Difference in the array type using numpy and cupy
I am using chainer library for my model and facing the below issue:
Say I have a file of test data having 3 features and a label (last column) with them. It is imported in the form of a list.
e.g.
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
Then I take the labels by converting the data into a numpy array and taking the labels column,
which I later convert into a list for comparison with the predicted labels say y_pred = [1,1,1,0]. i.e.
import numpy as np
y_true_np = list(np.array(test_set)[:,3])
print(y_true_np)
[1, 1, 0, 0]
My concern is when I run my model in GPU, it uses Cuda.cupy instead of numpy as I am using chainer library, and when I fetch the true labels I receive them as:
There by in cupy:
import cupy as cp
y_true_cp = list(cp.array(test_set)[:,3]) Or
y_true_cp = list(cuda.cp.array(test_set)[:,3])
Both returns a list of array:
y_true_cp: [array(1), array(1), array(0), array(0)]
As a workaround, I am using numpy in that specific place. Am I doing something wrong while using cupy, due to which I am not getting the values correctly?
python-3.x chainer cupy
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
add a comment |
I am using chainer library for my model and facing the below issue:
Say I have a file of test data having 3 features and a label (last column) with them. It is imported in the form of a list.
e.g.
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
Then I take the labels by converting the data into a numpy array and taking the labels column,
which I later convert into a list for comparison with the predicted labels say y_pred = [1,1,1,0]. i.e.
import numpy as np
y_true_np = list(np.array(test_set)[:,3])
print(y_true_np)
[1, 1, 0, 0]
My concern is when I run my model in GPU, it uses Cuda.cupy instead of numpy as I am using chainer library, and when I fetch the true labels I receive them as:
There by in cupy:
import cupy as cp
y_true_cp = list(cp.array(test_set)[:,3]) Or
y_true_cp = list(cuda.cp.array(test_set)[:,3])
Both returns a list of array:
y_true_cp: [array(1), array(1), array(0), array(0)]
As a workaround, I am using numpy in that specific place. Am I doing something wrong while using cupy, due to which I am not getting the values correctly?
python-3.x chainer cupy
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
add a comment |
I am using chainer library for my model and facing the below issue:
Say I have a file of test data having 3 features and a label (last column) with them. It is imported in the form of a list.
e.g.
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
Then I take the labels by converting the data into a numpy array and taking the labels column,
which I later convert into a list for comparison with the predicted labels say y_pred = [1,1,1,0]. i.e.
import numpy as np
y_true_np = list(np.array(test_set)[:,3])
print(y_true_np)
[1, 1, 0, 0]
My concern is when I run my model in GPU, it uses Cuda.cupy instead of numpy as I am using chainer library, and when I fetch the true labels I receive them as:
There by in cupy:
import cupy as cp
y_true_cp = list(cp.array(test_set)[:,3]) Or
y_true_cp = list(cuda.cp.array(test_set)[:,3])
Both returns a list of array:
y_true_cp: [array(1), array(1), array(0), array(0)]
As a workaround, I am using numpy in that specific place. Am I doing something wrong while using cupy, due to which I am not getting the values correctly?
python-3.x chainer cupy
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
I am using chainer library for my model and facing the below issue:
Say I have a file of test data having 3 features and a label (last column) with them. It is imported in the form of a list.
e.g.
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
Then I take the labels by converting the data into a numpy array and taking the labels column,
which I later convert into a list for comparison with the predicted labels say y_pred = [1,1,1,0]. i.e.
import numpy as np
y_true_np = list(np.array(test_set)[:,3])
print(y_true_np)
[1, 1, 0, 0]
My concern is when I run my model in GPU, it uses Cuda.cupy instead of numpy as I am using chainer library, and when I fetch the true labels I receive them as:
There by in cupy:
import cupy as cp
y_true_cp = list(cp.array(test_set)[:,3]) Or
y_true_cp = list(cuda.cp.array(test_set)[:,3])
Both returns a list of array:
y_true_cp: [array(1), array(1), array(0), array(0)]
As a workaround, I am using numpy in that specific place. Am I doing something wrong while using cupy, due to which I am not getting the values correctly?
