Update README.md

README.md

@@ -14,5 +14,81 @@ Under pictures are embeddings about mnist dataset after training.
 ### CurricularFace Loss
 ![download](https://user-images.githubusercontent.com/48349693/182266736-44cb7dc4-b273-45cc-9ff6-e3fd7672b0a5.png)
 
+---
+
+# Code Explanation
+
+```python
+import tensorflow as tf
+import numpy as np
+
+(train_x, train_y), (test_x, test_y) = tf.keras.datasets.mnist.load_data()
+print(train_x.shape, train_y.shape)
+print(train_x.dtype, train_y.dtype)
+
+train_x = train_x.astype(np.float32) / 255.0
+test_x = test_x.astype(np.float32) / 255.0
+
+train_y = tf.keras.utils.to_categorical(train_y, num_classes=10)
+test_y = tf.keras.utils.to_categorical(test_y, num_classes=10)
+print(train_y.shape, train_y.dtype)
+print(test_y.shape, test_y.dtype)
+```
+Load mnist dataset.
+
+```python
+class CurricularFaceLoss(tf.keras.losses.Loss):
+    def __init__(self, scale=30, margin=0.5, alpha=0.99, name="CurricularFaceLoss", **kwargs):
+        super().__init__(name=name, **kwargs)
+        self.scale = scale
+        self.margin = margin
+        self.alpha = alpha
+        self.t = tf.Variable(0.)
+        self.eps = 1e-7
+
+    def positive_forward(self, y_logit):
+        cosine_sim = y_logit 
+        theta_margin = tf.math.acos(cosine_sim) + self.margin
+        y_logit_pos = tf.math.cos(theta_margin)
+        return y_logit_pos
+
+    def negative_forward(self, y_logit_pos_masked, y_logit):
+        hard_sample_mask = y_logit_pos_masked < y_logit # (N, n_classes)
+        y_logit_neg = tf.where(hard_sample_mask, tf.square(y_logit)+self.t*y_logit, y_logit)
+        return y_logit_neg
+
+    def forward(self, y_true, y_logit):
+        y_logit = tf.clip_by_value(y_logit, -1.0+self.eps, 1.0-self.eps)
+        y_logit_masked = tf.expand_dims(tf.reduce_sum(y_true*y_logit, axis=1), axis=1) # (N, 1)
+        y_logit_pos_masked = self.positive_forward(y_logit_masked) # (N, 1)
+        y_logit_neg = self.negative_forward(y_logit_pos_masked, y_logit) # (N, n_classes)
+        # update t
+        r = tf.reduce_mean(y_logit_pos_masked)
+        self.t.assign(self.alpha*r + (1-self.alpha)*self.t)
+
+        y_true = tf.cast(y_true, dtype=tf.bool)
+        return tf.where(y_true, y_logit_pos_masked, y_logit_neg)
+
+    def __call__(self, y_true, y_logit): # shape(N, n_classes)
+        y_logit_fixed = self.forward(y_true, y_logit)
+        loss = tf.nn.softmax_cross_entropy_with_logits(y_true, y_logit_fixed*self.scale)
+        loss = tf.reduce_mean(loss)
+        return loss
+```
+
+
+
+
+
+
+---
+
+### Reference
+
+https://emkademy.medium.com/angular-margin-losses-for-representative-embeddings-training-arcface-2018-vs-mv-arc-softmax-96b54bcd030b
+
+https://linuxtut.com/en/a9dadc68a5cd0a2747c0/
+
+
+
 
-#