In EagerAI/fastai: Interface to 'fastai'
knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T)
Intro
The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models.
Dataset
Download dataset:
URLs_SIIM_SMALL()
Read example:
library(fastai)
library(magrittr)
library(zeallot)
items = get_dicom_files("siim_small/train/")
items
c(trn,val) %<-% RandomSplitter()(items)
patient = 7
xray_sample = dcmread(items[patient])
xray_sample %>% show() %>% plot()
At the same time it is possible to gather the information from dcm files:
# gather data
items_list = items$items
dicom_dataframe = data.frame()
for(i in 1:length(items_list)) {
res = dcmread(as.character(items_list[[i]])) %>% to_matrix(matrix = FALSE)
dicom_dataframe = dicom_dataframe %>% rbind(res)
if(i %% 50 == 0) {
print(i)
}
}
Output:
[1] 50
[1] 100
[1] 150
[1] 200
[1] 250
> tibble::tibble(head(dicom_dataframe))
# A tibble: 6 x 42
SpecificCharact… SOPClassUID SOPInstanceUID StudyDate StudyTime AccessionNumber Modality ConversionType
<chr> <chr> <chr> <int> <dbl> <lgl> <chr> <chr>
1 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD
2 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD
3 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD
4 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD
5 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD
6 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD
# … with 34 more variables: ReferringPhysicianName <lgl>, SeriesDescription <chr>, PatientName <chr>, PatientID <chr>,
# PatientBirthDate <lgl>, PatientSex <chr>, PatientAge <int>, BodyPartExamined <chr>, ViewPosition <chr>,
# StudyInstanceUID <chr>, SeriesInstanceUID <chr>, StudyID <lgl>, SeriesNumber <int>, InstanceNumber <int>,
# PatientOrientation <lgl>, SamplesPerPixel <int>, PhotometricInterpretation <chr>, Rows <int>, Columns <int>,
# PixelSpacing <dbl>, BitsAllocated <int>, BitsStored <int>, HighBit <int>, PixelRepresentation <int>,
# LossyImageCompression <int>, LossyImageCompressionMethod <chr>, fname <chr>, MultiPixelSpacing <int>,
# PixelSpacing1 <dbl>, img_min <int>, img_max <int>, img_mean <dbl>, img_std <dbl>, img_pct_window <dbl>
Dataloader
Prepare dataloader and see batch:
df = data.table::fread("siim_small/labels.csv")
pneumothorax = DataBlock(blocks = list(ImageBlock(cls = Dicom()), CategoryBlock()),
get_x = function(x) {paste('siim_small', x[[1]], sep = '/')},
get_y = function(x) {paste(x[[2]])},
batch_tfms = list(aug_transforms(size = 224),
Normalize_from_stats( imagenet_stats() )
))
dls = pneumothorax %>% dataloaders(as.matrix(df))
dls %>% show_batch(max_n = 16)
Fit model
At first, construct model and print summary:
learn = cnn_learner(dls, resnet34(), metrics = accuracy)
summary(learn)
Sequential (Input shape: ['64 x 3 x 224 x 224'])
================================================================
Layer (type) Output Shape Param # Trainable
================================================================
Conv2d 64 x 64 x 112 x 112 9,408 False
________________________________________________________________
BatchNorm2d 64 x 64 x 112 x 112 128 True
________________________________________________________________
ReLU 64 x 64 x 112 x 112 0 False
________________________________________________________________
MaxPool2d 64 x 64 x 56 x 56 0 False
________________________________________________________________
Conv2d 64 x 64 x 56 x 56 36,864 False
________________________________________________________________
BatchNorm2d 64 x 64 x 56 x 56 128 True
________________________________________________________________
ReLU 64 x 64 x 56 x 56 0 False
________________________________________________________________
Conv2d 64 x 64 x 56 x 56 36,864 False
________________________________________________________________
BatchNorm2d 64 x 64 x 56 x 56 128 True
________________________________________________________________
Conv2d 64 x 64 x 56 x 56 36,864 False
________________________________________________________________
BatchNorm2d 64 x 64 x 56 x 56 128 True
________________________________________________________________
ReLU 64 x 64 x 56 x 56 0 False
________________________________________________________________
Conv2d 64 x 64 x 56 x 56 36,864 False
________________________________________________________________
BatchNorm2d 64 x 64 x 56 x 56 128 True
________________________________________________________________
Conv2d 64 x 64 x 56 x 56 36,864 False
