使用RStudio的Keras接口使神经网络训练可重现

library(keras)
library(tensorflow)

set.seed(42)
tensorflow::set_random_seed(42)

options(keras.backend.num_cores = 1)
options(keras.backend.num_gpus = 0)

model <- keras_model_sequential()
model %>% 
  layer_dense(units = 64, activation = 'relu', input_shape = c(100)) %>%
  layer_dense(units = 10, activation = 'softmax')

model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_sgd(lr = 0.01),
  metrics = c('accuracy')
)

session_conf <- tf$ConfigProto(
  intra_op_parallelism_threads = 1,
  inter_op_parallelism_threads = 1
)

sess <- tf$Session(graph = tf$Graph(), config = session_conf)
keras::backend$set_session(sess)