Import image directories for Keras and Tensorflow

[TBanks]

I have datasets of images in several folders ConvNet/train_set/cats, ConvNet/train_set/dogs, ConvNet/test_set/cats and ConvNet/test_set/dogs. I need to read these in and run them through a CNN code using Keras and Tensorflow. I have the CNN built, but I'm having issues reading in the image directories. Can someone help?

[cderv]

Hello,

Can you provide a reprex to help us know what you already tried ?

You could be interested in this example
https://tensorflow.rstudio.com/tutorials/beginners/load/load_image/

[TBanks]

library(tidyverse)
library(tensorflow)
#> Warning: package 'tensorflow' was built under R version 3.6.3
library(keras)
#> Warning: package 'keras' was built under R version 3.6.3
library(tfdatasets)
#> Warning: package 'tfdatasets' was built under R version 3.6.3
library(readr)



data_dir <- unzip("C:/Users/tniebank/OneDrive - Cox Automotive/Documents/School/MachineLearning/ConvNet_dataset.zip")


images <- list.files(data_dir, pattern = ".jpg", recursive = TRUE)
length(images)
#> [1] 0

classes <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)
classes
#> character(0)


par(mfcol = c(5,6), mar = rep(1, 4), oma = rep(0.2, 4))
conv$train_set$x[index,,,] %>% 
  purrr::array_tree(1) %>%
  purrr::set_names(class_names[conv$train_set$y[index] + 1]) %>% 
  purrr::map(as.raster, max = 255) %>%
  purrr::iwalk(~{plot(.x); title(.y)})
#> Error in eval(lhs, parent, parent): object 'conv' not found

model <- keras_model_sequential() %>% 
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = "relu", 
                input_shape = c(64,64,3)) %>% 
  layer_max_pooling_2d(pool_size = c(2,2)) %>% 
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2,2)) %>% 
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = "relu")

summary(model)
#> Model: "sequential"
#> ________________________________________________________________________________
#> Layer (type)                        Output Shape                    Param #     
#> ================================================================================
#> conv2d (Conv2D)                     (None, 62, 62, 32)              896         
#> ________________________________________________________________________________
#> max_pooling2d (MaxPooling2D)        (None, 31, 31, 32)              0           
#> ________________________________________________________________________________
#> conv2d_1 (Conv2D)                   (None, 29, 29, 32)              9248        
#> ________________________________________________________________________________
#> max_pooling2d_1 (MaxPooling2D)      (None, 14, 14, 32)              0           
#> ________________________________________________________________________________
#> conv2d_2 (Conv2D)                   (None, 12, 12, 32)              9248        
#> ================================================================================
#> Total params: 19,392
#> Trainable params: 19,392
#> Non-trainable params: 0
#> ________________________________________________________________________________

model %>% 
  layer_flatten() %>% 
  layer_dense(units = 128, activation = "relu") %>% 
  layer_dense(units = 1, activation = "sigmoid")

summary(model)
#> Model: "sequential"
#> ________________________________________________________________________________
#> Layer (type)                        Output Shape                    Param #     
#> ================================================================================
#> conv2d (Conv2D)                     (None, 62, 62, 32)              896         
#> ________________________________________________________________________________
#> max_pooling2d (MaxPooling2D)        (None, 31, 31, 32)              0           
#> ________________________________________________________________________________
#> conv2d_1 (Conv2D)                   (None, 29, 29, 32)              9248        
#> ________________________________________________________________________________
#> max_pooling2d_1 (MaxPooling2D)      (None, 14, 14, 32)              0           
#> ________________________________________________________________________________
#> conv2d_2 (Conv2D)                   (None, 12, 12, 32)              9248        
#> ________________________________________________________________________________
#> flatten (Flatten)                   (None, 4608)                    0           
#> ________________________________________________________________________________
#> dense (Dense)                       (None, 128)                     589952      
#> ________________________________________________________________________________
#> dense_1 (Dense)                     (None, 1)                       129         
#> ================================================================================
#> Total params: 609,473
#> Trainable params: 609,473
#> Non-trainable params: 0
#> ________________________________________________________________________________

model %>% compile(
  optimizer = "adam",
  loss = "sparse_categorical_crossentropy",
  metrics = "accuracy"
)

history <- model %>% 
  fit(
    x = conv$train_set$x, y = conv$train_set$y,
    epochs = 25, batch_size = 8000,
    validation_data = unname(conv$test_set),
    verbose = 2
  )
#> Error in unname(conv$test_set): object 'conv' not found

plot(history)
#> Error in .External2(C_savehistory, file): 'savehistory' can only be used in Rgui and Rterm

evaluate(model, conv$test_set$x, conv$test_set$y, verbose = 0)
#> Error in is_tensorflow_dataset(x): object 'conv' not found

^{Created on 2020-03-30 by the reprex package (v0.3.0)}