Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
## -----------------------------------------------------------------------------
# commands_path = "SPEECHCOMMANDS"
# audio_files = get_audio_files(commands_path)
# length(audio_files$items)
# # [1] 105835
## -----------------------------------------------------------------------------
# DBMelSpec = SpectrogramTransformer(mel=TRUE, to_db=TRUE)
# a2s = DBMelSpec()
# crop_4000ms = ResizeSignal(4000)
# tfms = list(crop_4000ms, a2s)
## -----------------------------------------------------------------------------
# auds = DataBlock(blocks = list(AudioBlock(), CategoryBlock()),
# get_items = get_audio_files,
# splitter = RandomSplitter(),
# item_tfms = tfms,
# get_y = parent_label)
#
# audio_dbunch = auds %>% dataloaders(commands_path, item_tfms = tfms, bs = 20)
## -----------------------------------------------------------------------------
# audio_dbunch %>% show_batch(figsize = c(15, 8.5), nrows = 3, ncols = 3, max_n = 9, dpi = 180)
## -----------------------------------------------------------------------------
# torch = torch()
# nn = nn()
#
# learn = Learner(dls, xresnet18(pretrained = FALSE), nn$CrossEntropyLoss(), metrics=accuracy)
#
# # channel from 3 to 1
# learn$model[0][0][['in_channels']] %f% 1L
# # reshape
# new_weight_shape <- torch$nn$parameter$Parameter(
# (learn$model[0][0]$weight %>% narrow('[:,1,:,:]'))$unsqueeze(1L))
#
# # assign with %f%
# learn$model[0][0][['weight']] %f% new_weight_shape
## -----------------------------------------------------------------------------
# # login for the 1st time then remove it
# login("API_key_from_wandb_dot_ai")
# init(project='R')
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(3, lr_max=slice(1e-2), cbs = list(WandbCallback()))
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