R/tensorflow/abcdefg.R

In lstegger/fkdR: Facial Keypoint Detection Package

# Load data and start Tensorflow session
library(tensorflow)
datasets <- tf$contrib$learn$datasets
mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
sess <- tf$InteractiveSession()

# --------------------------
# Multilayer ConvNet
# --------------------------

# Weight Initialization
weight_variable <- function(shape) {
  initial <- tf$truncated_normal(shape, stddev=0.1)
  tf$Variable(initial)
}

bias_variable <- function(shape) {
  initial <- tf$constant(0.1, shape=shape)
  tf$Variable(initial)
}

# Convolution and Pooling
conv2d <- function(x, W) {
  tf$nn$conv2d(x, W, strides=c(1L, 1L, 1L, 1L), padding='SAME')
}

max_pool_2x2 <- function(x) {
  tf$nn$max_pool(
    x,
    ksize=c(1L, 2L, 2L, 1L),
    strides=c(1L, 2L, 2L, 1L),
    padding='SAME')
}

# First Convolutional Layer
W_conv1 <- weight_variable(shape(5L, 5L, 1L, 32L))
b_conv1 <- bias_variable(shape(32L))

x_image <- tf$reshape(x, shape(-1L, 28L, 28L, 1L))

h_conv1 <- tf$nn$relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 <- max_pool_2x2(h_conv1)

# Second Convolutional Layer
W_conv2 <- weight_variable(shape = shape(5L, 5L, 32L, 64L))
b_conv2 <- bias_variable(shape = shape(64L))

h_conv2 <- tf$nn$relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 <- max_pool_2x2(h_conv2)

# Densely Connected Layer
W_fc1 <- weight_variable(shape(7L * 7L * 64L, 1024L))
b_fc1 <- bias_variable(shape(1024L))

h_pool2_flat <- tf$reshape(h_pool2, shape(-1L, 7L * 7L * 64L))
h_fc1 <- tf$nn$relu(tf$matmul(h_pool2_flat, W_fc1) + b_fc1)

## Dropout
keep_prob <- tf$placeholder(tf$float32)
h_fc1_drop <- tf$nn$dropout(h_fc1, keep_prob)

# Readout Layer
W_fc2 <- weight_variable(shape(1024L, 10L))
b_fc2 <- bias_variable(shape(10L))

y_conv <- tf$nn$softmax(tf$matmul(h_fc1_drop, W_fc2) + b_fc2)

# Train and Evaluate the Model
cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * tf$log(y_conv), reduction_indices=1L))
train_step <- tf$train$AdamOptimizer(1e-4)$minimize(cross_entropy)
correct_prediction <- tf$equal(tf$argmax(y_conv, 1L), tf$argmax(y_, 1L))
accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
sess$run(tf$initialize_all_variables())

for (i in 1:20000) {
  batch <- mnist$train$next_batch(50L)
  if (i %% 100 == 0) {
    train_accuracy <- accuracy$eval(feed_dict = dict(
      x = batch[[1]], y_ = batch[[2]], keep_prob = 1.0))
    cat(sprintf("step %d, training accuracy %g\n", i, train_accuracy))
  }
  train_step$run(feed_dict = dict(
    x = batch[[1]], y_ = batch[[2]], keep_prob = 0.5))
}

train_accuracy <- accuracy$eval(feed_dict = dict(
  x = mnist$test$images, y_ = mnist$test$labels, keep_prob = 1.0))
cat(sprintf("test accuracy %g", train_accuracy))

# Save data
saver <- tf$train$Saver()
data_file <- saver$save(sess, "./myData.ckpt")

# Restore Data
# with(tf$Session() %as% sess, {
#   saver$restore(sess, "./myData.ckpt")
#   cat("Model restored.\n")
# })