NeuralProcesses 0.1.1

A framework for composing Neural Processes in Python

Neural Processes

A framework for composing Neural Processes in Python. See also NeuralProcesses.jl.

This package is currently under construction. There will be more here soon. In the meantime, see NeuralProcesses.jl.

Installation

See the instructions here. Then simply

pip install neuralprocesses

Examples of Predefined Models

TensorFlow

GNP

import lab as B
import tensorflow as tf

import neuralprocesses.tensorflow as nps

cnp = nps.construct_gnp(dim_x=2, dim_y=3, likelihood="lowrank")
dist = cnp(
    B.randn(tf.float32, 16, 2, 10),
    B.randn(tf.float32, 16, 3, 10),
    B.randn(tf.float32, 16, 2, 15),
)
mean, var = dist.mean, dist.var

print(dist.logpdf(B.randn(tf.float32, 16, 3, 15)))
print(dist.kl(dist))
print(dist.entropy())

ConvGNP

import lab as B
import tensorflow as tf

import neuralprocesses.tensorflow as nps

cnp = nps.construct_convgnp(dim_x=2, dim_y=3, likelihood="lowrank")

dist = cnp(
    B.randn(tf.float32, 16, 2, 10),
    B.randn(tf.float32, 16, 3, 10),
    B.randn(tf.float32, 16, 2, 15),
)
mean, var = dist.mean, dist.var

print(dist.logpdf(B.randn(tf.float32, 16, 3, 15)))
print(dist.kl(dist))
print(dist.entropy())

PyTorch

GNP

import lab as B
import torch

import neuralprocesses.torch as nps

cnp = nps.construct_gnp(dim_x=2, dim_y=3, likelihood="lowrank")
dist = cnp(
    B.randn(torch.float32, 16, 2, 10),
    B.randn(torch.float32, 16, 3, 10),
    B.randn(torch.float32, 16, 2, 15),
)
mean, var = dist.mean, dist.var

print(dist.logpdf(B.randn(torch.float32, 16, 3, 15)))
print(dist.kl(dist))
print(dist.entropy())

ConvGNP

import lab as B
import torch

import neuralprocesses.torch as nps

cnp = nps.construct_convgnp(dim_x=2, dim_y=3, likelihood="lowrank")
dist = cnp(
    B.randn(torch.float32, 16, 2, 10),
    B.randn(torch.float32, 16, 3, 10),
    B.randn(torch.float32, 16, 2, 15),
)
mean, var = dist.mean, dist.var

print(dist.logpdf(B.randn(torch.float32, 16, 3, 15)))
print(dist.kl(dist))
print(dist.entropy())

Build Your Own Model

ConvGNP

TensorFlow

import lab as B
import tensorflow as tf

import neuralprocesses.tensorflow as nps

dim_x = 1
dim_y = 1

# CNN architecture:
unet = nps.UNet(
    dim=dim_x,
    in_channels=2 * dim_y,
    out_channels=(2 + 512) * dim_y,
    channels=(8, 16, 16, 32, 32, 64),
)

# Discretisation of the functional embedding:
disc = nps.Discretisation(
    points_per_unit=64,
    multiple=2 ** unet.num_halving_layers,
    margin=0.1,
    dim=dim_x,
)

# Create the encoder and decoder and construct the model.
encoder = nps.FunctionalCoder(
    disc,
    nps.Chain(
        nps.PrependDensityChannel(),
        nps.SetConv(disc.points_per_unit),
        nps.DivideByFirstChannel(),
    ),
)
decoder =  nps.Chain(
    unet,
    nps.SetConv(disc.points_per_unit),
    nps.LowRankGaussianLikelihood(512)
)
convgnp = nps.Model(encoder, decoder)

# Run the model on some random data.
dist = convgnp(
    B.randn(tf.float32, 16, 1, 10),
    B.randn(tf.float32, 16, 1, 10),
    B.randn(tf.float32, 16, 1, 15),
)