Tensorflow 如何用tf.GradientTape模拟ReLU梯度

TensorFlow有一个名为梯度带的功能，类似于使用蒙特卡罗方法（？）获得梯度

我试着模拟ReLU的梯度，但这对X的负半部分不起作用

#colab or ipython reset
%reset -f

#libs
import tensorflow as tf;

#init
tf.enable_eager_execution();

#code
x = tf.convert_to_tensor([-3,-2,-1,0,1,2,3],dtype=tf.float32);

with tf.GradientTape() as t:
  t.watch(x);
  y = fx = x; #THIS IS JUST THE POSITIVE HALF OF X

dy_dx = t.gradient(y,x);
print(dy_dx); 

---

我想我必须在

y=fx=x

行中更改一些内容，比如如果x以下

grad

函数模拟ReLU函数的条件x，则添加一个

，但我不知道这是否是建议的方法：

#ipython
%reset -f

#libs
import tensorflow as tf;
import numpy      as np;

#init
tf.enable_eager_execution();

#code
X = tf.convert_to_tensor([-3,-2,-1,0,1,2,3], dtype=tf.float32);

with tf.GradientTape() as T:
  T.watch(X);
  Y = Fx = X;
#end with

Dy_Dx = T.gradient(Y,X);
#print(Dy_Dx);

#get gradient of function Fx with conditional X
def grad(Y,At):
  if (At<=0): return 0;

  for I in range(len(X)):
    if X[I].numpy()==At:
      return Dy_Dx[I].numpy();
#end def

print(grad(Y,-3));
print(grad(Y,-2));
print(grad(Y,-1));
print(grad(Y,-0));
print(grad(Y,1));
print(grad(Y,2));
print(grad(Y,3));

print("\nDone.");
#eof

#ipython
%重置-f
#自由基
导入tensorflow作为tf；
输入numpy作为np；
#初始化
tf.enable_eager_execution（）；
#代码
X=tf.convert_to_张量（[-3，-2，-1,0,1,2,3]，dtype=tf.float32）；
将tf.GradientTape（）作为T：
手表（X）；
Y=Fx=X；
#以
Dy_Dx=T.梯度（Y，X）；
#打印（Dy_Dx）；
#使用条件X获取函数Fx的梯度
def梯度（Y，At）：
如果（在）
tf.Tensor([0. 0. 0. 0. 1. 1. 1.], shape=(7,), dtype=float32)

#ipython
%reset -f

#libs
import tensorflow as tf;
import numpy      as np;

#init
tf.enable_eager_execution();

#code
X = tf.convert_to_tensor([-3,-2,-1,0,1,2,3], dtype=tf.float32);

with tf.GradientTape() as T:
  T.watch(X);
  Y = Fx = X;
#end with

Dy_Dx = T.gradient(Y,X);
#print(Dy_Dx);

#get gradient of function Fx with conditional X
def grad(Y,At):
  if (At<=0): return 0;

  for I in range(len(X)):
    if X[I].numpy()==At:
      return Dy_Dx[I].numpy();
#end def

print(grad(Y,-3));
print(grad(Y,-2));
print(grad(Y,-1));
print(grad(Y,-0));
print(grad(Y,1));
print(grad(Y,2));
print(grad(Y,3));

print("\nDone.");
#eof