tensorflow.r

# Setup the model
x <- tf$placeholder(tf$float32, shape(NULL, 1024L))
W <- tf$Variable(tf$zeros(shape(1024L, 10L)))
b <- tf$Variable(tf$zeros(shape(10L)))
t <- tf$nn$softmax(tf$matmul(x, W) + b)
# Specify the loss and optimizer
t_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
xent <- tf$reduce_mean(-tf$reduce_sum(t_ * log(t), reduction_indices=1L))
batch_step <- tf$train$GradientDescentOptimizer(0.5)$minimize(xent)
titanic_train_ds <- csv_record_spec("titanic.train.csv")
# Train
for (i in 1:1000) {
  batches <- tf$contrib$csv_batch$titanic_train_ds(100L)
  batch_xs <- batches[[1]]
  batch_ts <- batches[[2]]
  sess$run(train_step,
           feed_dict = dict(x = batch_xs, t_ = batch_ts))
}