Loading own train data and labels in dataloader using pytorch?
$begingroup$
I have x_data and labels separately. How can I combine and load them in the model using torch.utils.data.DataLoader?
I have a dataset that I created and the training data has 20k samples and the labels are also separate. Lets say I want to load a dataset in the model, shuffle each time and use the batch size that I prefer. The Dataloader function does that. How can I combine and put them in the function so that I can train it in the model in pytorch?
python dataset preprocessing pytorch
edited 21 mins ago
Community♦
1
asked Feb 20 at 21:13
AmarnathAmarnath
112
$endgroup$
add a comment |
$begingroup$
I have x_data and labels separately. How can I combine and load them in the model using torch.utils.data.DataLoader?
I have a dataset that I created and the training data has 20k samples and the labels are also separate. Lets say I want to load a dataset in the model, shuffle each time and use the batch size that I prefer. The Dataloader function does that. How can I combine and put them in the function so that I can train it in the model in pytorch?
python dataset preprocessing pytorch
edited 21 mins ago
Community♦
1
asked Feb 20 at 21:13
AmarnathAmarnath
112
$endgroup$
$begingroup$
See discussion on StackOverflow here: stackoverflow.com/questions/41924453/…
$endgroup$
– joha
9 hours ago
add a comment |
$begingroup$
I have x_data and labels separately. How can I combine and load them in the model using torch.utils.data.DataLoader?
I have a dataset that I created and the training data has 20k samples and the labels are also separate. Lets say I want to load a dataset in the model, shuffle each time and use the batch size that I prefer. The Dataloader function does that. How can I combine and put them in the function so that I can train it in the model in pytorch?
python dataset preprocessing pytorch
edited 21 mins ago
Community♦
1
asked Feb 20 at 21:13
AmarnathAmarnath
112
$endgroup$
I have x_data and labels separately. How can I combine and load them in the model using torch.utils.data.DataLoader?
I have a dataset that I created and the training data has 20k samples and the labels are also separate. Lets say I want to load a dataset in the model, shuffle each time and use the batch size that I prefer. The Dataloader function does that. How can I combine and put them in the function so that I can train it in the model in pytorch?
python dataset preprocessing pytorch
python dataset preprocessing pytorch
edited 21 mins ago
Community♦
1
asked Feb 20 at 21:13
AmarnathAmarnath
112
edited 21 mins ago
Community♦
1
asked Feb 20 at 21:13
AmarnathAmarnath
112
edited 21 mins ago
Community♦
1
edited 21 mins ago
Community♦
1
edited 21 mins ago
Community♦
1
1
asked Feb 20 at 21:13
AmarnathAmarnath
112
asked Feb 20 at 21:13
AmarnathAmarnath
112
asked Feb 20 at 21:13
AmarnathAmarnath
112
112
$begingroup$
See discussion on StackOverflow here: stackoverflow.com/questions/41924453/…
$endgroup$
– joha
9 hours ago
add a comment |
$begingroup$
See discussion on StackOverflow here: stackoverflow.com/questions/41924453/…
$endgroup$
– joha
9 hours ago
$begingroup$
See discussion on StackOverflow here: stackoverflow.com/questions/41924453/…
$endgroup$
– joha
9 hours ago
$begingroup$
See discussion on StackOverflow here: stackoverflow.com/questions/41924453/…
$endgroup$
– joha
9 hours ago
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Assuming both of x_data and labels are lists or numpy arrays,
train_data =
for i in range(len(x_data)):
train_data.append([x_data[i], labels[i]])
trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
i1, l1 = next(iter(trainloader))
print(i1.shape)
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I think the standard way is to create a Dataset class object from the arrays and pass the Dataset object to the DataLoader.
One solution is to inherit from the Dataset class and define a custom class that implements __len__() and __get__(), where you pass X and y to the __init__(self,X,y).
For your simple case with two arrays and without the necessity for a special __get__() function beyond taking the values in row i, you can also use transform the arrays into Tensor objects and pass them to TensorDataset.
Run the following code for a self-contained example.
# Create a dataset like the one you describe
from sklearn.datasets import make_classification
X,y = make_classification()
# Load necessary Pytorch packages
from torch.utils.data import DataLoader, TensorDataset
from torch import Tensor
# Create dataset from several tensors with matching first dimension
# Samples will be drawn from the first dimension (rows)
dataset = TensorDataset( Tensor(X), Tensor(y) )
# Create a data loader from the dataset
# Type of sampling and batch size are specified at this step
loader = DataLoader(dataset, batch_size= 3)
# Quick test
next(iter(loader))
answered 9 hours ago
johajoha
1012
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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
$begingroup$
Assuming both of x_data and labels are lists or numpy arrays,
train_data =
for i in range(len(x_data)):
train_data.append([x_data[i], labels[i]])
trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
i1, l1 = next(iter(trainloader))
print(i1.shape)
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Assuming both of x_data and labels are lists or numpy arrays,
train_data =
for i in range(len(x_data)):
train_data.append([x_data[i], labels[i]])
trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
i1, l1 = next(iter(trainloader))
print(i1.shape)
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Assuming both of x_data and labels are lists or numpy arrays,
train_data =
for i in range(len(x_data)):
train_data.append([x_data[i], labels[i]])
trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
i1, l1 = next(iter(trainloader))
print(i1.shape)
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Assuming both of x_data and labels are lists or numpy arrays,
train_data =
for i in range(len(x_data)):
train_data.append([x_data[i], labels[i]])
trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
i1, l1 = next(iter(trainloader))
print(i1.shape)
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
answered 12 hours ago
Ashutosh MishraAshutosh Mishra
11
11
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Ashutosh Mishra is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
$begingroup$
I think the standard way is to create a Dataset class object from the arrays and pass the Dataset object to the DataLoader.
