Merge pull request #1023 from henry090/master

update examples

Author: Sigrid Keydana

vignettes/examples/eager_cvae.R

@@ -5,17 +5,14 @@
 #' https://blogs.rstudio.com/tensorflow/posts/2018-10-22-mmd-vae/
 
 
+
 library(keras)
-use_implementation("tensorflow")
 library(tensorflow)
-tfe_enable_eager_execution(device_policy = "silent")
-
 library(tfdatasets)
 library(dplyr)
 library(ggplot2)
 library(glue)
 
-
 # Setup and preprocessing -------------------------------------------------
 
 fashion <- dataset_fashion_mnist()
@@ -24,9 +21,9 @@ c(train_images, train_labels) %<-% fashion$train
 c(test_images, test_labels) %<-% fashion$test
 
 train_x <-
-  train_images %>% `/`(255) %>% k_reshape(c(60000, 28, 28, 1))
+  train_images %>% `/`(255) %>% k_reshape(c(60000, 28, 28, 1)) %>% k_cast(dtype='float32')
 test_x <-
-  test_images %>% `/`(255) %>% k_reshape(c(10000, 28, 28, 1))
+  test_images %>% `/`(255) %>% k_reshape(c(10000, 28, 28, 1)) %>% k_cast(dtype='float32')
 
 class_names = c('T-shirt/top',
                 'Trouser',
@@ -63,7 +60,8 @@ encoder_model <- function(name = NULL) {
         filters = 32,
         kernel_size = 3,
         strides = 2,
-        activation = "relu"
+        activation = "relu",
+        dtype='float32'
       )
     self$conv2 <-
       layer_conv_2d(
@@ -126,22 +124,22 @@ decoder_model <- function(name = NULL) {
 }
 
 reparameterize <- function(mean, logvar) {
-  eps <- k_random_normal(shape = mean$shape, dtype = tf$float64)
+  eps <- k_random_normal(shape = mean$shape, dtype = tf$float32)
   eps * k_exp(logvar * 0.5) + mean
 }
 
 
 # Loss and optimizer ------------------------------------------------------
 
 normal_loglik <- function(sample, mean, logvar, reduce_axis = 2) {
-  loglik <- k_constant(0.5, dtype = tf$float64) * 
-    (k_log(2 * k_constant(pi, dtype = tf$float64)) +
-     logvar +
-     k_exp(-logvar) * (sample - mean) ^ 2)
+  loglik <- k_constant(0.5) * 
+    (k_log(2 * k_constant(pi)) +
+       logvar +
+       k_exp(-logvar) * (sample - mean) ^ 2)
   - k_sum(loglik, axis = reduce_axis)
 }
 
-optimizer <- tf$train$AdamOptimizer(1e-4)
+optimizer <- tf$keras$optimizers$Adam(1e-4)
 
 
 
@@ -151,7 +149,7 @@ num_examples_to_generate <- 64
 
 random_vector_for_generation <-
   k_random_normal(shape = list(num_examples_to_generate, latent_dim),
-                  dtype = tf$float64)
+                  dtype = tf$float32)
 
 generate_random_clothes <- function(epoch) {
   predictions <-
@@ -216,7 +214,7 @@ show_grid <- function(epoch) {
       z_sample <- matrix(c(grid_x[i], grid_y[j]), ncol = 2)
       column <-
         rbind(column,
-              (decoder(z_sample) %>% tf$nn$sigmoid() %>% as.numeric()) %>% matrix(ncol = img_size))
+              (decoder(k_cast(z_sample,'float32')) %>% tf$nn$sigmoid() %>% as.numeric()) %>% matrix(ncol = img_size))
     }
     rows <- cbind(rows, column)
   }
@@ -267,8 +265,8 @@ for (epoch in seq_len(num_epochs)) {
         -k_sum(crossentropy_loss)
       logpz <-
         normal_loglik(z,
-                      k_constant(0, dtype = tf$float64),
-                      k_constant(0, dtype = tf$float64))
+                      k_constant(0, dtype = tf$float32),
+                      k_constant(0, dtype = tf$float32))
       logqz_x <- normal_loglik(z, mean, logvar)
       loss <- -k_mean(logpx_z + logpz - logqz_x)
 
@@ -284,12 +282,10 @@ for (epoch in seq_len(num_epochs)) {
 
     optimizer$apply_gradients(purrr::transpose(list(
       encoder_gradients, encoder$variables
-    )),
-    global_step = tf$train$get_or_create_global_step())
+    )))
     optimizer$apply_gradients(purrr::transpose(list(
       decoder_gradients, decoder$variables
-    )),
-    global_step = tf$train$get_or_create_global_step())
+    )))
 
   })
 
@@ -313,3 +309,5 @@ for (epoch in seq_len(num_epochs)) {
   }
 }
 
+
+

vignettes/examples/eager_styletransfer.R

@@ -7,9 +7,7 @@
 
 library(keras)
 use_implementation("tensorflow")
-use_session_with_seed(7777, disable_gpu = FALSE, disable_parallel_cpu = FALSE)
 library(tensorflow)
-tfe_enable_eager_execution(device_policy = "silent")
 
 library(purrr)
 library(glue)
@@ -210,13 +208,12 @@ run_style_transfer <- function(content_path,
       gram_matrix(feature))
 
   init_image <- load_and_process_image(content_path)
-  init_image <-
-    tf$contrib$eager$Variable(init_image, dtype = "float32")
+  init_image <- tf$Variable(tf$cast(init_image,dtype = "float32"))
 
   optimizer <-
-    tf$train$AdamOptimizer(learning_rate = 1,
-                           beta1 = 0.99,
-                           epsilon = 1e-1)
+    tf$optimizers$Adam(learning_rate = 1,
+                       beta_1 = 0.99,
+                       epsilon = 1e-1)
 
   c(best_loss, best_image) %<-% list(Inf, NULL)
   loss_weights <- list(style_weight, content_weight)
@@ -242,16 +239,16 @@ run_style_transfer <- function(content_path,
 
     end_time <- Sys.time()
 
-    if (k_cast_to_floatx(loss) < best_loss) {
-      best_loss <- k_cast_to_floatx(loss)
+    if (as.array(loss) < best_loss) {
+      best_loss <- as.array(loss)
       best_image <- init_image
     }
 
     if (i %% 50 == 0) {
       glue("Iteration: {i}") %>% print()
       glue(
-        "Total loss: {k_cast_to_floatx(loss)}, style loss: {k_cast_to_floatx(style_score)},
-        content loss: {k_cast_to_floatx(content_score)}, total variation loss: {k_cast_to_floatx(variation_score)},
+        "Total loss: {as.array(loss)}, style loss: {as.array(style_score)},
+        content loss: {as.array(content_score)}, total variation loss: {as.array(variation_score)},
         time for 1 iteration: {(Sys.time() - start_time) %>% round(2)}"
       ) %>% print()