tf.enable_eager_execution error: R encounter a fatal error

[Kalondepatrick]

Hello,

I am learning to build convolution neural networks using tensorflow framework in R and when I run tensor_slice_dataset() I get an error indicating that the tensor object has no attribute ‘numpy’

In search for a solution, one tutorial that I found online indicated that soon after loading tensoflow library, I have to function enable_eager_execution(). However when I do that my r session is immediately terminated on the condition that R encountered a fatal error

I am currently looking for ideas on what could be causing this and possible remedies.

From the attempts that I have done so far, here is the interesting thing: 'the problem only occurs on my windows computer, and not on my mac'.

On my Mac, that step run smoothly with no errors and I am only having problems when I want to use my tfdatasets as training_dataset <- dataset_map(training_dataset, function(.x) list_modify(.x, img = tf$image$decode_jpeg(tf$io$read_file(.x$img)))). I am getting the error Error in py_call_impl(callable, dots$args, dots$keywords) : RuntimeError: in user code.

[t-kalinowski]

Hi, can you please provide a code snippet I can copy+paste into my R console to reproduce the error you're seeing? The reprex package might be helpful.

[Kalondepatrick]

Hi, can you please provide a code snippet I can copy+paste into my R console to reproduce the error you're seeing? The reprex package might be helpful.

#############################################
# Setting up the environment
############################################

# Libraries
library(keras) 
library(tensorflow) 
library(tfdatasets) 
library(purrr) 
library(ggplot2)
library(rsample)
library(stars)
library(raster)
library(reticulate)
library(mapview)

##################################################
# Building the Model
#################################################

first_model <- keras_model_sequential()
layer_conv_2d(first_model, filters = 32, kernel_size = 3, activation = "relu", input_shape = c(128,128,3))
layer_max_pooling_2d(first_model, pool_size = c(2,2))
layer_conv_2d(first_model, filters = 64, kernel_size = c(3,3), activation = "relu") 
layer_max_pooling_2d(first_model, pool_size = c(2,2))
layer_conv_2d(first_model, filters = 128, kernel_size = c(3,3), activation = "relu")
layer_max_pooling_2d(first_model, pool_size = c(2,2)) 
layer_conv_2d(first_model, filters = 128, kernel_size = c(3,3), activation = "relu")
layer_max_pooling_2d(first_model, pool_size = c(2,2)) 
layer_flatten(first_model) 
layer_dense(first_model, units = 256, activation = "relu") 
layer_dense(first_model, units = 1, activation = "sigmoid")

#The model
summary(first_model)

##################################################
# Preparing the data
#################################################

#Getting all file paths containing our targets
subset_list <- list.files("./training/true", full.names = T)

#Create a dataframe with two columns: file paths and labels (1)
data_true <- data.frame(image=subset_list, lbl=rep(1L, length(subset_list)))

#Getting all file paths containing non-targets
subset_list <- list.files("./training/false", full.names = T)

#Create a dataframe with two columns: file paths and labels (1)
data_false <- data.frame(image=subset_list, lbl=rep(0L, length(subset_list)))

#Merge the two dataframes
data <- rbind(data_true, data_false)

#Randonly split data into 75 percent training and 25 percent testing. The split should be done proportional of the two categories

set.seed(2020)
data <- initial_split(data, prop = 0.75, strata = "lbl")

## Looking at the data
data
head(training(data))
c(nrow(training(data)[training(data)$lbl==0,]), nrow(training(data)[training(data)$lbl==1,])) #Check equal split #That is 0's and 1's

##########################################################
#       WINDOWS FATAL ERROR                             ##          
#           OCCUR WITH THE NEXT FUNCTION                ##
##########################################################

training_dataset <- tensor_slices_dataset(training(data)) 

## A List of all tensors
dataset_iterator <- as_iterator(training_dataset)
dataset_list <- iterate(dataset_iterator)
head(dataset_list)

#Get shape of the first model
subset_size <- first_model$input_shape[2:3]

##########################################################
#       MACBOOK ERROR                                    ##          
#           OCCUR WITH THE NEXT FUNCTION                ##
##########################################################

##  1 Convert the images to a float
training_dataset <-
  dataset_map(training_dataset, function(.x)
    list_modify(.x, img = tf$image$decode_jpeg(tf$io$read_file(.x$img))))

# 2 Convert data type
training_dataset<-
  dataset_map(training_dataset, function(.x)
    list_modify(.x, img = tf$image$convert_image_dtype(.x$img, dtype = tf$float32)))

# Resize to the size expected by the model
training_dataset<-
  dataset_map(training_dataset, function(.x)
    list_modify(.x, img = tf$image$resize(.x$img, size = shape(subset_size[1], subset_size[1],
                                                               subset_size[2]))))
## The data has 0's then targets. Suffle the data
training_dataset<-dataset_shuffle(training_dataset, buffer_size = 10L*128)
#Create batches for data processing
training_dataset<-dataset_batch(training_dataset, 10L)

training_dataset<-dataset_map(training_dataset, unname)