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#' This is the companion code to the post | ||
#' "Generating digits with Keras and TensorFlow eager execution" | ||
#' on the TensorFlow for R blog. | ||
#' | ||
#' https://blogs.rstudio.com/tensorflow/posts/2018-08-26-eager-dcgan/ | ||
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# Setup ------------------------------------------------------------------- | ||
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# Important: Make sure you are using the latest versions of reticulate, keras, tensorflow and tfdatasets from github. | ||
# devtools::install_github( | ||
# c( | ||
# "rstudio/keras", | ||
# "rstudio/tensorflow", | ||
# "rstudio/tfdatasets", | ||
# "rstudio/reticulate" | ||
# ) | ||
# ) | ||
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library(keras) | ||
use_implementation("tensorflow") | ||
library(tensorflow) | ||
tfe_enable_eager_execution(device_policy = "silent") | ||
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library(tfdatasets) | ||
tf$set_random_seed(7777) | ||
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mnist <- dataset_mnist() | ||
c(train_images, train_labels) %<-% mnist$train | ||
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train_images <- train_images %>% | ||
k_expand_dims() %>% | ||
k_cast(dtype = "float32") | ||
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train_images <- (train_images - 127.5) / 127.5 | ||
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buffer_size <- 60000 | ||
batch_size <- 256L | ||
batches_per_epoch <- (buffer_size / batch_size) %>% round() | ||
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train_dataset <- tensor_slices_dataset(train_images) %>% | ||
dataset_shuffle(buffer_size) %>% | ||
dataset_batch(batch_size) | ||
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generator <- | ||
function(name = NULL) { | ||
keras_model_custom(name = name, function(self) { | ||
self$fc1 <- layer_dense(units = 7 * 7 * 64, use_bias = FALSE) | ||
self$batchnorm1 <- layer_batch_normalization() | ||
self$leaky_relu1 <- layer_activation_leaky_relu() | ||
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self$conv1 <- | ||
layer_conv_2d_transpose( | ||
filters = 64, | ||
kernel_size = c(5, 5), | ||
strides = c(1, 1), | ||
padding = "same", | ||
use_bias = FALSE | ||
) | ||
self$batchnorm2 <- layer_batch_normalization() | ||
self$leaky_relu2 <- layer_activation_leaky_relu() | ||
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self$conv2 <- | ||
layer_conv_2d_transpose( | ||
filters = 32, | ||
kernel_size = c(5, 5), | ||
strides = c(2, 2), | ||
padding = "same", | ||
use_bias = FALSE | ||
) | ||
self$batchnorm3 <- layer_batch_normalization() | ||
self$leaky_relu3 <- layer_activation_leaky_relu() | ||
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self$conv3 <- | ||
layer_conv_2d_transpose( | ||
filters = 1, | ||
kernel_size = c(5, 5), | ||
strides = c(2, 2), | ||
padding = "same", | ||
use_bias = FALSE, | ||
activation = "tanh" | ||
) | ||
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function(inputs, | ||
mask = NULL, | ||
training = TRUE) { | ||
self$fc1(inputs) %>% | ||
self$batchnorm1(training = training) %>% | ||
self$leaky_relu1() %>% | ||
k_reshape(shape = c(-1, 7, 7, 64)) %>% | ||
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self$conv1() %>% | ||
self$batchnorm2(training = training) %>% | ||
self$leaky_relu2() %>% | ||
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self$conv2() %>% | ||
self$batchnorm3(training = training) %>% | ||
self$leaky_relu3() %>% | ||
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self$conv3() | ||
} | ||
}) | ||
} | ||
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discriminator <- | ||
function(name = NULL) { | ||
keras_model_custom(name = name, function(self) { | ||
self$conv1 <- layer_conv_2d( | ||
filters = 64, | ||
kernel_size = c(5, 5), | ||
strides = c(2, 2), | ||
padding = "same" | ||
) | ||
self$leaky_relu1 <- layer_activation_leaky_relu() | ||
self$dropout <- layer_dropout(rate = 0.3) | ||
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self$conv2 <- | ||
layer_conv_2d( | ||
filters = 128, | ||
kernel_size = c(5, 5), | ||
strides = c(2, 2), | ||
padding = "same" | ||
) | ||
self$leaky_relu2 <- layer_activation_leaky_relu() | ||
self$flatten <- layer_flatten() | ||
# no sigmoid because using tf$losses$sigmoid_cross_entropy | ||
self$fc1 <- layer_dense(units = 1) | ||
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function(inputs, | ||
mask = NULL, | ||
training = TRUE) { | ||
inputs %>% self$conv1() %>% | ||
self$leaky_relu1() %>% | ||
self$dropout(training = training) %>% | ||
self$conv2() %>% | ||
self$leaky_relu2() %>% | ||
self$flatten() %>% | ||
self$fc1() | ||
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} | ||
}) | ||
} | ||
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generator <- generator() | ||
