Using Pre-Trained Models

# instantiate the model
model <- application_resnet50(weights = 'imagenet')

# load the image
img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)

# ensure we have a 4d tensor with single element in the batch dimension,
# the preprocess the input for prediction using resnet50
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)

# make predictions then decode and print them
preds <- model %>% predict(x)
imagenet_decode_predictions(preds, top = 3)[[1]]

model <- application_vgg16(weights = 'imagenet', include_top = FALSE)

img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)

features <- model %>% predict(x)

base_model <- application_vgg19(weights = 'imagenet')
model <- keras_model(inputs = base_model$input, 
                     outputs = get_layer(base_model, 'block4_pool')$output)

img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)

block4_pool_features <- model %>% predict(x)

# 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
freeze_weights(base_model)

# 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:
freeze_weights(base_model, from = 1, to = 172)
unfreeze_weights(base_model, from = 173)

# 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(...)

# this could also be the output a different Keras model or layer
input_tensor <- layer_input(shape = c(224, 224, 3))

model <- application_inception_V3(input_tensor = input_tensor, 
                                  weights='imagenet', 
                                  include_top = TRUE)