python-3.x chainer cupy
python-3.x chainer cupy
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
edited Nov 21 '18 at 16:41
talonmies
59.7k17129199
59.7k17129199
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
asked Nov 21 '18 at 15:24
Gurpreet.SGurpreet.S
626
626
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
While NumPy converts 0-dimensional arrays to scalars, CuPy does not.
https://docs-cupy.chainer.org/en/stable/reference/difference.html#zero-dimensional-array
In the result [array(1), array(1), array(0), array(0)], each data of arrays is on GPU. I'd use cupy.asnumpy if an efficient CPU array is needed.
y_true_cp = list(cp.asnumpy(cp.array(test_set)[:,3]))
answered Nov 22 '18 at 3:21
tostos
462
add a comment |
There is no necessity to go through numpy.
Input
import cupy as cp
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
test_set = cp.array(test_set)
x_true = test_set[:, :3]
y_true = test_set[:, 3]
print("x_true:n".format(x_true))
print("y_true:n".format(y_true))
Output
x_true:
[[1 0 9]
[7 0 8]
[7 0 2]
[8 0 1]]
y_true:
[1 1 0 0]
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
add a comment |
As you wrote, it seems the behavior when you wrap by list is different
import numpy as np
import cupy as cp
print(list(np.arange(3)) # --> [0, 1, 2]
print(list(cp.arange(3)) # --> [array(0), array(1), array(2)]
However in your case, I think you can just use numpy array or cupy array without converting list.
y_true = test_set[:, 3] # it should work for both numpy & cupy
y_true_np = cuda.to_cpu(y_true) # If you want to convert the array to numpy
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
While NumPy converts 0-dimensional arrays to scalars, CuPy does not.
https://docs-cupy.chainer.org/en/stable/reference/difference.html#zero-dimensional-array
In the result [array(1), array(1), array(0), array(0)], each data of arrays is on GPU. I'd use cupy.asnumpy if an efficient CPU array is needed.
y_true_cp = list(cp.asnumpy(cp.array(test_set)[:,3]))
answered Nov 22 '18 at 3:21
tostos
462
add a comment |
While NumPy converts 0-dimensional arrays to scalars, CuPy does not.
https://docs-cupy.chainer.org/en/stable/reference/difference.html#zero-dimensional-array
In the result [array(1), array(1), array(0), array(0)], each data of arrays is on GPU. I'd use cupy.asnumpy if an efficient CPU array is needed.
y_true_cp = list(cp.asnumpy(cp.array(test_set)[:,3]))
answered Nov 22 '18 at 3:21
tostos
462
add a comment |
While NumPy converts 0-dimensional arrays to scalars, CuPy does not.
https://docs-cupy.chainer.org/en/stable/reference/difference.html#zero-dimensional-array
In the result [array(1), array(1), array(0), array(0)], each data of arrays is on GPU. I'd use cupy.asnumpy if an efficient CPU array is needed.
y_true_cp = list(cp.asnumpy(cp.array(test_set)[:,3]))
answered Nov 22 '18 at 3:21
tostos
462
While NumPy converts 0-dimensional arrays to scalars, CuPy does not.
https://docs-cupy.chainer.org/en/stable/reference/difference.html#zero-dimensional-array
In the result [array(1), array(1), array(0), array(0)], each data of arrays is on GPU. I'd use cupy.asnumpy if an efficient CPU array is needed.
y_true_cp = list(cp.asnumpy(cp.array(test_set)[:,3]))
answered Nov 22 '18 at 3:21
tostos
462
answered Nov 22 '18 at 3:21
tostos
462
answered Nov 22 '18 at 3:21
tostos
462
answered Nov 22 '18 at 3:21
tostos
462
462
add a comment |
add a comment |
There is no necessity to go through numpy.
