Python Tensorflow ReLU异常地规范化

我认为校正后的线性单元应执行以下功能：

relu(x) = max(x, 0)

然而，

tf.nn.relu的情况似乎并非如此：

import tensorflow as tf
import numpy as np
rand_large = np.random.randn(10, 3)*100
X = tf.placeholder(tf.float32, [10, 3])
sess = tf.Session()
sess.run(tf.nn.relu(X), feed_dict={X:rand_large})

随机矩阵如下所示：

>>> rand_large
array([[  21.94064161,  -82.16632876,   16.25152777],
   [  55.54897693,  -93.15235155,  118.99166126],
   [ -13.36452239,   39.36508285,   65.42844521],
   [-193.34041145,  -97.08632376,   99.22162259],
   [  87.02924619,    2.04134891,  -27.29975745],
   [-181.11406687,   43.55952393,   42.29312993],
   [ -29.81242188,   93.5764354 , -165.62711447],
   [  17.78380711, -171.30536766, -197.20709038],
   [ 105.94903623,   34.07995616,   -7.27568839],
   [-100.59533697, -189.88957685,   -7.52421816]])

>>> sess.run(tf.nn.relu(X), feed_dict={X:rand_large})array([[ 1. ,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5]], dtype=float32)

relu函数的输出如下：

>>> rand_large
array([[  21.94064161,  -82.16632876,   16.25152777],
   [  55.54897693,  -93.15235155,  118.99166126],
   [ -13.36452239,   39.36508285,   65.42844521],
   [-193.34041145,  -97.08632376,   99.22162259],
   [  87.02924619,    2.04134891,  -27.29975745],
   [-181.11406687,   43.55952393,   42.29312993],
   [ -29.81242188,   93.5764354 , -165.62711447],
   [  17.78380711, -171.30536766, -197.20709038],
   [ 105.94903623,   34.07995616,   -7.27568839],
   [-100.59533697, -189.88957685,   -7.52421816]])

>>> sess.run(tf.nn.relu(X), feed_dict={X:rand_large})array([[ 1. ,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5],
   [ 0.5,  0.5,  0.5]], dtype=float32)

所以，如果我看对了，
tf.nn.relu
做了某种规格化，对吗？如果是，为什么不在报告中提及？

好吧，我发现整个问题都与我的tensorflow安装有关，它似乎已损坏。在另一台机器上，我确实得到了预期的结果。
感谢您的帮助和宝贵意见。
tf.nn.relu
不规范数据。例如，如果我跑步

import tensorflow as tf
import numpy as np
X = tf.placeholder(tf.float32, [2, 3])
relu_X=tf.nn.relu(X)

sess = tf.Session()
mat = np.array([[-1,2,3],[2,-5,1]])
sess.run(relu_X, feed_dict={X:mat})

结果是

array([[ 0.,  2.,  3.],
       [ 2.,  0.,  1.]], dtype=float32)

你提到的输出是错误的，你应该得到负值作为零，保留正值。当我运行你的代码时，我没有得到你提到的输出。真奇怪。。。也许这是我安装的TF？