reduce_max function in tensorflow
Screenshot
>>> boxes = tf.random_normal([ 5])
>>> with s.as_default():
... s.run(boxes)
... s.run(keras.backend.argmax(boxes,axis=0))
... s.run(tf.reduce_max(boxes,axis=0))
...
array([ 0.37312034, -0.97431135, 0.44504794, 0.35789603, 1.2461706 ],
dtype=float32)
3
0.856236
.
Why am I getting 0.8564. I expect the value to be 1.2461. since 1.2461 is big.right?
I am getting correct answer if i use tf.constant.
But I am not getting correct answer while using radom_normal
machine-learning argmax
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
add a comment |
Screenshot
>>> boxes = tf.random_normal([ 5])
>>> with s.as_default():
... s.run(boxes)
... s.run(keras.backend.argmax(boxes,axis=0))
... s.run(tf.reduce_max(boxes,axis=0))
...
array([ 0.37312034, -0.97431135, 0.44504794, 0.35789603, 1.2461706 ],
dtype=float32)
3
0.856236
.
Why am I getting 0.8564. I expect the value to be 1.2461. since 1.2461 is big.right?
I am getting correct answer if i use tf.constant.
But I am not getting correct answer while using radom_normal
machine-learning argmax
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
Please don't include text as pictures or images.
– Matias Valdenegro
Dec 30 '18 at 12:11
add a comment |
Screenshot
>>> boxes = tf.random_normal([ 5])
>>> with s.as_default():
... s.run(boxes)
... s.run(keras.backend.argmax(boxes,axis=0))
... s.run(tf.reduce_max(boxes,axis=0))
...
array([ 0.37312034, -0.97431135, 0.44504794, 0.35789603, 1.2461706 ],
dtype=float32)
3
0.856236
.
Why am I getting 0.8564. I expect the value to be 1.2461. since 1.2461 is big.right?
I am getting correct answer if i use tf.constant.
But I am not getting correct answer while using radom_normal
machine-learning argmax
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
Screenshot
>>> boxes = tf.random_normal([ 5])
>>> with s.as_default():
... s.run(boxes)
... s.run(keras.backend.argmax(boxes,axis=0))
... s.run(tf.reduce_max(boxes,axis=0))
...
array([ 0.37312034, -0.97431135, 0.44504794, 0.35789603, 1.2461706 ],
dtype=float32)
3
0.856236
.
Why am I getting 0.8564. I expect the value to be 1.2461. since 1.2461 is big.right?
I am getting correct answer if i use tf.constant.
But I am not getting correct answer while using radom_normal
machine-learning argmax
machine-learning argmax
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
edited Dec 30 '18 at 12:14
Bubesh p
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
asked Dec 30 '18 at 11:52
Bubesh pBubesh p
164
164
Please don't include text as pictures or images.
– Matias Valdenegro
Dec 30 '18 at 12:11
add a comment |
Please don't include text as pictures or images.
– Matias Valdenegro
Dec 30 '18 at 12:11
Please don't include text as pictures or images.
– Matias Valdenegro
Dec 30 '18 at 12:11
Please don't include text as pictures or images.
– Matias Valdenegro
Dec 30 '18 at 12:11
add a comment |
2 Answers
2
active
oldest
votes
Each time a new boxes is regenerated when you run s.run() with radom_normal. So your three results are different. If you want to get consistent results, you should only run s.run() once.
result = s.run([boxes,keras.backend.argmax(boxes,axis=0),tf.reduce_sum(boxes,axis=0)])
print(result[0])
print(result[1])
print(result[2])
#print
[ 0.69957364 1.3192859 -0.6662426 -0.5895929 0.22300807]
1
0.9860319
In addition, the code should be given in text format rather than picture format.
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
add a comment |
TensorFlow is different from numpy because TF only uses symbolic operations. That means when you instantiate the random_normal, you don't get numeric values, but a symbolic normal distribution, so each time you evaluate it, you get different numbers.
Each time you operate with this distribution, with any other operation, you are getting different numbers, and that explains the results you see.
