# create the base pre-trained model
base_model <- application_inception_v3(weights = 'imagenet', include_top = FALSE)
 
# add our custom layers
predictions <- base_model$output %>% 
  layer_global_average_pooling_2d() %>% 
  layer_dense(units = 1024, activation = 'relu') %>% 
  layer_dense(units = 200, activation = 'softmax')
 
# this is the model we will train
model <- keras_model(inputs = base_model$input, outputs = predictions)
 
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for (layer in base_model$layers)
  layer$trainable <- FALSE
 
# compile the model (should be done *after* setting layers to non-trainable)
model %>% compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy')
 
# train the model on the new data for a few epochs
model %>% fit_generator(...)
 
# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.
 
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
layers <- base_model$layers
for (i in 1:length(layers))
  cat(i, layers[[i]]$name, "\n")
 
# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 172 layers and unfreeze the rest:
for (i in 1:172)
  layers[[i]]$trainable <- FALSE
for (i in 173:length(layers))
  layers[[i]]$trainable <- TRUE
 
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
model %>% compile(
  optimizer = optimizer_sgd(lr = 0.0001, momentum = 0.9), 
  loss = 'categorical_crossentropy'
)
 
# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model %>% fit_generator(...)

require(keras)
### Keras transfer learning example
 
################### Section 1 #########################
img_width <- 32
img_height <- 32
batch_size <- 8
 
train_directory <- "flickrData/train"
test_directory <- "flickrData/test"
 
train_generator <- flow_images_from_directory(train_directory, generator = image_data_generator(),
                                              target_size = c(img_width, img_height), color_mode = "rgb",
                                              class_mode = "categorical", batch_size = batch_size, shuffle = TRUE,
                                              seed = 123)
 
validation_generator <- flow_images_from_directory(test_directory, generator = image_data_generator(),
                                                   target_size = c(img_width, img_height), color_mode = "rgb", classes = NULL,
                                                   class_mode = "categorical", batch_size = batch_size, shuffle = TRUE,
                                                   seed = 123)
train_samples = 498
validation_samples = 177
 
################### Section 2 #########################
#base_model <- application_inception_v3(weights = 'imagenet', include_top = FALSE)
base_model <- application_vgg16(weights = 'imagenet', include_top = FALSE)
### use vgg16 -  as inception won't converge --- 
 
################### Section 3 #########################
## add your custom layers
predictions <- base_model$output %>% 
  layer_global_average_pooling_2d(trainable = T) %>% 
  layer_dense(64, trainable = T) %>%
  layer_activation("relu", trainable = T) %>%
  layer_dropout(0.4, trainable = T) %>%
  layer_dense(27, trainable=T) %>%    ## important to adapt to fit the 27 classes in the dataset!
  layer_activation("softmax", trainable=T)
# this is the model we will train
model <- keras_model(inputs = base_model$input, outputs = predictions)
 
################### Section 4 #########################
for (layer in base_model$layers)
  layer$trainable <- FALSE
 
################### Section 5 #########################
model %>% compile(
  loss = "categorical_crossentropy",
  optimizer = optimizer_rmsprop(lr = 0.003, decay = 1e-6),  ## play with the learning rate
  metrics = "accuracy"
)
 
hist <- model %>% fit_generator(
  train_generator,
  steps_per_epoch = as.integer(train_samples/batch_size), 
  epochs = 20, 
  validation_data = validation_generator,
  validation_steps = as.integer(validation_samples/batch_size),
  verbose=2
)
 
### saveable data frame obejct.
histDF <- data.frame(acc = unlist(hist$history$acc), val_acc=unlist(hist$history$val_acc), val_loss = unlist(hist$history$val_loss),loss = unlist(hist$history$loss))