Neural Processes

This is an implementation of Neural Processes for 1D-regression, accompanying my blog post.

Structure of the repo

The implementation uses TensorFlow in R:

• The files NP_architecture*.R specify the NN architectures for the encoder h and decoder g as well as the aggregator and the mapping from r to z.
• The file NP_core.R contains functions to define the loss function and carry out posterior prediction.

Note: when changing network architecture, e.g. when fitting a new model, you need to run tf$reset_default_graph() or restart your R session.

All experiments can be found in the "experiments" folder (where they appear in the same order as in the blog post):

• The first experiment involves training an NP on a single small data set.
• The second experiment involves training an NP on a small class of functions of the form a * sin(x).
• The third experiment involves training an NP on repeated draws from the GP.

Example code

Loading all the libraries and helper functions

library(tidyverse)
library(tensorflow)
library(patchwork)

source("NP_architecture1.R")
source("NP_core.R")
source("GP_helpers.R")
source("helpers_for_plotting.R")

Setting up the NP model:

sess <- tf$Session()

# specify (global variables) for dimensionality of r, z, and hidden layers of g and h
dim_r <- 2L
dim_z <- 2L
dim_h_hidden <- 32L
dim_g_hidden <- 32L

# placeholders for training inputs
x_context <- tf$placeholder(tf$float32, shape(NULL, 1))
y_context <- tf$placeholder(tf$float32, shape(NULL, 1))
x_target <- tf$placeholder(tf$float32, shape(NULL, 1))
y_target <- tf$placeholder(tf$float32, shape(NULL, 1))

# set up NN
train_op_and_loss <- init_NP(x_context, y_context, x_target, y_target, learning_rate = 0.001)

# initialise
init <- tf$global_variables_initializer()
sess$run(init)

Now, sampling data according to the function y = a*sin(x),we can fit the model as follows:

n_iter <- 10000

for(iter in 1:n_iter){
  # sample data (x_obs, y_obs)
  N <- 20
  x_obs <- runif(N, -3, 3)
  a <- runif(1, -2, 2)
  y_obs <- a * sin(x_obs)

  # sample N_context for training
  N_context <- sample(1:10, 1)

  # use helper function to pick a random context set
  feed_dict <- helper_context_and_target(x_obs, y_obs, N_context, x_context, y_context, x_target, y_target)

  # optimisation step
  a <- sess$run(train_op_and_loss, feed_dict = feed_dict)

  if(iter %% 1e3 == 0){
    cat(sprintf("loss = %1.3f\n", a[[2]]))
  }
}

Prediction using the trained model:

# context set at prediction-time
x0 <- c(0, 1)
y0 <- 1*sin(x0)

# prediction grid
x_star <- seq(-4, 4, length=100)

# plot posterior draws
plot_posterior_draws(x0, y0, x_star, n_draws = 50)

Other resources

Update (February 2019): The authors of the Neural Process papers have now made their implementation available here https://github.com/deepmind/neural-processes