Update with TF 2.0: Image classification with Keras and TensorFlow
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Recently, I have been getting a few comments on my old article on image classification with Keras, saying that they are getting errors with the code. And I have also gotten a few questions about how to use a Keras model to predict on new images (of different size). Instead of replying to them all individually, I decided to write this updated version using recent Keras and TensorFlow versions (all package versions and system information can be found at the bottom of this article, as usual).
Specific changes compared to the old article:
using TF 2.2.0
using Keras 2.3.1
used validation split instead of a validation set
used folder Validation/Test for showing how to predict on new images
Workshop announcement: Because this year’s UseR 2020 in Munich couldn’t happen as an in-person event, I will be giving my workshop on Deep Learning with Keras and TensorFlow as an online event. I’ll be announcing the date once it’s been finalized but if you are interested in participating: we are looking at the first week of October (13:00 UTC).
If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free – one slot available per weekday):
If you have been enjoying my content and would like to help me be able to create more, please consider sending me a donation at . Thank you! 🙂
Note:
I have been having an issue with blogdown, where the code below runs perfectly fine in my R console from within the RMarkdown file but whenever I include any function from the keras package, blogdown::serve_site() throws an error: Error in render_page(f) : Failed to render 'content/post/keras.Rmd'.
Since I have no idea what’s going on there (blogdown::serve_site() builds my site without trouble as long as I don’t include keras functions) and haven’t gotten a reply to the issue I posted to Github, I had to write this blogpost without actually running the code while building the site.
I worked around that problem by not evaluating most of the code below and instead ran the code locally on my computer. Objects and images, I then saved as .RData or .png files and manually put them back into the document. By just reading this blogpost as it rendered on my site, you won’t notice much of a difference (except where I copy/pasted the output as comments below the code) but in order to be fully transparent, you can see on Github what I did with hidden code chunks to create this post.
Loading libraries
# load libraries library(tidyverse) library(tensorflow) library(keras) tf$random$set_seed(42) # check TF version tf_version() #[1] ‘2.2’ # check if keras is available is_keras_available() #[1] TRUE
Loading images (data)
The dataset I am using here is the fruit images dataset from Kaggle. I downloaded it to my computer and unpacked it. Because I don’t want to build a model for all the different fruits, I define a list of fruits (corresponding to the folder names) that I want to include in the model.
# path to image folders
train_image_files_path <- "/fruits/Training/"
# list of fruits to modle
fruit_list <- c("Kiwi", "Banana", "Apricot", "Avocado", "Cocos", "Clementine", "Mandarine", "Orange",
"Limes", "Lemon", "Peach", "Plum", "Raspberry", "Strawberry", "Pineapple", "Pomegranate")
# number of output classes (i.e. fruits)
output_n <- length(fruit_list)
# image size to scale down to (original images are 100 x 100 px)
img_width <- 20
img_height <- 20
target_size <- c(img_width, img_height)
# RGB = 3 channels
channels <- 3
# define batch size
batch_size <- 32
The handy image_data_generator() and flow_images_from_directory() functions can be used to load images from a directory without having to store all data in memory at the same time. Instead image_data_generator will loop over the data and process the images in batches.
When we train our model, we want to have a way to judge how well it learned and if learning improves over the epochs. Therefore, we want to use validation data, to make these performance measures less biased compared to using the training data only. In Keras, we can either give a specific validation set (as I did in the old article on image classification with Keras) or we define a validation split.
If you want to use data augmentation, you can directly define how and in what way you want to augment your images with image_data_generator. Here I am not augmenting the data, I only scale the pixel values to fall between 0 and 1.
train_data_gen <- image_data_generator( rescale = 1/255, validation_split = 0.3)
Now we load the images into memory and resize them.
# training images
train_image_array_gen <- flow_images_from_directory(train_image_files_path,
train_data_gen,
subset = 'training',
target_size = target_size,
class_mode = "categorical",
classes = fruit_list,
batch_size = batch_size,
seed = 42)
#Found 5401 images belonging to 16 classes.
# validation images
valid_image_array_gen <- flow_images_from_directory(train_image_files_path,
train_data_gen,
subset = 'validation',
target_size = target_size,
class_mode = "categorical",
classes = fruit_list,
batch_size = batch_size,
seed = 42)
#Found 2308 images belonging to 16 classes.
cat("Number of images per class:")
table(factor(train_image_array_gen$classes))
#Number of images per class:
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
#327 343 345 299 343 343 343 336 343 345 345 313 343 345 343 345
Note, that even though Keras’ image_data_generator recognizes folder names as class labels for classification tasks, these labels will be converted into indices (alphabetical starting from 0) for training and prediction later. Thus, it is useful to create a library object that matches these indices back to human-interpretable labels.