One solution is to inherit from the Dataset class and define a custom class that implements __len__() and __get__(), where you pass X and y to the __init__(self,X,y).
For your simple case with two arrays and without the necessity for a special __get__() function beyond taking the values in row i, you can also use transform the arrays into Tensor objects and pass them to TensorDataset.
Run the following code for a self-contained example.
# Create a dataset like the one you describe
from sklearn.datasets import make_classification
X,y = make_classification()
# Load necessary Pytorch packages
from torch.utils.data import DataLoader, TensorDataset
from torch import Tensor
# Create dataset from several tensors with matching first dimension
# Samples will be drawn from the first dimension (rows)
dataset = TensorDataset( Tensor(X), Tensor(y) )
# Create a data loader from the dataset
# Type of sampling and batch size are specified at this step
loader = DataLoader(dataset, batch_size= 3)
# Quick test
next(iter(loader))
answered 9 hours ago
johajoha
1012
$endgroup$
add a comment |
$begingroup$
I think the standard way is to create a Dataset class object from the arrays and pass the Dataset object to the DataLoader.
One solution is to inherit from the Dataset class and define a custom class that implements __len__() and __get__(), where you pass X and y to the __init__(self,X,y).
For your simple case with two arrays and without the necessity for a special __get__() function beyond taking the values in row i, you can also use transform the arrays into Tensor objects and pass them to TensorDataset.
Run the following code for a self-contained example.
# Create a dataset like the one you describe
from sklearn.datasets import make_classification
X,y = make_classification()
# Load necessary Pytorch packages
from torch.utils.data import DataLoader, TensorDataset
from torch import Tensor
# Create dataset from several tensors with matching first dimension
# Samples will be drawn from the first dimension (rows)
dataset = TensorDataset( Tensor(X), Tensor(y) )
# Create a data loader from the dataset
# Type of sampling and batch size are specified at this step
loader = DataLoader(dataset, batch_size= 3)
# Quick test
next(iter(loader))
answered 9 hours ago
johajoha
1012
$endgroup$
add a comment |
$begingroup$
I think the standard way is to create a Dataset class object from the arrays and pass the Dataset object to the DataLoader.
One solution is to inherit from the Dataset class and define a custom class that implements __len__() and __get__(), where you pass X and y to the __init__(self,X,y).
For your simple case with two arrays and without the necessity for a special __get__() function beyond taking the values in row i, you can also use transform the arrays into Tensor objects and pass them to TensorDataset.
Run the following code for a self-contained example.
# Create a dataset like the one you describe
from sklearn.datasets import make_classification
X,y = make_classification()
# Load necessary Pytorch packages
from torch.utils.data import DataLoader, TensorDataset
from torch import Tensor
# Create dataset from several tensors with matching first dimension
# Samples will be drawn from the first dimension (rows)
dataset = TensorDataset( Tensor(X), Tensor(y) )
# Create a data loader from the dataset
# Type of sampling and batch size are specified at this step
loader = DataLoader(dataset, batch_size= 3)
# Quick test
next(iter(loader))
answered 9 hours ago
johajoha
1012
$endgroup$
I think the standard way is to create a Dataset class object from the arrays and pass the Dataset object to the DataLoader.
One solution is to inherit from the Dataset class and define a custom class that implements __len__() and __get__(), where you pass X and y to the __init__(self,X,y).
For your simple case with two arrays and without the necessity for a special __get__() function beyond taking the values in row i, you can also use transform the arrays into Tensor objects and pass them to TensorDataset.
Run the following code for a self-contained example.
# Create a dataset like the one you describe
from sklearn.datasets import make_classification
X,y = make_classification()
# Load necessary Pytorch packages
from torch.utils.data import DataLoader, TensorDataset
from torch import Tensor
# Create dataset from several tensors with matching first dimension
# Samples will be drawn from the first dimension (rows)
dataset = TensorDataset( Tensor(X), Tensor(y) )
# Create a data loader from the dataset
# Type of sampling and batch size are specified at this step
loader = DataLoader(dataset, batch_size= 3)
# Quick test
next(iter(loader))
answered 9 hours ago
johajoha
1012
answered 9 hours ago
johajoha
1012
answered 9 hours ago
johajoha
1012
answered 9 hours ago
johajoha
1012
1012
add a comment |
add a comment |
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$begingroup$
See discussion on StackOverflow here: stackoverflow.com/questions/41924453/…
$endgroup$
– joha
9 hours ago