How do I split the training dataset into training, validation and test datasets?
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I have a custom data set of images and its target. I have created a training data set in PyTorch. I want to split it into 3 parts: training, validation and test. How do I do it?
machine-learning dataset conv-neural-network pytorch
edited Jan 7 at 14:34
nbro
5,786105198
asked Nov 29 '18 at 5:22
SherlockSherlock
416
add a comment |
1
I have a custom data set of images and its target. I have created a training data set in PyTorch. I want to split it into 3 parts: training, validation and test. How do I do it?
machine-learning dataset conv-neural-network pytorch
edited Jan 7 at 14:34
nbro
5,786105198
asked Nov 29 '18 at 5:22
SherlockSherlock
416
add a comment |
1
1
1
2
I have a custom data set of images and its target. I have created a training data set in PyTorch. I want to split it into 3 parts: training, validation and test. How do I do it?
machine-learning dataset conv-neural-network pytorch
edited Jan 7 at 14:34
nbro
5,786105198
asked Nov 29 '18 at 5:22
SherlockSherlock
416
I have a custom data set of images and its target. I have created a training data set in PyTorch. I want to split it into 3 parts: training, validation and test. How do I do it?
machine-learning dataset conv-neural-network pytorch
machine-learning dataset conv-neural-network pytorch
edited Jan 7 at 14:34
nbro
5,786105198
asked Nov 29 '18 at 5:22
SherlockSherlock
416
edited Jan 7 at 14:34
nbro
5,786105198
asked Nov 29 '18 at 5:22
SherlockSherlock
416
edited Jan 7 at 14:34
nbro
5,786105198
edited Jan 7 at 14:34
nbro
5,786105198
edited Jan 7 at 14:34
nbro
5,786105198
5,786105198
asked Nov 29 '18 at 5:22
SherlockSherlock
416
asked Nov 29 '18 at 5:22
SherlockSherlock
416
asked Nov 29 '18 at 5:22
SherlockSherlock
416
416
add a comment |
add a comment |
1 Answer
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Once you have the "master" dataset you can use data.Subset to split it.
Here's an example for random split
import torch
from torch.utils import data
import random
master = data.Dataset( ... ) # your "master" dataset
n = len(master) # how many total elements you have
n_test = int( n * .05 ) # number of test/val elements
n_train = n - 2 * n_test
idx = list(range(n)) # indices to all elements
random.shuffle(idx) # in-place shuffle the indices to facilitate random splitting
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = data.Subset(master, train_idx)
val_set = data.Subset(master, val_idx)
test_set = data.Subset(master, test_idx)
This can also be achieved using data.random_split:
train_set, val_set, test_set = data.random_split(master, (n_train, n_val, n_test))
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
add a comment |
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1 Answer
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1 Answer
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2
Once you have the "master" dataset you can use data.Subset to split it.
Here's an example for random split
import torch
from torch.utils import data
import random
master = data.Dataset( ... ) # your "master" dataset
n = len(master) # how many total elements you have
n_test = int( n * .05 ) # number of test/val elements
n_train = n - 2 * n_test
idx = list(range(n)) # indices to all elements
random.shuffle(idx) # in-place shuffle the indices to facilitate random splitting
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = data.Subset(master, train_idx)
val_set = data.Subset(master, val_idx)
test_set = data.Subset(master, test_idx)
This can also be achieved using data.random_split:
train_set, val_set, test_set = data.random_split(master, (n_train, n_val, n_test))
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
add a comment |
2
Once you have the "master" dataset you can use data.Subset to split it.
Here's an example for random split
import torch
from torch.utils import data
import random
master = data.Dataset( ... ) # your "master" dataset
n = len(master) # how many total elements you have
n_test = int( n * .05 ) # number of test/val elements
n_train = n - 2 * n_test
idx = list(range(n)) # indices to all elements
random.shuffle(idx) # in-place shuffle the indices to facilitate random splitting
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = data.Subset(master, train_idx)
val_set = data.Subset(master, val_idx)
test_set = data.Subset(master, test_idx)
This can also be achieved using data.random_split:
train_set, val_set, test_set = data.random_split(master, (n_train, n_val, n_test))
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
add a comment |
2
2
2
Once you have the "master" dataset you can use data.Subset to split it.
Here's an example for random split
import torch
from torch.utils import data
import random
master = data.Dataset( ... ) # your "master" dataset
n = len(master) # how many total elements you have
n_test = int( n * .05 ) # number of test/val elements
n_train = n - 2 * n_test
idx = list(range(n)) # indices to all elements
random.shuffle(idx) # in-place shuffle the indices to facilitate random splitting
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = data.Subset(master, train_idx)
val_set = data.Subset(master, val_idx)
test_set = data.Subset(master, test_idx)
This can also be achieved using data.random_split:
train_set, val_set, test_set = data.random_split(master, (n_train, n_val, n_test))
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
Once you have the "master" dataset you can use data.Subset to split it.
Here's an example for random split
import torch
from torch.utils import data
import random
master = data.Dataset( ... ) # your "master" dataset
n = len(master) # how many total elements you have
n_test = int( n * .05 ) # number of test/val elements
n_train = n - 2 * n_test
idx = list(range(n)) # indices to all elements
random.shuffle(idx) # in-place shuffle the indices to facilitate random splitting
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = data.Subset(master, train_idx)
val_set = data.Subset(master, val_idx)
test_set = data.Subset(master, test_idx)
This can also be achieved using data.random_split:
train_set, val_set, test_set = data.random_split(master, (n_train, n_val, n_test))
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
edited Dec 2 '18 at 5:59
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
answered Nov 29 '18 at 6:35
ShaiShai
70.8k23138247
70.8k23138247
add a comment |
add a comment |
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