AdaBN算法的tensorflow实现
时间: 2023-07-27 20:05:58 浏览: 121
以下是AdaBN算法的tensorflow实现示例:
```python
import tensorflow as tf
def adabn(inputs, is_training, epsilon=1e-5, decay=0.9, scope='adabn'):
with tf.variable_scope(scope):
# 获取输入张量的形状信息
_, var = tf.nn.moments(inputs, [1, 2], keep_dims=True)
# 标准化输入
inputs = tf.div(inputs, tf.sqrt(var + epsilon))
# 获取beta和gamma
beta = tf.get_variable('beta', [inputs.get_shape()[-1]], initializer=tf.constant_initializer(0.0), trainable=True)
gamma = tf.get_variable('gamma', [inputs.get_shape()[-1]], initializer=tf.constant_initializer(1.0), trainable=True)
# 获取全局平均值和方差
batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=decay)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
# 根据is_training条件使用不同的均值和方差
mean, var = tf.cond(is_training, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var)))
# 应用标准化和缩放
outputs = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, epsilon)
return outputs
```
这段代码实现了AdaBN算法的标准化和缩放过程,并使用指数加权平均计算全局平均值和方差。在训练过程中,使用当前batch的均值和方差,更新全局平均值和方差;在测试过程中,使用全局平均值和方差进行标准化和缩放。