AdamW Optimizer
介绍
AdamW优化器支持Embedding Variable,相对于Adam优化器增加了了权重衰减的功能。
这是Loshch ilov & Hutter (https://arxiv.org/abs/1711.05101)的 "Decoupled Weight Decay Regularization"中描述的AdamW优化器的一个实现。
用户接口
训练时只需要定义tf.train.AdamWOptimizer
即可,和其他TF原生Optimizer使用方式相同。具体定义如下:
class AdamWOptimizer(DecoupledWeightDecayExtension, adam.AdamOptimizer):
def __init__(self,
weight_decay,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-8,
use_locking=False,
name="AdamW"):
# 调用方法:
optimizer = tf.train.AdamWOptimizer(
weight_decay=weight_decay_new
learning_rate=learning_rate_new,
beta1=0.9,
beta2=0.999,
epsilon=1e-8)
使用示例
import tensorflow as tf
var = tf.get_variable("var_0", shape=[10,16],
initializer=tf.ones_initializer(tf.float32))
emb = tf.nn.embedding_lookup(var, tf.cast([0,1,2,5,6,7], tf.int64))
fun = tf.multiply(emb, 2.0, name='multiply')
loss = tf.reduce_sum(fun, name='reduce_sum')
gs= tf.train.get_or_create_global_step()
opt = tf.train.AdamWOptimizer(weight_decay=0.01, learning_rate=0.1)
g_v = opt.compute_gradients(loss)
train_op = opt.apply_gradients(g_v)
init = tf.global_variables_initializer()
sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
with tf.Session(config=sess_config) as sess:
sess.run([init])
print(sess.run([emb, train_op, loss]))
print(sess.run([emb, train_op, loss]))
print(sess.run([emb, train_op, loss]))