python - Tensorflow ReLU normalizes strangely -


in opinion rectified linear unit supposed execute following function:

relu(x) = max(x, 0)

however, seems not case tf.nn.relu:

import tensorflow tf import numpy 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})

the random matrix looks this:

>>> 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]])

and output relu function this:

>>> 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)

so, if see correctly, tf.nn.relu sort of normalization, right? if yes, why isn't mentioned in docs?

okay, found out whole issue related tensorflow installtion seemed corrupt. on machine, did expected results. thank , helpful comments.

tf.nn.relu not normalize data. example, if run

import tensorflow tf import numpy 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})

the result is

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

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