MNIST + Tensorflow

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library(MNIST)
library(tensorflow)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Load that data:

datasets <- tf$contrib$learn$datasets
mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)

Define model:

x <- tf$placeholder(tf$float32, shape(NULL, 784L))
W <- tf$Variable(tf$zeros(shape(784L, 10L)))
b <- tf$Variable(tf$zeros(shape(10L)))

y <- tf$nn$softmax(tf$matmul(x, W) + b)

# Define loss and optimizer
y_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * log(y), reduction_indices=1L))
train_step <- tf$train$GradientDescentOptimizer(0.5)$minimize(cross_entropy)

Create session and initialize variables

sess <- tf$Session()
sess$run(tf$global_variables_initializer())

# Train
for (i in 1:1000) {
  batches <- mnist$train$next_batch(100L)
  batch_xs <- batches[[1]]
  batch_ys <- batches[[2]]
  sess$run(train_step,
           feed_dict = dict(x = batch_xs, y_ = batch_ys))
}

Test trained model

correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L))
accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
sess$run(accuracy,
         feed_dict = dict(x = mnist$test$images, y_ = mnist$test$labels))

## [1] 0.9191

Deep MNIST

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)
}

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')
}

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)

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)

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)

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

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)

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$run(tf$global_variables_initializer())

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

## step 100, training accuracy 0.94
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train_accuracy <- accuracy$eval(session=sess, feed_dict = dict(
     x = mnist$test$images, y_ = mnist$test$labels, keep_prob = 1.0))
cat(sprintf("test accuracy %g", train_accuracy))

## test accuracy 0.9932

Chris Hua

2017-04-26

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Deep MNIST

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