________________________________________________________________
BatchNorm2d 64 x 64 x 56 x 56 128 True
________________________________________________________________
ReLU 64 x 64 x 56 x 56 0 False
________________________________________________________________
Conv2d 64 x 64 x 56 x 56 36,864 False
________________________________________________________________
BatchNorm2d 64 x 64 x 56 x 56 128 True
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 73,728 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
ReLU 64 x 128 x 28 x 28 0 False
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 8,192 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
ReLU 64 x 128 x 28 x 28 0 False
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
ReLU 64 x 128 x 28 x 28 0 False
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
ReLU 64 x 128 x 28 x 28 0 False
________________________________________________________________
Conv2d 64 x 128 x 28 x 28 147,456 False
________________________________________________________________
BatchNorm2d 64 x 128 x 28 x 28 256 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 294,912 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
ReLU 64 x 256 x 14 x 14 0 False
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 32,768 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
ReLU 64 x 256 x 14 x 14 0 False
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
ReLU 64 x 256 x 14 x 14 0 False
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
ReLU 64 x 256 x 14 x 14 0 False
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
ReLU 64 x 256 x 14 x 14 0 False
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
ReLU 64 x 256 x 14 x 14 0 False
________________________________________________________________
Conv2d 64 x 256 x 14 x 14 589,824 False
________________________________________________________________
BatchNorm2d 64 x 256 x 14 x 14 512 True
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 1,179,648 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
ReLU 64 x 512 x 7 x 7 0 False
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 2,359,296 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 131,072 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 2,359,296 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
ReLU 64 x 512 x 7 x 7 0 False
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 2,359,296 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 2,359,296 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
ReLU 64 x 512 x 7 x 7 0 False
________________________________________________________________
Conv2d 64 x 512 x 7 x 7 2,359,296 False
________________________________________________________________
BatchNorm2d 64 x 512 x 7 x 7 1,024 True
________________________________________________________________
AdaptiveAvgPool2d 64 x 512 x 1 x 1 0 False
________________________________________________________________
AdaptiveMaxPool2d 64 x 512 x 1 x 1 0 False
________________________________________________________________
Flatten 64 x 1024 0 False
________________________________________________________________
BatchNorm1d 64 x 1024 2,048 True
________________________________________________________________
Dropout 64 x 1024 0 False
________________________________________________________________
Linear 64 x 512 524,288 True
________________________________________________________________
ReLU 64 x 512 0 False
________________________________________________________________
BatchNorm1d 64 x 512 1,024 True
________________________________________________________________
Dropout 64 x 512 0 False
________________________________________________________________
Linear 64 x 2 1,024 True
________________________________________________________________
Total params: 21,813,056
Total trainable params: 545,408
Total non-trainable params: 21,267,648
Optimizer used: <function Adam at 0x7f2c8cd3b1e0>
Loss function: FlattenedLoss of CrossEntropyLoss()
Model frozen up to parameter group #2
Callbacks:
- TrainEvalCallback
- Recorder
- ProgressCallback
Finally, fit the model:
learn %>% fit_one_cycle(3)
epoch train_loss valid_loss accuracy time
0 1.272695 0.905151 0.640000 00:02
1 1.130290 0.811485 0.680000 00:02
2 1.053936 0.748447 0.700000 00:02
Conclusion
learn %>% show_results()
EagerAI/fastai documentation built on April 16, 2024, 12:01 p.m.
