Log of the R console for replicating #3736(dmlc/mxnet)

Replicate_R_CNN.txt

 # Clean workspace
> rm(list=ls())
> 
> # Load MXNet
> require(mxnet)
> 
> # Loading data and set up
> #-------------------------------------------------------------------------------
> 
> # Load train and test datasets
> train <- read.csv("JAFFE_64_train_multi_label.csv")
> test <- read.csv("JAFFE_64_test_multi_label.csv")
> 
> # Set up train and test datasets
> train <- data.matrix(train)
> train_x <- t(train[, -(1:6)])
> train_y <- train[, 1:6]
> train_array <- train_x
> dim(train_array) <- c(64, 64, 1, ncol(train_x))
> 
> test_x <- t(test[, -(1:6)])
> test_y <- test[, 1:6]
> test_array <- test_x
> dim(test_array) <- c(64, 64, 1, ncol(test_x))
> # Set up the symbolic model
> #-------------------------------------------------------------------------------
> 
> data <- mx.symbol.Variable('data')
> # 1st convolutional layer
> 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")
> #tanh_1 <- mx.symbol.SoftmaxActivation(data = conv_1)
> pool_1 <- mx.symbol.Pooling(data = tanh_1, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))
> # 2nd convolutional layer
> 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")
> #tanh_2 <- mx.symbol.SoftmaxActivation(data = conv_2)
> pool_2 <- mx.symbol.Pooling(data=tanh_2, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))
> # 1st fully connected layer
> flatten <- mx.symbol.Flatten(data = pool_2)
> fc_1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = 500)
> tanh_3 <- mx.symbol.Activation(data = fc_1, act_type = "tanh")
> # 2nd fully connected layer
> fc_2 <- mx.symbol.FullyConnected(data = tanh_3, num_hidden = 6)
> # Output. Softmax output since we'd like to get some probabilities.
> NN_model <- mx.symbol.SoftmaxOutput(data = fc_2, multi.output = TRUE)
> # Set seed for reproducibility
> mx.set.seed(100)
> 
> # Device used. CPU in my case.
> devices <- mx.cpu()
> 
> model <- mx.model.FeedForward.create(NN_model,
+                                      X = train_array,
+                                      y = train_y,
+                                      ctx = devices,
+                                      num.round = 380,
+                                      array.batch.size = 76,
+                                      learning.rate = 0.01,
+                                      momentum = 0.9,
+                                      eval.metric = mx.metric.accuracy,
+                                      epoch.end.callback = mx.callback.log.train.metric(100))
Error in mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds,  : 
  basic_string::resize