Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,eval = FALSE)
## -----------------------------------------------------------------------------
# URLs_MNIST_SAMPLE()
## -----------------------------------------------------------------------------
# # transformations
# tfms = aug_transforms(do_flip = FALSE)
# path = 'mnist_sample'
# bs = 20
#
# #load into memory
# data = ImageDataLoaders_from_folder(path, batch_tfms = tfms, size = 26, bs = bs)
#
# learn = cnn_learner(data, resnet18(), metrics = accuracy)
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(1, cbs = TerminateOnNaNCallback())
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(10, cbs = EarlyStoppingCallback(monitor='valid_loss', patience = 1))
## -----------------------------------------------------------------------------
# learn = cnn_learner(data, resnet18(), metrics = accuracy, path = getwd())
#
# learn %>% fit_one_cycle(3, cbs = SaveModelCallback(every_epoch = TRUE, fname = 'model'))
## -----------------------------------------------------------------------------
# list.files('models')
# # [1] "model_0.pth" "model_1.pth" "model_2.pth"
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(10, 1e-2, cbs = ReduceLROnPlateau(monitor='valid_loss', patience = 1))
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(10, 1e-2, cbs = ReduceLROnPlateau(monitor='valid_loss',
# min_delta=0.1, patience = 1, min_lr = 1e-8))
## -----------------------------------------------------------------------------
# learn = cnn_learner(data, resnet18(), metrics = accuracy, path = getwd())
#
# learn %>% fit_one_cycle(2, cbs = list(CSVLogger(),
# ReduceLROnPlateau(monitor='valid_loss',
# min_delta=0.1, patience = 1, min_lr = 1e-8)))
# history = read.csv('history.csv')
# history
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