Input
import cupy as cp
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
test_set = cp.array(test_set)
x_true = test_set[:, :3]
y_true = test_set[:, 3]
print("x_true:n".format(x_true))
print("y_true:n".format(y_true))
Output
x_true:
[[1 0 9]
[7 0 8]
[7 0 2]
[8 0 1]]
y_true:
[1 1 0 0]
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
add a comment |
There is no necessity to go through numpy.
Input
import cupy as cp
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
test_set = cp.array(test_set)
x_true = test_set[:, :3]
y_true = test_set[:, 3]
print("x_true:n".format(x_true))
print("y_true:n".format(y_true))
Output
x_true:
[[1 0 9]
[7 0 8]
[7 0 2]
[8 0 1]]
y_true:
[1 1 0 0]
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
add a comment |
There is no necessity to go through numpy.
Input
import cupy as cp
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
test_set = cp.array(test_set)
x_true = test_set[:, :3]
y_true = test_set[:, 3]
print("x_true:n".format(x_true))
print("y_true:n".format(y_true))
Output
x_true:
[[1 0 9]
[7 0 8]
[7 0 2]
[8 0 1]]
y_true:
[1 1 0 0]
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
There is no necessity to go through numpy.
Input
import cupy as cp
test_set = [[1,0,9,1],[7,0,8,1],[7,0,2,0],[8,0,1,0]]
test_set = cp.array(test_set)
x_true = test_set[:, :3]
y_true = test_set[:, 3]
print("x_true:n".format(x_true))
print("y_true:n".format(y_true))
Output
x_true:
[[1 0 9]
[7 0 8]
[7 0 2]
[8 0 1]]
y_true:
[1 1 0 0]
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
edited Nov 23 '18 at 7:44
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
answered Nov 22 '18 at 7:12
Yuki HashimotoYuki Hashimoto
6159
6159
add a comment |
add a comment |
As you wrote, it seems the behavior when you wrap by list is different
import numpy as np
import cupy as cp
print(list(np.arange(3)) # --> [0, 1, 2]
print(list(cp.arange(3)) # --> [array(0), array(1), array(2)]
However in your case, I think you can just use numpy array or cupy array without converting list.
y_true = test_set[:, 3] # it should work for both numpy & cupy
y_true_np = cuda.to_cpu(y_true) # If you want to convert the array to numpy
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
add a comment |
As you wrote, it seems the behavior when you wrap by list is different
import numpy as np
import cupy as cp
print(list(np.arange(3)) # --> [0, 1, 2]
print(list(cp.arange(3)) # --> [array(0), array(1), array(2)]
However in your case, I think you can just use numpy array or cupy array without converting list.
y_true = test_set[:, 3] # it should work for both numpy & cupy
y_true_np = cuda.to_cpu(y_true) # If you want to convert the array to numpy
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
add a comment |
As you wrote, it seems the behavior when you wrap by list is different
import numpy as np
import cupy as cp
print(list(np.arange(3)) # --> [0, 1, 2]
print(list(cp.arange(3)) # --> [array(0), array(1), array(2)]
However in your case, I think you can just use numpy array or cupy array without converting list.
y_true = test_set[:, 3] # it should work for both numpy & cupy
y_true_np = cuda.to_cpu(y_true) # If you want to convert the array to numpy
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
As you wrote, it seems the behavior when you wrap by list is different
import numpy as np
import cupy as cp
print(list(np.arange(3)) # --> [0, 1, 2]
print(list(cp.arange(3)) # --> [array(0), array(1), array(2)]
However in your case, I think you can just use numpy array or cupy array without converting list.
y_true = test_set[:, 3] # it should work for both numpy & cupy
y_true_np = cuda.to_cpu(y_true) # If you want to convert the array to numpy
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
answered Nov 22 '18 at 1:04
corochanncorochann
1,2051619
1,2051619
add a comment |
add a comment |
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