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Each time a new boxes is regenerated when you run s.run() with radom_normal. So your three results are different. If you want to get consistent results, you should only run s.run() once.
result = s.run([boxes,keras.backend.argmax(boxes,axis=0),tf.reduce_sum(boxes,axis=0)])
print(result[0])
print(result[1])
print(result[2])
#print
[ 0.69957364 1.3192859 -0.6662426 -0.5895929 0.22300807]
1
0.9860319
In addition, the code should be given in text format rather than picture format.
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
add a comment |
Each time a new boxes is regenerated when you run s.run() with radom_normal. So your three results are different. If you want to get consistent results, you should only run s.run() once.
result = s.run([boxes,keras.backend.argmax(boxes,axis=0),tf.reduce_sum(boxes,axis=0)])
print(result[0])
print(result[1])
print(result[2])
#print
[ 0.69957364 1.3192859 -0.6662426 -0.5895929 0.22300807]
1
0.9860319
In addition, the code should be given in text format rather than picture format.
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
add a comment |
Each time a new boxes is regenerated when you run s.run() with radom_normal. So your three results are different. If you want to get consistent results, you should only run s.run() once.
result = s.run([boxes,keras.backend.argmax(boxes,axis=0),tf.reduce_sum(boxes,axis=0)])
print(result[0])
print(result[1])
print(result[2])
#print
[ 0.69957364 1.3192859 -0.6662426 -0.5895929 0.22300807]
1
0.9860319
In addition, the code should be given in text format rather than picture format.
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
Each time a new boxes is regenerated when you run s.run() with radom_normal. So your three results are different. If you want to get consistent results, you should only run s.run() once.
result = s.run([boxes,keras.backend.argmax(boxes,axis=0),tf.reduce_sum(boxes,axis=0)])
print(result[0])
print(result[1])
print(result[2])
#print
[ 0.69957364 1.3192859 -0.6662426 -0.5895929 0.22300807]
1
0.9860319
In addition, the code should be given in text format rather than picture format.
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
answered Dec 30 '18 at 12:14
giser_yuganggiser_yugang
1,5931419
1,5931419
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
add a comment |
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
Thank you. sorry for image.Actually new to stackoverflow., Now i changed that to text :-)
– Bubesh p
Dec 30 '18 at 12:16
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
@Bubeshp It doesn't matter. Welcome to stackoverflow.
– giser_yugang
Dec 30 '18 at 12:29
add a comment |
TensorFlow is different from numpy because TF only uses symbolic operations. That means when you instantiate the random_normal, you don't get numeric values, but a symbolic normal distribution, so each time you evaluate it, you get different numbers.
Each time you operate with this distribution, with any other operation, you are getting different numbers, and that explains the results you see.
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
add a comment |
TensorFlow is different from numpy because TF only uses symbolic operations. That means when you instantiate the random_normal, you don't get numeric values, but a symbolic normal distribution, so each time you evaluate it, you get different numbers.
Each time you operate with this distribution, with any other operation, you are getting different numbers, and that explains the results you see.
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
add a comment |
TensorFlow is different from numpy because TF only uses symbolic operations. That means when you instantiate the random_normal, you don't get numeric values, but a symbolic normal distribution, so each time you evaluate it, you get different numbers.
Each time you operate with this distribution, with any other operation, you are getting different numbers, and that explains the results you see.
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
TensorFlow is different from numpy because TF only uses symbolic operations. That means when you instantiate the random_normal, you don't get numeric values, but a symbolic normal distribution, so each time you evaluate it, you get different numbers.
Each time you operate with this distribution, with any other operation, you are getting different numbers, and that explains the results you see.
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
answered Dec 30 '18 at 12:15
Matias ValdenegroMatias Valdenegro
31.3k45377
31.3k45377
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
add a comment |
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
Thank you! Now I understood. Sorry for putting image...Actually new to stackoverflow
– Bubesh p
Dec 30 '18 at 12:18
add a comment |
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– Matias Valdenegro
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