Results not matching when running session multiple times
When i tried to print out1 and out2 i observed that the values coming in out2 doesnt exist in out1. But out2 is just finding the maximum from out1. Need help
import tensorflow as tf
from keras import backend as K
box_class_probs = tf.random_normal([2, 2, 1, 2], mean=1, stddev=4, seed = 1)
max_ind_class=K.max(box_class_probs,axis=-1)
with tf.Session() as sess:
out1=sess.run(box_class_probs)
print(out1)
out2=sess.run(max_ind_class)
print(out2)
output:
[[[[-2.24527287 6.93839502]]
[[ 1.26131749 -8.77081585]]]
[[[ 1.39699364 3.36489725]]
[[ 3.37129188 -7.49171829]]]]
---------------------------------------------
---------------------------------------------
---------------------------------------------
[[[ 1.96837616]
[ 3.06311464]]
[[ 9.33515644]
[ 6.58941841]]]
tensorflow keras
edited Nov 22 '18 at 7:51
kvish
56428
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
add a comment |
When i tried to print out1 and out2 i observed that the values coming in out2 doesnt exist in out1. But out2 is just finding the maximum from out1. Need help
import tensorflow as tf
from keras import backend as K
box_class_probs = tf.random_normal([2, 2, 1, 2], mean=1, stddev=4, seed = 1)
max_ind_class=K.max(box_class_probs,axis=-1)
with tf.Session() as sess:
out1=sess.run(box_class_probs)
print(out1)
out2=sess.run(max_ind_class)
print(out2)
output:
[[[[-2.24527287 6.93839502]]
[[ 1.26131749 -8.77081585]]]
[[[ 1.39699364 3.36489725]]
[[ 3.37129188 -7.49171829]]]]
---------------------------------------------
---------------------------------------------
---------------------------------------------
[[[ 1.96837616]
[ 3.06311464]]
[[ 9.33515644]
[ 6.58941841]]]
tensorflow keras
edited Nov 22 '18 at 7:51
kvish
56428
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
add a comment |
When i tried to print out1 and out2 i observed that the values coming in out2 doesnt exist in out1. But out2 is just finding the maximum from out1. Need help
import tensorflow as tf
from keras import backend as K
box_class_probs = tf.random_normal([2, 2, 1, 2], mean=1, stddev=4, seed = 1)
max_ind_class=K.max(box_class_probs,axis=-1)
with tf.Session() as sess:
out1=sess.run(box_class_probs)
print(out1)
out2=sess.run(max_ind_class)
print(out2)
output:
[[[[-2.24527287 6.93839502]]
[[ 1.26131749 -8.77081585]]]
[[[ 1.39699364 3.36489725]]
[[ 3.37129188 -7.49171829]]]]
---------------------------------------------
---------------------------------------------
---------------------------------------------
[[[ 1.96837616]
[ 3.06311464]]
[[ 9.33515644]
[ 6.58941841]]]
tensorflow keras
edited Nov 22 '18 at 7:51
kvish
56428
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
When i tried to print out1 and out2 i observed that the values coming in out2 doesnt exist in out1. But out2 is just finding the maximum from out1. Need help
import tensorflow as tf
from keras import backend as K
box_class_probs = tf.random_normal([2, 2, 1, 2], mean=1, stddev=4, seed = 1)
max_ind_class=K.max(box_class_probs,axis=-1)
with tf.Session() as sess:
out1=sess.run(box_class_probs)
print(out1)
out2=sess.run(max_ind_class)
print(out2)
output:
[[[[-2.24527287 6.93839502]]
[[ 1.26131749 -8.77081585]]]
[[[ 1.39699364 3.36489725]]
[[ 3.37129188 -7.49171829]]]]
---------------------------------------------
---------------------------------------------
---------------------------------------------
[[[ 1.96837616]
[ 3.06311464]]
[[ 9.33515644]
[ 6.58941841]]]
tensorflow keras
tensorflow keras
edited Nov 22 '18 at 7:51
kvish
56428
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
edited Nov 22 '18 at 7:51
kvish
56428
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
edited Nov 22 '18 at 7:51
kvish
56428
edited Nov 22 '18 at 7:51
kvish
56428
edited Nov 22 '18 at 7:51
kvish
56428
56428
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
asked Nov 22 '18 at 2:13
Satish EdupugantiSatish Edupuganti
82
82
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You need to run both of your results in one session run, because you are generating the box_class_probs randomly, and according to the random seed (default or internal), it is going to change every time you execute a session run. And also, keep in mind its always more consistent to get the current keras backend session using K.get_session() and then run your code when you are mixing keras and tensorflow.