train_image_array_gen_t <- train_image_array_gen$class_indices %>%
as.tibble()
cat("\nClass label vs index mapping:\n")
##
## Class label vs index mapping:
train_image_array_gen_t
## # A tibble: 1 x 16
## Kiwi Banana Apricot Avocado Cocos Clementine Mandarine Orange Limes Lemon
## <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 0 1 2 3 4 5 6 7 8 9
## # … with 6 more variables: Peach <int>, Plum <int>, Raspberry <int>,
## # Strawberry <int>, Pineapple <int>, Pomegranate <int>
Training the model
Now, I define and train the model just as before:
# number of training samples
train_samples <- train_image_array_gen$n
# number of validation samples
valid_samples <- valid_image_array_gen$n
# define number of epochs
epochs <- 10
# initialise model
model <- keras_model_sequential()
# add layers
model %>%
layer_conv_2d(filter = 32, kernel_size = c(3,3), padding = "same", input_shape = c(img_width, img_height, channels)) %>%
layer_activation("relu") %>%
# Second hidden layer
layer_conv_2d(filter = 16, kernel_size = c(3,3), padding = "same") %>%
layer_activation_leaky_relu(0.5) %>%
layer_batch_normalization() %>%
# Use max pooling
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_dropout(0.25) %>%
# Flatten max filtered output into feature vector
# and feed into dense layer
layer_flatten() %>%
layer_dense(100) %>%
layer_activation("relu") %>%
layer_dropout(0.5) %>%
# Outputs from dense layer are projected onto output layer
layer_dense(output_n) %>%
layer_activation("softmax")
# compile
model %>% compile(
loss = "categorical_crossentropy",
optimizer = optimizer_rmsprop(lr = 0.0001, decay = 1e-6),
metrics = "accuracy"
)
Fitting the model: because I used image_data_generator() and flow_images_from_directory() I am now also using the fit_generator() to run the training.
Note: In future releases of TensorFlow, generator functions will be deprecated (i.e. fit_generator(), evaluate_generator() & predict_generator()) and as of TensorFlow 2.1.0 the regular functions (fit(), evaluate() & predict()) handle generators directly. However, this does not seem to be implemented in the Keras (R-) package yet (see ?fit.keras.engine.training.Model), so I’ll be sticking to the old way of doing things for now.
# fit hist <- model %>% fit_generator( # training data train_image_array_gen, # epochs steps_per_epoch = as.integer(train_samples / batch_size), epochs = epochs, # validation data validation_data = valid_image_array_gen, validation_steps = as.integer(valid_samples / batch_size) ) Epoch 1/10 168/168 [==============================] - 8s 51ms/step - loss: 1.8614 - accuracy: 0.4194 - val_loss: 2.1271 - val_accuracy: 0.6897 Epoch 2/10 168/168 [==============================] - 6s 37ms/step - loss: 0.7967 - accuracy: 0.7463 - val_loss: 0.9940 - val_accuracy: 0.9444 Epoch 3/10 168/168 [==============================] - 6s 34ms/step - loss: 0.4556 - accuracy: 0.8622 - val_loss: 0.2636 - val_accuracy: 0.9722 Epoch 4/10 168/168 [==============================] - 6s 37ms/step - loss: 0.3001 - accuracy: 0.9074 - val_loss: 0.1298 - val_accuracy: 0.9779 Epoch 5/10 168/168 [==============================] - 6s 35ms/step - loss: 0.2010 - accuracy: 0.9417 - val_loss: 0.1194 - val_accuracy: 0.9757 Epoch 6/10 168/168 [==============================] - 6s 34ms/step - loss: 0.1539 - accuracy: 0.9581 - val_loss: 0.0768 - val_accuracy: 0.9688 Epoch 7/10 168/168 [==============================] - 6s 36ms/step - loss: 0.1147 - accuracy: 0.9672 - val_loss: 0.0574 - val_accuracy: 0.9831 Epoch 8/10 168/168 [==============================] - 6s 34ms/step - loss: 0.0831 - accuracy: 0.9771 - val_loss: 0.0676 - val_accuracy: 0.9826 Epoch 9/10 168/168 [==============================] - 6s 35ms/step - loss: 0.0713 - accuracy: 0.9786 - val_loss: 0.0542 - val_accuracy: 0.9796 Epoch 10/10 168/168 [==============================] - 6s 35ms/step - loss: 0.0572 - accuracy: 0.9842 - val_loss: 0.0548 - val_accuracy: 0.9835
In RStudio we are seeing the output as an interactive plot in the “Viewer” pane but we can also plot it:
plot(hist)
model %>% save_model_hdf5("my_model.h5")
model <- load_model_hdf5("my_model.h5")
Predicting on “new” set of images
Here is what’s new: how to use the model we just trained for predicting on new images. Alternatively, load a previously trained model and use that for predictions. Just keep in mind, that these “new” images here aren’t actually new. They come from the same data source, i.e. the same distribution and thus aren’t independent enough to give an accurate assessment of how well the model generalizes.
