https://github.com/wesselb/neuralprocesses

A framework for composing Neural Processes in Python
https://github.com/wesselb/neuralprocesses

machine-learning meta-learning neural-processes

Last synced: about 1 year ago
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A framework for composing Neural Processes in Python

• Host: GitHub
• URL: https://github.com/wesselb/neuralprocesses
• Owner: wesselb
• License: mit
• Created: 2020-12-22T16:25:33.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2024-12-17T21:08:13.000Z (over 1 year ago)
• Last Synced: 2025-03-28T10:05:06.474Z (about 1 year ago)
• Topics: machine-learning, meta-learning, neural-processes
• Language: Python
• Homepage: https://wesselb.github.io/neuralprocesses
• Size: 8 MB
• Stars: 82
• Watchers: 4
• Forks: 15
• Open Issues: 8
• Metadata Files:
  • Readme: README.md

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README

          
# [Neural Processes](http://github.com/wesselb/neuralprocesses)

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A framework for composing Neural Processes in Python.

## Installation

```

pip install neuralprocesses tensorflow tensorflow-probability  # For use with TensorFlow

pip install neuralprocesses torch                              # For use with PyTorch

```

If something is not working or unclear, please feel free to open an issue.

## Documentation

See [here](https://wesselb.github.io/neuralprocesses).

## TL;DR! Just Get me Started!

Here you go:

```python

import torch

import neuralprocesses.torch as nps

# Construct a ConvCNP.

convcnp = nps.construct_convgnp(dim_x=1, dim_y=2, likelihood="het")

# Construct optimiser.

opt = torch.optim.Adam(convcnp.parameters(), 1e-3)

# Training: optimise the model for 32 batches.

for _ in range(32):

    # Sample a batch of new context and target sets. Replace this with your data. The

    # shapes are `(batch_size, dimensionality, num_data)`.

    xc = torch.randn(16, 1, 10)  # Context inputs

    yc = torch.randn(16, 2, 10)  # Context outputs

    xt = torch.randn(16, 1, 15)  # Target inputs

    yt = torch.randn(16, 2, 15)  # Target output

    # Compute the loss and update the model parameters.

    loss = -torch.mean(nps.loglik(convcnp, xc, yc, xt, yt, normalise=True))

    opt.zero_grad(set_to_none=True)

    loss.backward()

    opt.step()

# Testing: make some predictions.

mean, var, noiseless_samples, noisy_samples = nps.predict(

    convcnp,

    torch.randn(16, 1, 10),  # Context inputs

    torch.randn(16, 2, 10),  # Context outputs

    torch.randn(16, 1, 15),  # Target inputs

)

```