Neural Processes
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.
TL;DR! Just Get me Started!
Here you go:
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
)
1 maintainer
Metadata
1 maintainer
Release files for NeuralProcesses 0.2.7
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|---|---|---|---|---|
| neuralprocesses-0.2.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 182.9 kB
Release files / neuralprocesses-0.2.7.tar.gz
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