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#' Train a recurrent convolutional network on the IMDB sentiment
#' classification task.
#'
#' Achieves 0.8498 test accuracy after 2 epochs. 41s/epoch on K520 GPU.
library(keras)
# Parameters --------------------------------------------------------------
# Embedding
max_features = 20000
maxlen = 100
embedding_size = 128
# Convolution
kernel_size = 5
filters = 64
pool_size = 4
# LSTM
lstm_output_size = 70
# Training
batch_size = 30
epochs = 2
# Data Preparation --------------------------------------------------------
# The x data includes integer sequences, each integer is a word
# The y data includes a set of integer labels (0 or 1)
# The num_words argument indicates that only the max_fetures most frequent
# words will be integerized. All other will be ignored.
# See help(dataset_imdb)
imdb <- dataset_imdb(num_words = max_features)
# Keras load all data into a list with the following structure:
str(imdb)
# Pad the sequences to the same length
# This will convert our dataset into a matrix: each line is a review
# and each column a word on the sequence
# We pad the sequences with 0s to the left
x_train <- imdb$train$x %>%
pad_sequences(maxlen = maxlen)
x_test <- imdb$test$x %>%
pad_sequences(maxlen = maxlen)
# Defining Model ------------------------------------------------------
model <- keras_model_sequential()
model %>%
layer_embedding(max_features, embedding_size, input_length = maxlen) %>%
layer_dropout(0.25) %>%
layer_conv_1d(
filters,
kernel_size,
padding = "valid",
activation = "relu",
strides = 1
) %>%
layer_max_pooling_1d(pool_size) %>%
layer_lstm(lstm_output_size) %>%
layer_dense(1) %>%
layer_activation("sigmoid")
model %>% compile(
loss = "binary_crossentropy",
optimizer = "adam",
metrics = "accuracy"
)
# Training ----------------------------------------------------------------
model %>% fit(
x_train, imdb$train$y,
batch_size = batch_size,
epochs = epochs,
validation_data = list(x_test, imdb$test$y)
)