Image recognition in R using convolutional neural networks with the MXNet package. Part 2. Full article at: https://firsttimeprogrammer.blogspot.com/2016/07/image-recognition-in-r-using.html

im_rec_3.R

rm(list=ls())

# Load MXNet
require(mxnet)

# Train test datasets
train <- read.csv("train_28.csv")
test <- read.csv("test_28.csv")

# Fix train and test datasets
train <- data.matrix(train)
train_x <- t(train[,-1])
train_y <- train[,1]
train_array <- train_x
dim(train_array) <- c(28, 28, 1, ncol(train_x))

test__ <- data.matrix(test)
test_x <- t(test[,-1])
test_y <- test[,1]
test_array <- test_x
dim(test_array) <- c(28, 28, 1, ncol(test_x))

# Model
data <- mx.symbol.Variable('data')
# 1st convolutional layer 5x5 kernel and 20 filters.
conv_1 <- mx.symbol.Convolution(data= data, kernel = c(5,5), num_filter = 20)
tanh_1 <- mx.symbol.Activation(data= conv_1, act_type = "tanh")
pool_1 <- mx.symbol.Pooling(data = tanh_1, pool_type = "max", kernel = c(2,2), stride = c(2,2))
# 2nd convolutional layer 5x5 kernel and 50 filters.
conv_2 <- mx.symbol.Convolution(data = pool_1, kernel = c(5,5), num_filter = 50)
tanh_2 <- mx.symbol.Activation(data = conv_2, act_type = "tanh")
pool_2 <- mx.symbol.Pooling(data = tanh_2, pool_type = "max", kernel = c(2,2), stride = c(2,2))
# 1st fully connected layer
flat <- mx.symbol.Flatten(data = pool_2)
fcl_1 <- mx.symbol.FullyConnected(data = flat, num_hidden = 500)
tanh_3 <- mx.symbol.Activation(data = fcl_1, act_type = "tanh")
# 2nd fully connected layer
fcl_2 <- mx.symbol.FullyConnected(data = tanh_3, num_hidden = 2)
# Output
NN_model <- mx.symbol.SoftmaxOutput(data = fcl_2)

# Set seed for reproducibility
mx.set.seed(100)

# Device used. Sadly not the GPU :-(
device <- mx.cpu()

# Train on 1200 samples
model <- mx.model.FeedForward.create(NN_model, X = train_array, y = train_y,
                                     ctx = device,
                                     num.round = 30,
                                     array.batch.size = 100,
                                     learning.rate = 0.05,
                                     momentum = 0.9,
                                     wd = 0.00001,
                                     eval.metric = mx.metric.accuracy,
                                     epoch.end.callback = mx.callback.log.train.metric(100))

# Test on 312 samples
predict_probs <- predict(model, test_array)
predicted_labels <- max.col(t(predict_probs)) - 1
table(test__[,1], predicted_labels)
sum(diag(table(test__[,1], predicted_labels)))/312

##############################################
# Output
##############################################
#   predicted_labels
#      0   1
#  0  83  47
#  1  34 149
#
#
# [1] 0.7412141
#

Thank you for your code and blogs, it's so great
But I encountered a few problems
in plants_28.csv Set label <<<<< label set 2?

in Test on 312 samples My results:
predicted_labels
        1
  1 156
  2 156
I can not find what is causing this

Sincerely

@you-ruei Hi, I made a new tutorial with a reproducible example so that you can check step by step that each intermediate result is correct. This more comprehensive tutorial can be found at https://firsttimeprogrammer.blogspot.it/2016/08/image-recognition-tutorial-in-r-using.html if you are new to deep learning with MXNet I suggest you to try this first, then you can apply the same logic to the dogs vs plants classification and maybe do other experiments.