library(keras)
library(kextra)
cifar10 <- dataset_cifar10()
cifar10$train$y <- to_categorical(cifar10$train$y)
cifar10$test$y <- to_categorical(cifar10$test$y)
x <- cifar10$train$x
y <- cifar10$train$y
input <- layer_input(c(32, 32, 3))
output <- input %>%
layer_image_resize(size = c(20, 20)) %>%
layer_image_rgb_to_grayscale() %>%
layer_conv_2d(kernel_size = c(2,2), filters = 32, padding = "same") %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_conv_2d(filters = 32, kernel_size = c(2,2)) %>%
layer_max_pooling_2d() %>%
layer_flatten() %>%
layer_dense(10)
model <- keras_model(input, output)
model %>%
compile(
loss = "categorical_crossentropy",
optimizer = "adam",
metrics = "accuracy"
)
model %>%
fit(
x = x, y = y,
validation_split = 0.1
)
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