tf2 update

01-import.R

@@ -3,13 +3,13 @@ library(dplyr)
 
 files <- fs::dir_ls(
   path = "data/speech_commands_v0.01/", 
-  recursive = TRUE, 
+  recurse = TRUE, 
   glob = "*.wav"
 )
 
 files <- files[!str_detect(files, "background_noise")]
 
-df <- data_frame(
+df <- tibble(
   fname = files, 
   class = fname %>% str_extract("1/.*/") %>% 
     str_replace_all("1/", "") %>%

02-generator.R

@@ -2,8 +2,6 @@
 
 library(tfdatasets)
 
-audio_ops <- tf$contrib$framework$python$ops$audio_ops
-
 data_generator <- function(df, batch_size, shuffle = TRUE, 
                            window_size_ms = 30, window_stride_ms = 10) {
 
@@ -21,18 +19,17 @@ data_generator <- function(df, batch_size, shuffle = TRUE,
     dataset_map(function(obs) {
 
       # decoding wav files
-      audio_binary <- tf$read_file(tf$reshape(obs$fname, shape = list()))
-      wav <- audio_ops$decode_wav(audio_binary, desired_channels = 1)
+      audio_binary <- tf$io$read_file(tf$reshape(obs$fname, shape = list()))
+      wav <- tf$audio$decode_wav(audio_binary, desired_channels = 1)
+      samples <- wav$audio
+      samples <- samples %>% tf$transpose(perm = c(1L, 0L))
 
       # create the spectrogram
-      spectrogram <- audio_ops$audio_spectrogram(
-        wav$audio, 
-        window_size = window_size, 
-        stride = stride,
-        magnitude_squared = TRUE
-      )
+      spectrogram <- tf$signal$stft(samples,
+                                   frame_length = as.integer(window_size),
+                                   frame_step = as.integer(stride))
 
-      spectrogram <- tf$log(tf$abs(spectrogram) + 0.01)
+      spectrogram <- tf$math$log(tf$abs(spectrogram) + 0.01)
       spectrogram <- tf$transpose(spectrogram, perm = c(1L, 2L, 0L))
 
       # transform the class_id into a one-hot encoded vector

03-model.R

@@ -28,7 +28,7 @@ model %>%
 
 # Compile model
 model %>% compile(
-  loss = loss_categorical_crossentropy,
+  loss = "categorical_crossentropy",
   optimizer = optimizer_adadelta(),
   metrics = c('accuracy')
 )
@@ -42,7 +42,4 @@ model %>% fit_generator(
   validation_steps = 0.3*nrow(df)/32
 )
 
-save_model_hdf5(model, "model.hdf5")
-
-
-
+save_model_hdf5(model, filepath = "model.hdf5")