The easiest and most efficient way to predict on a set of new images (and resize them to the target width and height), is to use the image data generator just as we did for reading in the training data above. Of course, you will need to know how the training images were preprocessed, so that you can apply the same steps to your test images. Here, the image pixel values were scaled to fall between 0 and 1 and the width/height were set to 20 pixels.
# path to image folders
test_image_files_path <- "/fruits/Test/"
test_datagen <- image_data_generator(rescale = 1/255)
test_generator <- flow_images_from_directory(
test_image_files_path,
test_datagen,
target_size = target_size,
class_mode = "categorical",
classes = fruit_list,
batch_size = 1,
shuffle = FALSE,
seed = 42)
#Found 2592 images belonging to 16 classes.
Next, we can use the image data generator we created for our test images on two functions. First, I’ll run evaluate_generator, to get an overall idea of how well the model predicted the test images (note, that this requires for us to know the correct predictions for our images):
model %>%
evaluate_generator(test_generator,
steps = as.integer(test_generator$n))
loss accuracy
0.02915302 0.99305558
Usually, what’s wanted is to get individual predictions for each test image. For that, we use the predict_generator() function. This function will return a matrix:
- each row represents one test image
- columns gives prediction probabilities for all possibles classes (if you are running a classifier)
In order to compare predictions with actual class labels, I am preparing the true labels so that they can be merged with the predictions:
classes <- test_generator$classes %>% factor() %>% table() %>% as.tibble() colnames(classes)[1] <- "value" # create library of indices & class labels indices <- test_generator$class_indices %>% as.data.frame() %>% gather() %>% mutate(value = as.character(value)) %>% left_join(classes, by = "value")
Now, I’m running the predictions, rename the columns to show the class labels instead of indices and I am merging the true labels, together with their corresponding labels and the total number of images in each class.
(Running test_generator$reset() isn’t strictly necessary, but sometimes strange things happen if you are using the image data generator several times, so I always run reset first, to be on the safe side.)
# predict on test data
test_generator$reset()
predictions <- model %>%
predict_generator(
generator = test_generator,
steps = as.integer(test_generator$n)
) %>%
round(digits = 2) %>%
as.tibble()
colnames(predictions) <- indices$key
predictions <- predictions %>%
mutate(truth_idx = as.character(test_generator$classes)) %>%
left_join(indices, by = c("truth_idx" = "value"))
If you want to know which class is predicted for each image, you need to filter for the class with the highest prediction probability (here, tidyverse-style). Afterwards, for each class, I am also counting how many images were predicted with which label.
pred_analysis <- predictions %>% mutate(img_id = seq(1:test_generator$n)) %>% gather(pred_lbl, y, Kiwi:Pomegranate) %>% group_by(img_id) %>% filter(y == max(y)) %>% arrange(img_id) %>% group_by(key, n, pred_lbl) %>% count()
And since plotting makes comparing much easier than looking at a table, I’m creating a tile plot that shows the percentages of test images predicted for each label:
p <- pred_analysis %>%
mutate(percentage_pred = nn / n * 100) %>%
ggplot(aes(x = key, y = pred_lbl,
fill = percentage_pred,
label = round(percentage_pred, 2))) +
geom_tile() +
scale_fill_continuous() +
scale_fill_gradient(low = "blue", high = "red") +
geom_text(color = "white") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
labs(x = "True class",
y = "Predicted class",
fill = "Percentage\nof predictions",
title = "True v. predicted class labels",
subtitle = "Percentage of test images predicted for each label",
caption = "For every class of test images, this figure shows the percentage of images with predicted labels for each possible label.
E.g.: 100% of test images in the class 'Apricot' were predicted correctly. Of test images from the class 'Cocos'
only 94.58% were predicted correctly, while 0.6% of these images were predicted to show a Strawberry and 4.82% a Pineapple.")
p
And another tile plot showing the percentages of correct and falsely predicted images in each class:
p2 <- pred_analysis %>%
mutate(prediction = case_when(
key == pred_lbl ~ "correct",
TRUE ~ "false"
)) %>%
group_by(key, prediction, n) %>%
summarise(sum = sum(nn)) %>%
mutate(percentage_pred = sum / n * 100) %>%
ggplot(aes(x = key, y = prediction,
fill = percentage_pred,
label = round(percentage_pred, 2))) +
geom_tile() +
scale_fill_continuous() +
geom_text(color = "white") +
coord_flip() +
scale_fill_gradient(low = "blue", high = "red") +
labs(x = "True class",
y = "Prediction is...",
fill = "Percentage\nof predictions",
title = "Percentage of correct v false predictions",
subtitle = "Percentage of test image classes predicted correctly v. falsely",
caption = "For every class of test images, this figure shows the percentage of
images with correctly and falsely predicted labels. E.g.: 100% of test images
in the class 'Apricot' were predicted correctly. Of test images from the class
'Cocos' only 94.58% were predicted correctly, while 5.42% were predicted falsely.")
