你要的答案或许都在这里：小鹏的博客目录
MachineLP的Github（欢迎follow）：https://github.com/MachineLP
paper：
CosFace: Large Margin Cosine Loss（MLCL） for Deep Face Recognition
下载地址： https://arxiv.org/pdf/1801.09414.pdf
论文中的cos loss：
cos loss 的 TF 实现：
代码语言：javascript复制# coding=utf-8
import tensorflow as tf
import numpy as np
def py_func(func, inp, Tout, stateful = True, name=None, grad_func=None):
rand_name = 'PyFuncGrad' str(np.random.randint(0,1E 8))
tf.RegisterGradient(rand_name)(grad_func)
g = tf.get_default_graph()
with g.gradient_override_map({'PyFunc':rand_name}):
return tf.py_func(func,inp,Tout,stateful=stateful, name=name)
def coco_forward(xw, y, m, name=None):
#pdb.set_trace()
xw_copy = xw.copy()
num = len(y)
orig_ind = range(num)
xw_copy[orig_ind,y] -= m
return xw_copy
def coco_help(grad,y):
grad_copy = grad.copy()
return grad_copy
def coco_backward(op, grad):
y = op.inputs[1]
m = op.inputs[2]
grad_copy = tf.py_func(coco_help,[grad,y],tf.float32)
return grad_copy,y,m
def coco_func(xw,y,m, name=None):
with tf.op_scope([xw,y,m],name,"Coco_func") as name:
coco_out = py_func(coco_forward,[xw,y,m],tf.float32,name=name,grad_func=coco_backward)
return coco_out
def cos_loss(x, y, num_cls, reuse=False, alpha=0.25, scale=64,name = 'cos_loss'):
'''''
x: B x D - features
y: B x 1 - labels
num_cls: 1 - total class number
alpah: 1 - margin
scale: 1 - scaling paramter
'''
# define the classifier weights
xs = x.get_shape()
y = tf.reshape(tf.cast(y, dtype = tf.int32),[-1])
with tf.variable_scope('centers_var',reuse=reuse) as center_scope:
w = tf.get_variable("centers", [xs[1], num_cls], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),trainable=True)
#normalize the feature and weight
#(N,D)
x_feat_norm = tf.nn.l2_normalize(x,1,1e-10)
#(D,C)
w_feat_norm = tf.nn.l2_normalize(w,0,1e-10)
# get the scores after normalization
#(N,C)
xw_norm = tf.matmul(x_feat_norm, w_feat_norm)
#value = tf.identity(xw)
#substract the marigin and scale it
value = coco_func(xw_norm,y,alpha) * scale
# compute the loss as softmax loss
cos_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=value))
return cos_loss
