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Training neural networks in PyTorch

Project description

torchtuples

Python package 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).

Installation

Requires python 3.6 or 3.7.

torchtuples depends on PyTorch which should be installed from HERE.

Next, torchtuples can be installed using pip:

pip install git+git://github.com/havakv/torchtuples.git

or by cloning the repo:

git clone https://github.com/havakv/torchtuples.git
cd torchtuples
python setup.py install

Example

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):
        super().__init__()
        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 = torch.cat([x, x_tensor], dim=1)
        return self.lin_cat(x)

We can now fit the model with

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

and make predictions with

preds = model.predict(x)

Project details


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