p2
As we can see, the model is quite accurate on the test data. However, we need to keep in mind that our images are very uniform, they all have the same white background and show the fruits centered and without anything else in the images. Thus, our model will not work with images that don’t look similar as the ones we trained on (that’s also why we can achieve such good results with such a small neural net).
devtools::session_info() ## ─ Session info ─────────────────────────────────────────────────────────────── ## setting value ## version R version 4.0.2 (2020-06-22) ## os macOS Catalina 10.15.6 ## system x86_64, darwin17.0 ## ui X11 ## language (EN) ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Europe/Berlin ## date 2020-09-14 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version date lib source ## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0) ## backports 1.1.9 2020-08-24 [1] CRAN (R 4.0.2) ## base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.0.0) ## blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.2) ## blogdown 0.20.1 2020-09-09 [1] Github (rstudio/blogdown@d96fe78) ## bookdown 0.20 2020-06-23 [1] CRAN (R 4.0.2) ## broom 0.7.0 2020-07-09 [1] CRAN (R 4.0.2) ## callr 3.4.4 2020-09-07 [1] CRAN (R 4.0.2) ## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.0) ## cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.0) ## colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.0) ## crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0) ## DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.0) ## dbplyr 1.4.4 2020-05-27 [1] CRAN (R 4.0.2) ## desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0) ## devtools 2.3.1 2020-07-21 [1] CRAN (R 4.0.2) ## digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.0) ## dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.2) ## ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.0) ## evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.1) ## fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0) ## forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.0) ## fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2) ## generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.0) ## ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.2) ## glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2) ## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0) ## haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.2) ## hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.0) ## htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2) ## httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2) ## jsonlite 1.7.1 2020-09-07 [1] CRAN (R 4.0.2) ## keras * 2.3.0.0 2020-05-19 [1] CRAN (R 4.0.2) ## knitr 1.29 2020-06-23 [1] CRAN (R 4.0.2) ## lattice 0.20-41 2020-04-02 [1] CRAN (R 4.0.2) ## lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0) ## lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.2) ## magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.0) ## Matrix 1.2-18 2019-11-27 [1] CRAN (R 4.0.2) ## memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.0) ## modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.2) ## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.0) ## pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.2) ## pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2) ## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0) ## pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2) ## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.0) ## processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.2) ## ps 1.3.4 2020-08-11 [1] CRAN (R 4.0.2) ## purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.0) ## R6 2.4.1 2019-11-12 [1] CRAN (R 4.0.0) ## Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2) ## readr * 1.3.1 2018-12-21 [1] CRAN (R 4.0.0) ## readxl 1.3.1 2019-03-13 [1] CRAN (R 4.0.0) ## remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2) ## reprex 0.3.0 2019-05-16 [1] CRAN (R 4.0.0) ## reticulate 1.16 2020-05-27 [1] CRAN (R 4.0.2) ## rlang 0.4.7 2020-07-09 [1] CRAN (R 4.0.2) ## rmarkdown 2.3 2020-06-18 [1] CRAN (R 4.0.2) ## rprojroot 1.3-2 2018-01-03 [1] CRAN (R 4.0.0) ## rstudioapi 0.11 2020-02-07 [1] CRAN (R 4.0.0) ## rvest 0.3.6 2020-07-25 [1] CRAN (R 4.0.2) ## scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.0) ## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.0) ## stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2) ## stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.0) ## tensorflow * 2.2.0 2020-05-11 [1] CRAN (R 4.0.0) ## testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.0) ## tfruns 1.4 2018-08-25 [1] CRAN (R 4.0.0) ## tibble * 3.0.3 2020-07-10 [1] CRAN (R 4.0.2) ## tidyr * 1.1.2 2020-08-27 [1] CRAN (R 4.0.2) ## tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.0) ## tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 4.0.0) ## usethis 1.6.1 2020-04-29 [1] CRAN (R 4.0.0) ## utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.0) ## vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.2) ## whisker 0.4 2019-08-28 [1] CRAN (R 4.0.0) ## withr 2.2.0 2020-04-20 [1] CRAN (R 4.0.0) ## xfun 0.17 2020-09-09 [1] CRAN (R 4.0.2) ## xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.0) ## yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0) ## zeallot 0.1.0 2018-01-28 [1] CRAN (R 4.0.0) ## ## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.