add example "GloVe"

Author: turgut090

vignettes/examples/load_GloVe.R

@@ -0,0 +1,87 @@
+# This example shows how one can quickly load glove vectors
+# and train a Keras model in R
+
+library(keras)
+library(dplyr)
+
+# Download Glove vectors if necessary
+if (!file.exists('glove.6B.zip')) {
+  download.file('http://nlp.stanford.edu/data/glove.6B.zip',destfile = 'glove.6B.zip')
+  unzip('glove.6B.zip')
+}
+
+# load an example dataset from text2vec
+library(text2vec)
+data("movie_review")
+as_tibble(movie_review)
+
+# load glove vectors into R
+vectors = data.table::fread('glove.6B.300d.txt', data.table = F,  encoding = 'UTF-8') 
+colnames(vectors) = c('word',paste('dim',1:300,sep = '_'))
+
+# structure of the vectors
+as_tibble(vectors)
+
+# define parameters of Keras model
+library(keras)
+max_words = 1e4
+maxlen = 60
+dim_size = 300
+
+# tokenize the input data and then fit the created object
+word_seqs = text_tokenizer(num_words = max_words) %>%
+  fit_text_tokenizer(movie_review$review)
+
+# apply tokenizer to the text and get indices instead of words
+# later pad the sequence
+x_train = texts_to_sequences(word_seqs, movie_review$review) %>%
+  pad_sequences( maxlen = maxlen)
+
+# extract the output
+y_train = as.matrix(movie_review$sentiment)
+
+# unlist word indices
+word_indices = unlist(word_seqs$word_index)
+
+# then place them into data.frame 
+dic = data.frame(word = names(word_indices), key = word_indices, stringsAsFactors = FALSE) %>%
+  arrange(key) %>% .[1:max_words,]
+
+# join the words with GloVe vectors and
+# if word does not exist in GloVe, then fill NA's with 0
+word_embeds = dic  %>% left_join(vectors) %>% .[,3:302] %>% replace(., is.na(.), 0) %>% as.matrix()
+
+# Use Keras Functional API 
+input = layer_input(shape = list(maxlen), name = "input")
+
+model = input %>%
+  layer_embedding(input_dim = max_words, output_dim = dim_size, input_length = maxlen, 
+                  # put weights into list and do not allow training
+                  weights = list(word_embeds), trainable = FALSE) %>%
+  layer_spatial_dropout_1d(rate = 0.2 ) %>%
+  bidirectional(
+    layer_gru(units = 80, return_sequences = TRUE) 
+  )
+max_pool = model %>% layer_global_max_pooling_1d()
+ave_pool = model %>% layer_global_average_pooling_1d()
+
+output = layer_concatenate(list(ave_pool, max_pool)) %>%
+  layer_dense(units = 1, activation = "sigmoid")
+
+model = keras_model(input, output)
+
+# instead of accuracy we can use "AUC" metrics from "tensorflow.keras"
+model %>% compile(
+  optimizer = "adam",
+  loss = "binary_crossentropy",
+  metrics = tensorflow::tf$keras$metrics$AUC()
+)
+
+history = model %>% keras::fit(
+  x_train, y_train,
+  epochs = 8,
+  batch_size = 32,
+  validation_split = 0.2
+)
+
+