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Training neural networks in PyTorch

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Python package PyPI PyPI PyPI - Python Version License

torchtuples is a small python package for training PyTorch models. It works equally well for numpy arrays and torch tensors. One of the main benefits of torchtuples is that it handles data in the form of nested tuples (see example below).


torchtuples depends on PyTorch which should be installed from HERE.

Next, torchtuples can be installed with pip:

pip install torchtuples

Or, via conda:

conda install -c conda-forge torchtuples

For the bleeding edge version, install directly from github (consider adding --force-reinstall):

pip install git+git://

or by cloning the repo:

git clone
cd torchtuples
python install


import torch
from torch import nn
from torchtuples import Model, optim

Make a data set with three sets of covariates x0, x1 and x2, and a target y. The covariates are structured in a nested tuple x.

n = 500
x0, x1, x2 = [torch.randn(n, 3) for _ in range(3)]
y = torch.randn(n, 1)
x = (x0, (x0, x1, x2))

Create a simple ReLU net that takes as input the tensor x_tensor and the tuple x_tuple. Note that x_tuple can be of arbitrary length. The tensors in x_tuple are passed through a layer lin_tuple, averaged, and concatenated with x_tensor. We then pass our new tensor through the layer lin_cat.

class Net(nn.Module):
    def __init__(self):
        self.lin_tuple = nn.Linear(3, 2)
        self.lin_cat = nn.Linear(5, 1)
        self.relu = nn.ReLU()

    def forward(self, x_tensor, x_tuple):
        x = [self.relu(self.lin_tuple(xi)) for xi in x_tuple]
        x = torch.stack(x).mean(0)
        x =[x, x_tensor], dim=1)
        return self.lin_cat(x)

    def predict(self, x_tensor, x_tuple):
        x = self.forward(x_tensor, x_tuple)
        return torch.sigmoid(x)

We can now fit the model with

model = Model(Net(), nn.MSELoss(), optim.SGD(0.01))
log =, y, batch_size=64, epochs=5)

and make predictions with either the Net.predict method

preds = model.predict(x)

or with the Net.forward method

preds = model.predict_net(x)

For more examples, see the examples folder.

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