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knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T)
Intro
The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models.
Dataset
Download dataset:
URLs_SIIM_SMALL()
Read example:
library(fastai) library(magrittr) library(zeallot) items = get_dicom_files("siim_small/train/") items c(trn,val) %<-% RandomSplitter()(items) patient = 7 xray_sample = dcmread(items[patient]) xray_sample %>% show() %>% plot()
At the same time it is possible to gather the information from dcm files:
# gather data items_list = items$items dicom_dataframe = data.frame() for(i in 1:length(items_list)) { res = dcmread(as.character(items_list[[i]])) %>% to_matrix(matrix = FALSE) dicom_dataframe = dicom_dataframe %>% rbind(res) if(i %% 50 == 0) { print(i) } }
Output:
[1] 50 [1] 100 [1] 150 [1] 200 [1] 250 > tibble::tibble(head(dicom_dataframe)) # A tibble: 6 x 42 SpecificCharact… SOPClassUID SOPInstanceUID StudyDate StudyTime AccessionNumber Modality ConversionType <chr> <chr> <chr> <int> <dbl> <lgl> <chr> <chr> 1 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD 2 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD 3 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD 4 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD 5 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD 6 ISO_IR 100 1.2.840.10… 1.2.276.0.723… 19010101 0 NA CR WSD # … with 34 more variables: ReferringPhysicianName <lgl>, SeriesDescription <chr>, PatientName <chr>, PatientID <chr>, # PatientBirthDate <lgl>, PatientSex <chr>, PatientAge <int>, BodyPartExamined <chr>, ViewPosition <chr>, # StudyInstanceUID <chr>, SeriesInstanceUID <chr>, StudyID <lgl>, SeriesNumber <int>, InstanceNumber <int>, # PatientOrientation <lgl>, SamplesPerPixel <int>, PhotometricInterpretation <chr>, Rows <int>, Columns <int>, # PixelSpacing <dbl>, BitsAllocated <int>, BitsStored <int>, HighBit <int>, PixelRepresentation <int>, # LossyImageCompression <int>, LossyImageCompressionMethod <chr>, fname <chr>, MultiPixelSpacing <int>, # PixelSpacing1 <dbl>, img_min <int>, img_max <int>, img_mean <dbl>, img_std <dbl>, img_pct_window <dbl>
Dataloader
Prepare dataloader and see batch:
df = data.table::fread("siim_small/labels.csv") pneumothorax = DataBlock(blocks = list(ImageBlock(cls = Dicom()), CategoryBlock()), get_x = function(x) {paste('siim_small', x[[1]], sep = '/')}, get_y = function(x) {paste(x[[2]])}, batch_tfms = list(aug_transforms(size = 224), Normalize_from_stats( imagenet_stats() ) )) dls = pneumothorax %>% dataloaders(as.matrix(df)) dls %>% show_batch(max_n = 16)
Fit model
At first, construct model and print summary:
learn = cnn_learner(dls, resnet34(), metrics = accuracy) summary(learn)
Sequential (Input shape: ['64 x 3 x 224 x 224']) ================================================================ Layer (type) Output Shape Param # Trainable ================================================================ Conv2d 64 x 64 x 112 x 112 9,408 False ________________________________________________________________ BatchNorm2d 64 x 64 x 112 x 112 128 True ________________________________________________________________ ReLU 64 x 64 x 112 x 112 0 False ________________________________________________________________ MaxPool2d 64 x 64 x 56 x 56 0 False ________________________________________________________________ Conv2d 64 x 64 x 56 x 56 36,864 False ________________________________________________________________ BatchNorm2d 64 x 64 x 56 x 56 128 True ________________________________________________________________ ReLU 64 x 64 x 56 x 56 0 False ________________________________________________________________ Conv2d 64 x 64 x 56 x 56 36,864 False ________________________________________________________________ BatchNorm2d 64 x 64 x 56 x 56 128 True ________________________________________________________________ Conv2d 64 x 64 x 56 x 56 36,864 False ________________________________________________________________ BatchNorm2d 64 x 64 x 56 x 56 128 True ________________________________________________________________ ReLU 64 x 64 x 56 x 56 0 False ________________________________________________________________ Conv2d 64 x 64 x 56 x 56 36,864 False ________________________________________________________________ BatchNorm2d 64 x 64 x 56 x 56 128 True ________________________________________________________________ Conv2d 64 x 