sess = K.get_session()
out1, out2 = sess.run([box_class_probs, max_ind_class])
print(out1)
print(out2)
Result:
[[[[-2.2452729 6.938395 ]]
[[ 1.2613175 -8.770817 ]]]
[[[ 1.3969936 3.3648973]]
[[ 3.3712919 -7.4917183]]]]
[[[6.938395 ]
[1.2613175]]
[[3.3648973]
[3.3712919]]]
answered Nov 22 '18 at 3:57
kvishkvish
56428
add a comment |
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1 Answer
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1 Answer
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You need to run both of your results in one session run, because you are generating the box_class_probs randomly, and according to the random seed (default or internal), it is going to change every time you execute a session run. And also, keep in mind its always more consistent to get the current keras backend session using K.get_session() and then run your code when you are mixing keras and tensorflow.
sess = K.get_session()
out1, out2 = sess.run([box_class_probs, max_ind_class])
print(out1)
print(out2)
Result:
[[[[-2.2452729 6.938395 ]]
[[ 1.2613175 -8.770817 ]]]
[[[ 1.3969936 3.3648973]]
[[ 3.3712919 -7.4917183]]]]
[[[6.938395 ]
[1.2613175]]
[[3.3648973]
[3.3712919]]]
answered Nov 22 '18 at 3:57
kvishkvish
56428
add a comment |
You need to run both of your results in one session run, because you are generating the box_class_probs randomly, and according to the random seed (default or internal), it is going to change every time you execute a session run. And also, keep in mind its always more consistent to get the current keras backend session using K.get_session() and then run your code when you are mixing keras and tensorflow.
sess = K.get_session()
out1, out2 = sess.run([box_class_probs, max_ind_class])
print(out1)
print(out2)
Result:
[[[[-2.2452729 6.938395 ]]
[[ 1.2613175 -8.770817 ]]]
[[[ 1.3969936 3.3648973]]
[[ 3.3712919 -7.4917183]]]]
[[[6.938395 ]
[1.2613175]]
[[3.3648973]
[3.3712919]]]
answered Nov 22 '18 at 3:57
kvishkvish
56428
add a comment |
You need to run both of your results in one session run, because you are generating the box_class_probs randomly, and according to the random seed (default or internal), it is going to change every time you execute a session run. And also, keep in mind its always more consistent to get the current keras backend session using K.get_session() and then run your code when you are mixing keras and tensorflow.
sess = K.get_session()
out1, out2 = sess.run([box_class_probs, max_ind_class])
print(out1)
print(out2)
Result:
[[[[-2.2452729 6.938395 ]]
[[ 1.2613175 -8.770817 ]]]
[[[ 1.3969936 3.3648973]]
[[ 3.3712919 -7.4917183]]]]
[[[6.938395 ]
[1.2613175]]
[[3.3648973]
[3.3712919]]]
answered Nov 22 '18 at 3:57
kvishkvish
56428
You need to run both of your results in one session run, because you are generating the box_class_probs randomly, and according to the random seed (default or internal), it is going to change every time you execute a session run. And also, keep in mind its always more consistent to get the current keras backend session using K.get_session() and then run your code when you are mixing keras and tensorflow.
sess = K.get_session()
out1, out2 = sess.run([box_class_probs, max_ind_class])
print(out1)
print(out2)
Result:
[[[[-2.2452729 6.938395 ]]
[[ 1.2613175 -8.770817 ]]]
[[[ 1.3969936 3.3648973]]
[[ 3.3712919 -7.4917183]]]]
[[[6.938395 ]
[1.2613175]]
[[3.3648973]
[3.3712919]]]
answered Nov 22 '18 at 3:57
kvishkvish
56428
answered Nov 22 '18 at 3:57
kvishkvish
56428
answered Nov 22 '18 at 3:57
kvishkvish
56428
answered Nov 22 '18 at 3:57
kvishkvish
56428
56428
add a comment |
add a comment |
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