How to normalization params when use tensorflow to inference
I'm study tensorflow,for example,I have a array contains 1~10000 linear number to be a train data,before I train data I normalization this array like below
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
data_array = min_max_scaler.fit_transform(data_array)
now,I get a model,but how to use this model to inference? my app user input a param maybe 20000,how should I normalize 20000？
tensorflow
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
add a comment |
I'm study tensorflow,for example,I have a array contains 1~10000 linear number to be a train data,before I train data I normalization this array like below
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
data_array = min_max_scaler.fit_transform(data_array)
now,I get a model,but how to use this model to inference? my app user input a param maybe 20000,how should I normalize 20000？
tensorflow
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
add a comment |
I'm study tensorflow,for example,I have a array contains 1~10000 linear number to be a train data,before I train data I normalization this array like below
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
data_array = min_max_scaler.fit_transform(data_array)
now,I get a model,but how to use this model to inference? my app user input a param maybe 20000,how should I normalize 20000？
tensorflow
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
I'm study tensorflow,for example,I have a array contains 1~10000 linear number to be a train data,before I train data I normalization this array like below
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
data_array = min_max_scaler.fit_transform(data_array)
now,I get a model,but how to use this model to inference? my app user input a param maybe 20000,how should I normalize 20000？
tensorflow
tensorflow
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
asked Nov 28 '18 at 8:27
candrwowcandrwow
839
839
add a comment |
add a comment |
1 Answer
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oldest
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You can use the simple normalization formula :
- Calculate the min and max from your data. Min=1 and max=20000
- For every element in the array, which is suppose 'x' , subtract the min and divide the answer by max -min
- The formula : x( new ) = x( old ) - min / max - min
Refer here.
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can use the simple normalization formula :
- Calculate the min and max from your data. Min=1 and max=20000
- For every element in the array, which is suppose 'x' , subtract the min and divide the answer by max -min
- The formula : x( new ) = x( old ) - min / max - min
Refer here.
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
add a comment |
You can use the simple normalization formula :
- Calculate the min and max from your data. Min=1 and max=20000
- For every element in the array, which is suppose 'x' , subtract the min and divide the answer by max -min
- The formula : x( new ) = x( old ) - min / max - min
Refer here.
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
add a comment |
You can use the simple normalization formula :
- Calculate the min and max from your data. Min=1 and max=20000
- For every element in the array, which is suppose 'x' , subtract the min and divide the answer by max -min
- The formula : x( new ) = x( old ) - min / max - min
Refer here.
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
You can use the simple normalization formula :
- Calculate the min and max from your data. Min=1 and max=20000
- For every element in the array, which is suppose 'x' , subtract the min and divide the answer by max -min
- The formula : x( new ) = x( old ) - min / max - min
Refer here.
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
answered Nov 28 '18 at 10:43
Shubham PanchalShubham Panchal
534310
534310
add a comment |
add a comment |
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