64 x 56 x 56 36,864 False ________________________________________________________________ BatchNorm2d 64 x 64 x 56 x 56 128 True ________________________________________________________________ ReLU 64 x 64 x 56 x 56 0 False ________________________________________________________________ Conv2d 64 x 64 x 56 x 56 36,864 False ________________________________________________________________ BatchNorm2d 64 x 64 x 56 x 56 128 True ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 73,728 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ ReLU 64 x 128 x 28 x 28 0 False ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 8,192 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ ReLU 64 x 128 x 28 x 28 0 False ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ ReLU 64 x 128 x 28 x 28 0 False ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ ReLU 64 x 128 x 28 x 28 0 False ________________________________________________________________ Conv2d 64 x 128 x 28 x 28 147,456 False ________________________________________________________________ BatchNorm2d 64 x 128 x 28 x 28 256 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 294,912 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ ReLU 64 x 256 x 14 x 14 0 False ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 32,768 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ ReLU 64 x 256 x 14 x 14 0 False ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ ReLU 64 x 256 x 14 x 14 0 False ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ ReLU 64 x 256 x 14 x 14 0 False ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ ReLU 64 x 256 x 14 x 14 0 False ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ ReLU 64 x 256 x 14 x 14 0 False ________________________________________________________________ Conv2d 64 x 256 x 14 x 14 589,824 False ________________________________________________________________ BatchNorm2d 64 x 256 x 14 x 14 512 True ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 1,179,648 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ ReLU 64 x 512 x 7 x 7 0 False ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 2,359,296 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 131,072 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 2,359,296 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ ReLU 64 x 512 x 7 x 7 0 False ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 2,359,296 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 2,359,296 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ ReLU 64 x 512 x 7 x 7 0 False ________________________________________________________________ Conv2d 64 x 512 x 7 x 7 2,359,296 False ________________________________________________________________ BatchNorm2d 64 x 512 x 7 x 7 1,024 True ________________________________________________________________ AdaptiveAvgPool2d 64 x 512 x 1 x 1 0 False ________________________________________________________________ AdaptiveMaxPool2d 64 x 512 x 1 x 1 0 False ________________________________________________________________ Flatten 64 x 1024 0 False ________________________________________________________________ BatchNorm1d 64 x 1024 2,048 True ________________________________________________________________ Dropout 64 x 1024 0 False ________________________________________________________________ Linear 64 x 512 524,288 True ________________________________________________________________ ReLU 64 x 512 0 False ________________________________________________________________ BatchNorm1d 64 x 512 1,024 True ________________________________________________________________ Dropout 64 x 512 0 False ________________________________________________________________ Linear 64 x 2 1,024 True ________________________________________________________________ Total params: 21,813,056 Total trainable params: 545,408 Total non-trainable params: 21,267,648 Optimizer used: <function Adam at 0x7f2c8cd3b1e0> Loss function: FlattenedLoss of CrossEntropyLoss() Model frozen up to parameter group #2 Callbacks: - TrainEvalCallback - Recorder - ProgressCallback
Finally, fit the model:
learn %>% fit_one_cycle(3)
epoch train_loss valid_loss accuracy time 0 1.272695 0.905151 0.640000 00:02 1 1.130290 0.811485 0.680000 00:02 2 1.053936 0.748447 0.700000 00:02
Conclusion
learn %>% show_results()
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