Coursera Neural Networks – week 5 – embeddings visualisation with PCA in R

m10_vocab.csv

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output_vocab.m

## the csvwrite won't work for cell of strings so have to do this manually--seriously!
clear all;

load('m10.mat');  ## this is the trained model object 

fid = fopen("m10_vocab.csv", "a");
vocab = m10.vocab;
nVocab = size(vocab, 2);


 for i = 1:nVocab
   fprintf(fid, "%s\n", cell2mat(vocab(i)));
   # printf("%s\n", cell2mat(vocab(i)));
 endfor

fclose(fid);

## also write out the embeddings
csvwrite('m10_word_embedding_weights.csv', m10.word_embedding_weights)

word_embeddings_visualisation.R

rm(list = ls())

setwd("~/Documents/Courses/neural networks for machine learning/lecture_5/assignment2/")

library(readr)
library(ggplot2)
theme_set(theme_bw())

# read in the embeddings (250 x 50) --> rows: vocabulary words, cols = embedding units
wordEmbeddingsWeights <- read_csv("./m10_word_embedding_weights.csv", col_names = FALSE)
vocab <- read_csv("./m10_vocab.csv", col_names = FALSE)


pca <- prcomp(wordEmbeddingsWeights)

plotDf <- as.data.frame(pca$x[, 1:2]) %>%
  mutate(vocab = vocab$X1)
p <- ggplot(plotDf, aes(x = PC1, y = PC2)) + geom_text(aes(label = vocab)) 
p

# zoom in
p + ylim(c(-1, 1)) + xlim(c(-1,1))

# plotly!
plotly::ggplotly(p)

word_embeddings_weights.csv