这是在 tensorflow 中实现的 Faster R-CNN。
proposal_layer是implement通过 python
我很好奇梯度是否可以通过 tf.py_func
权重和偏差不断变化
所以我认为梯度回传成功
然后我做一个小测试
import tensorflow as tf
import numpy as np
def addone(x):
# print type(x)
return x + 1
def pyfunc_test():
# create data
x_data = tf.placeholder(dtype=tf.float32, shape=[None])
y_data = tf.placeholder(dtype=tf.float32, shape=[None])
w = tf.Variable(tf.constant([0.5]))
b = tf.Variable(tf.zeros([1]))
y1 = tf.mul(w, x_data, name='y1')
y2 = tf.py_func(addone, [y1], tf.float32)
y = tf.add(y2, b)
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in xrange(201):
ran = np.random.rand(115).astype(np.float32)
ans = ran * 1.5 + 3
dic = {x_data: ran, y_data: ans}
tt, yy, yy1= sess.run([train, y1, y2], feed_dict=dic)
if step % 20 == 0:
print 'step {}'.format(step)
print '{}, {}'.format(w.eval(), b.eval())
test = sess.run(y, feed_dict={x_data:[1]})
print 'test = {}'.format(test)
if __name__ == '__main__':
pyfunc_test()
变量 b
不断变化,但是w
初始化后保留值,永不改变
sess.run(tf.gradients(loss, b), feed_dict=dic)
获取值(value)
sess.run(tf.gradients(loss, w), feed_dict=dic)
得到 {TypeError}Fetch argument None has invalid type <type 'NoneType'>
我知道一些问题建议使用tf.RegisterGradient
和 gradient_override_map
但我无法在更快的 rcnn 存储库中找到这些(帖子顶部的链接)
我是做错了什么还是遗漏了什么w
卡住
最佳答案
py_func
的梯度为None
(只需检查 ops.get_gradient_function(y2.op)
)。有这个 gist @harpone 展示了如何为 py_func 使用梯度覆盖图。
这是修改后的示例以使用该配方
import numpy as np
import tensorflow as tf
def addone(x):
# print(type(x)
return x + 1
def addone_grad(op, grad):
x = op.inputs[0]
return x
from tensorflow.python.framework import ops
import numpy as np
# Define custom py_func which takes also a grad op as argument:
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad) # see _MySquareGrad for grad example
g = tf.get_default_graph()
with g.gradient_override_map({"PyFunc": rnd_name}):
return tf.py_func(func, inp, Tout, stateful=stateful, name=name)
def pyfunc_test():
# create data
x_data = tf.placeholder(dtype=tf.float32, shape=[None])
y_data = tf.placeholder(dtype=tf.float32, shape=[None])
w = tf.Variable(tf.constant([0.5]))
b = tf.Variable(tf.zeros([1]))
y1 = tf.mul(w, x_data, name='y1')
y2 = py_func(addone, [y1], [tf.float32], grad=addone_grad)[0]
y = tf.add(y2, b)
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
print("Pyfunc grad", ops.get_gradient_function(y2.op))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(10):
# ran = np.random.rand(115).astype(np.float32)
ran = np.ones((115)).astype(np.float32)
ans = ran * 1.5 + 3
dic = {x_data: ran, y_data: ans}
tt, yy, yy1= sess.run([train, y1, y2], feed_dict=dic)
if step % 1 == 0:
print('step {}'.format(step))
print('{}, {}'.format(w.eval(), b.eval()))
test = sess.run(y, feed_dict={x_data:[1]})
print('test = {}'.format(test))
if __name__ == '__main__':
pyfunc_test()
关于python - 梯度如何通过 tf.py_func,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/41535347/