Question

我试图在R Studio中运行此脚本，并且如果我运行以下任一行（在脚本的其余部分之后），它将强制退出R Studio

find_similar_words("2", embedding_matrix)

tsne <- Rtsne(embedding_matrix[2:500,], perplexity = 50, pca = FALSE)

给我“ R遇到致命错误，会话已终止”

这是整个脚本（如果您想尝试使用它，我会注释数据URL源）

#download.file("https://snap.stanford.edu/data/finefoods.txt.gz", "finefoods.txt.gz")
library(readr)
library(stringr)
library(reticulate)
library(purrr)
library(keras)
library(dplyr)
library(Rtsne)
library(ggplot2)
library(plotly)



reviews <- read_lines("finefoods.txt") 
reviews <- reviews[str_sub(reviews, 1, 12) == "review/text:"]
reviews <- str_sub(reviews, start = 14)
head(reviews, 2)


tokenizer <- text_tokenizer(num_words = 20000)
tokenizer %>% fit_text_tokenizer(reviews)


skipgrams_generator <- function(text, tokenizer, window_size, negative_samples) {
  gen <- texts_to_sequences_generator(tokenizer, sample(text))
  function() {
    skip <- iter_next(gen) %>%
      skipgrams(
        vocabulary_size = tokenizer$num_words, 
        window_size = window_size, 
        negative_samples = 1
      )
    x <- transpose(skip$couples) %>% map(. %>% unlist %>% as.matrix(ncol = 1))
    y <- skip$labels %>% as.matrix(ncol = 1)
    list(x, y)
  }
}

embedding_size <- 128  # Dimension of the embedding vector.
skip_window <- 5       # How many words to consider left and right.
num_sampled <- 1       # Number of negative examples to sample for each word.
input_target <- layer_input(shape = 1)
input_context <- layer_input(shape = 1)
embedding <- layer_embedding(
  input_dim = tokenizer$num_words + 1, 
  output_dim = embedding_size, 
  input_length = 1, 
  name = "embedding"
)

target_vector <- input_target %>% 
  embedding() %>% 
  layer_flatten()

context_vector <- input_context %>%
  embedding() %>%
  layer_flatten()
dot_product <- layer_dot(list(target_vector, context_vector), axes = 1)
output <- layer_dense(dot_product, units = 1, activation = "sigmoid")
model <- keras_model(list(input_target, input_context), output)
model %>% compile(loss = "binary_crossentropy", optimizer = "adam")
summary(model)
model %>%
  fit_generator(
    skipgrams_generator(reviews, tokenizer, skip_window, negative_samples), 
    steps_per_epoch = 100, epochs = 5
  )

embedding_matrix <- get_weights(model)[[1]]

words <- data_frame(
  word = names(tokenizer$word_index), 
  id = as.integer(unlist(tokenizer$word_index))
)

words <- words %>%
  filter(id <= tokenizer$num_words) %>%
  arrange(id)

row.names(embedding_matrix) <- c("UNK", words$word)

library(text2vec)

find_similar_words <- function(word, embedding_matrix, n = 5) {
  similarities <- embedding_matrix[word, , drop = FALSE] %>%
    sim2(embedding_matrix, y = ., method = "cosine")

  similarities[,1] %>% sort(decreasing = TRUE) %>% head(n)
}


find_similar_words("2", embedding_matrix)

tsne <- Rtsne(embedding_matrix[2:500,], perplexity = 50, pca = FALSE)

tsne_plot <- tsne$Y %>%
  as.data.frame() %>%
  mutate(word = row.names(embedding_matrix)[2:500]) %>%
  ggplot(aes(x = V1, y = V2, label = word)) +
  geom_text(size = 3)
tsne_plot

R Studio崩溃似乎与RTsne有关

0 个答案: