tf.reduce_sum((1-alpha) * tf.pow( pt_0, gamma) * tf.log(1. - pt_0 + 1e-07))
+        bce = tf.losses.binary_crossentropy(y_true, y_pred)
+        p_t = (y_true * y_pred) + ((1 - y_true) * (1 - y_pred))
+        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)
+        modulating_factor = tf.pow(1.0 - p_t, gamma)
+        loss = tf.reduce_sum(alpha_factor * modulating_factor * bce,axis = -1 )
         return loss
     return focal_loss_fixed
 
@@ -116,16 +121,16 @@ def focal_loss(gamma=2., alpha=0.25):
 ```python
 class FocalLoss(losses.Loss):
 
-    def __init__(self,gamma=2.0,alpha=0.25):
+    def __init__(self,gamma=2.0,alpha=0.75,name = "focal_loss"):
         self.gamma = gamma
         self.alpha = alpha
 
     def call(self,y_true,y_pred):
-
-        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
-        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
-        loss = -tf.reduce_sum(self.alpha * tf.pow(1. - pt_1, self.gamma) * tf.log(1e-07+pt_1)) \
-           -tf.reduce_sum((1-self.alpha) * tf.pow( pt_0, self.gamma) * tf.log(1. - pt_0 + 1e-07))
+        bce = tf.losses.binary_crossentropy(y_true, y_pred)
+        p_t = (y_true * y_pred) + ((1 - y_true) * (1 - y_pred))
+        alpha_factor = y_true * self.alpha + (1 - y_true) * (1 - self.alpha)
+        modulating_factor = tf.pow(1.0 - p_t, self.gamma)
+        loss = tf.reduce_sum(alpha_factor * modulating_factor * bce,axis = -1 )
         return loss
 ```