discriminator <- discriminator() | ||
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# https://www.tensorflow.org/api_docs/python/tf/contrib/eager/defun | ||
generator$call = tf$contrib$eager$defun(generator$call) | ||
discriminator$call = tf$contrib$eager$defun(discriminator$call) | ||
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discriminator_loss <- function(real_output, generated_output) { | ||
real_loss <- | ||
tf$losses$sigmoid_cross_entropy(multi_class_labels = k_ones_like(real_output), | ||
logits = real_output) | ||
generated_loss <- | ||
tf$losses$sigmoid_cross_entropy(multi_class_labels = k_zeros_like(generated_output), | ||
logits = generated_output) | ||
real_loss + generated_loss | ||
} | ||
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generator_loss <- function(generated_output) { | ||
tf$losses$sigmoid_cross_entropy(tf$ones_like(generated_output), generated_output) | ||
} | ||
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discriminator_optimizer <- tf$train$AdamOptimizer(1e-4) | ||
generator_optimizer <- tf$train$AdamOptimizer(1e-4) | ||
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num_epochs <- 150 | ||
noise_dim <- 100L | ||
num_examples_to_generate <- 25L | ||
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random_vector_for_generation <- | ||
k_random_normal(c(num_examples_to_generate, | ||
noise_dim)) | ||
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generate_and_save_images <- function(model, epoch, test_input) { | ||
predictions <- model(test_input, training = FALSE) | ||
png(paste0("images_epoch_", epoch, ".png")) | ||
par(mfcol = c(5, 5)) | ||
par(mar = c(0.5, 0.5, 0.5, 0.5), | ||
xaxs = 'i', | ||
yaxs = 'i') | ||
for (i in 1:25) { | ||
img <- predictions[i, , , 1] | ||
img <- t(apply(img, 2, rev)) | ||
image( | ||
1:28, | ||
1:28, | ||
img * 127.5 + 127.5, | ||
col = gray((0:255) / 255), | ||
xaxt = 'n', | ||
yaxt = 'n' | ||
) | ||
} | ||
dev.off() | ||
} | ||
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train <- function(dataset, epochs, noise_dim) { | ||
for (epoch in seq_len(num_epochs)) { | ||
start <- Sys.time() | ||
total_loss_gen <- 0 | ||
total_loss_disc <- 0 | ||
iter <- make_iterator_one_shot(train_dataset) | ||
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until_out_of_range({ | ||
batch <- iterator_get_next(iter) | ||
noise <- k_random_normal(c(batch_size, noise_dim)) | ||
with(tf$GradientTape() %as% gen_tape, { | ||
with(tf$GradientTape() %as% disc_tape, { | ||
generated_images <- generator(noise) | ||
disc_real_output <- discriminator(batch, training = TRUE) | ||
disc_generated_output <- | ||
discriminator(generated_images, training = TRUE) | ||
gen_loss <- generator_loss(disc_generated_output) | ||
disc_loss <- | ||
discriminator_loss(disc_real_output, disc_generated_output) | ||
}) | ||
}) | ||
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gradients_of_generator <- | ||
gen_tape$gradient(gen_loss, generator$variables) | ||
gradients_of_discriminator <- | ||
disc_tape$gradient(disc_loss, discriminator$variables) | ||
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generator_optimizer$apply_gradients(purrr::transpose(list( | ||
gradients_of_generator, generator$variables | ||
))) | ||
discriminator_optimizer$apply_gradients(purrr::transpose( | ||
list(gradients_of_discriminator, discriminator$variables) | ||
)) | ||
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total_loss_gen <- total_loss_gen + gen_loss | ||
total_loss_disc <- total_loss_disc + disc_loss | ||
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}) | ||
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cat("Time for epoch ", epoch, ": ", Sys.time() - start, "\n") | ||
cat("Generator loss: ", | ||
total_loss_gen$numpy() / batches_per_epoch, | ||
"\n") | ||
cat("Discriminator loss: ", | ||
total_loss_disc$numpy() / batches_per_epoch, | ||
"\n\n") | ||
if (epoch %% 10 == 0) | ||
generate_and_save_images(generator, | ||
epoch, | ||
random_vector_for_generation) | ||
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} | ||
} | ||
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train(train_dataset, num_epochs, noise_dim) |
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--- | ||
title: eager_dcgan | ||
type: docs | ||
repo: https://github.com/rstudio/keras | ||
menu: | ||
main: | ||
parent: keras-examples | ||
--- | ||
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<div class="source-ref"> | ||
<span class="caption">Source: </span>`r sprintf("https://github.com/rstudio/keras/blob/master/vignettes/examples/%s.R", rmarkdown::metadata$title)` | ||
</div> | ||
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```{r, echo = FALSE} | ||
knitr::opts_chunk$set(eval = FALSE) | ||
knitr::spin_child(paste0(rmarkdown::metadata$title, ".R")) | ||
``` |
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