base_dir <- "/home/shiga/R_data/cat_and_dog/small_set"
train_dir <- file.path(base_dir, "train")
validation_dir <- file.path(base_dir, "validation")
test_dir <- file.path(base_dir, "test")
library(keras)
model <- keras_model_sequential() %>%
layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
input_shape = c(150, 150, 3)) %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_flatten() %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 512, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
model %>% compile(
loss = "binary_crossentropy",
optimizer = optimizer_rmsprop(lr = 1e-4),
metrics = c("acc")
)
datagen <- image_data_generator(
rescale = 1/255,
rotation_range = 40,
width_shift_range = 0.2,
height_shift_range = 0.2,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = TRUE
)
test_datagen <- image_data_generator(rescale = 1/255)
train_generator <- flow_images_from_directory(
train_dir,
datagen,
target_size = c(150, 150),
batch_size = 32,
class_mode = "binary"
)
validation_generator <- flow_images_from_directory(
validation_dir,
test_datagen,
target_size = c(150, 150),
batch_size = 32,
class_mode = "binary"
)
history <- model %>% fit_generator(
train_generator,
steps_per_epoch = 100,
epochs = 100,
validation_data = validation_generator,
validation_steps = 50
)
###
img_path <- "/home/shiga/R_data/cat_and_dog/small_set/test/cat/cat.1700.jpg"
img <- image_load(img_path, target_size = c(150, 150))
img_tensor <- image_to_array(img)
img_tensor <- array_reshape(img_tensor, c(1, 150, 150, 3))
img_tensor <- img_tensor / 255
plot(as.raster(img_tensor[1,,,]))
layer_outputs <- lapply(model$layers[1:8], function(layer) layer$output)
# Creates a model that will return these outputs, given the model input:
activation_model <- keras_model(inputs = model$input, outputs = layer_outputs)
activations <- activation_model %>% predict(img_tensor)
first_layer_activation <- activations[[1]]
dim(first_layer_activation)
plot_channel <- function(channel) {
rotate <- function(x) t(apply(x, 2, rev))
image(rotate(channel), axes = FALSE, asp = 1,
col = terrain.colors(12))
}
plot_channel(first_layer_activation[1,,,5])
fifth_layer_activation <- activations[[5]]
plot_channel(fifth_layer_activation[1,,,1])
plot_channel(fifth_layer_activation[1,,,3])
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