在训练rnn模型时,很可能会遇到一段时间后,无论怎么训练,损失函数都不变化的情况.仿佛时间静止了一样.这时候很可能是大多数参数都不变了.也就是遇到了梯度消失的问题.
原理
令
[egin{equation*}
sigma(x)=frac{1}{1+e^{-x}}
end{equation*}
]
有以下方程组
[egin{equation*}
egin{cases}
z_0=2w_0+b_0 \
z_1=sigma(z_0)w_1+b_1 \
z_2=sigma(z_1)w_2+b_2 \
z_3=sigma(z_2)w_3+b_3 \
z_4=sigma(z_3)w_4+b_4 \
z_5=sigma(z_4)w_5+b_5 \
z=sigma(z_5) \
end{cases}
end{equation*}
]
设某个模型的损失函数为(z),现在求(z)对(w_0)的偏导.
[frac{partial z}{partial w_0}=2sigma'(z_5)w_5sigma'(z_4)w_4sigma'(z_3)w_3sigma'(z_2)w_2sigma'(z_1)w_1sigma'(z_0)
]
(sigma'(z_i)) 的范围在(0,1)之间.6个小于1的数相乘,会导致结果非常接近0.这就造成(w_0)的梯度很小,在训练的过程中(w_0)变化特别慢,这就是梯度消失的原因.反过来看,当(w_i)很大时,5个很大的数相乘,会造成(w_0)的梯度特别大,更新一次就可能越过极值.这就是梯度爆炸的原因.
用tensorflow求偏导
为了重现梯度消失,要把梯度的值打印出来.所以先介绍怎么用tensorflow求偏导.假设某个模型的损失函数如下所示:
[egin{equation*}
z=x^2+y^2
end{equation*}
]
现在从(1,1)开始用反向传播算法,让(z)逐渐减少.因为这个函数简单,可以先自己编码求偏导,代码如下:
# coding:utf-8
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
learning_rate = 0.1
def z(x, y):
return x * x + y * y
x = 1
y = 1
for i in range(10):
dx = 2 * x
dy = 2 * y
x -= dx * learning_rate
y -= dy * learning_rate
print("step={},dx={:.4f},x={:.4f},z={:.4f}".format(i, dx, x, z(x, y)))
print("step={},dy={:.4f},y={:.4f},z={:.4f}".format(i, dy, y, z(x, y)))
再用tensorflow实现:
# coding:utf-8
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
import tensorflow as tf
x = tf.Variable(1, dtype=tf.float32, name="x")
y = tf.Variable(1, dtype=tf.float32, name="y")
z = x * x + y * y
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
gra_and_var = optimizer.compute_gradients(z, [x, y])
train_step = optimizer.apply_gradients(gra_and_var)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(10):
sess.run(train_step)
r_z, r_gv = sess.run([z, gra_and_var])
print("step={},dx={:.4f},x={:.4f},z={:.4f}".format(i, r_gv[0][0], r_gv[0][1], r_z))
print("step={},dy={:.4f},y={:.4f},z={:.4f}".format(i, r_gv[1][0], r_gv[1][1], r_z))
两段代码的结果是一样的.optimizer.compute_gradients(z, [x, y])是对 (z) 分别求(x),(y)的偏导,返回一个list,list的每个元素是1个2元组.2元组第1个元素是变量的梯度,第2个元素是变量的值.可以用这种方法看到训练过程中梯度变化的情况.
复现梯度消失
定义一个不太简单也不太复杂的损失函数:
[egin{equation*}
egin{cases}
z_0=2w_0+b_0 \
z_1=sigma(z_0)w_1+b_1 \
z_2=sigma(z_1)w_2+b_2 \
z=sigma(z_2) \
end{cases}
end{equation*}
]
用反向传播算法调整各变量让(z)不断减少.代码如下:
# coding:utf-8
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
import tensorflow as tf
w0 = tf.Variable(0.5, dtype=tf.float32, name="w0")
b0 = tf.Variable(0.5, dtype=tf.float32, name="b0")
w1 = tf.Variable(0.5, dtype=tf.float32, name="w1")
b1 = tf.Variable(0.5, dtype=tf.float32, name="b1")
w2 = tf.Variable(0.5, dtype=tf.float32, name="w2")
b2 = tf.Variable(0.5, dtype=tf.float32, name="b2")
z = tf.sigmoid(tf.sigmoid(tf.sigmoid(2 * w0 + b0) * w1 + b1) * w2 + b2)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
gra_and_var = optimizer.compute_gradients(z, [w0, b0, w1, b1, w2, b2])
train_step = optimizer.apply_gradients(gra_and_var)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
sess.run(train_step)
r_z, r_gv = sess.run([z, gra_and_var])
print("step={},dw0={:.4f},w0={:.4f},z={:.4f}".format(i, r_gv[0][0], r_gv[0][1], r_z))
print("step={},db0={:.4f},b0={:.4f},z={:.4f}".format(i, r_gv[1][0], r_gv[1][1], r_z))
print("step={},dw1={:.4f},w1={:.4f},z={:.4f}".format(i, r_gv[2][0], r_gv[2][1], r_z))
print("step={},db1={:.4f},b1={:.4f},z={:.4f}".format(i, r_gv[3][0], r_gv[3][1], r_z))
print("step={},dw2={:.4f},w2={:.4f},z={:.4f}".format(i, r_gv[4][0], r_gv[4][1], r_z))
print("step={},db3={:.4f},b2={:.4f},z={:.4f}".format(i, r_gv[5][0], r_gv[5][1], r_z))
把最后几次的输出列出来:
step=998,dw0=-0.0004,w0=0.5947,z=0.0061
step=998,db0=-0.0002,b0=0.5474,z=0.0061
step=998,dw1=-0.0014,w1=0.9078,z=0.0061
step=998,db1=-0.0017,b1=0.9895,z=0.0061
step=998,dw2=0.0052,w2=-2.2514,z=0.0061
step=998,db2=0.0061,b2=-3.1738,z=0.0061
step=999,dw0=-0.0004,w0=0.5948,z=0.0061
step=999,db0=-0.0002,b0=0.5474,z=0.0061
step=999,dw1=-0.0014,w1=0.9080,z=0.0061
step=999,db1=-0.0017,b1=0.9897,z=0.0061
step=999,dw2=0.0052,w2=-2.2519,z=0.0061
step=999,db2=0.0060,b2=-3.1744,z=0.0061
当step=999时,dw2为0.0052,dw1为-0.0014,dw0为-0.0004.dw0已经非常接近0.也就是在训练过程中(w_0)变化非常慢.这还是3层网络,假定是100层.那(w_0)就基本等于0个.无论怎么训练(w_0)都不会怎么变化.想复现梯度爆炸,可以把各个w调大一点,比如100以上.同样的代码,看看效果.