DNN (2주차)

Special Topics in Machine Learning
추천시스템
텍스트분석
GAN
Author

SEOYEON CHOI

Published

September 14, 2022

기계학습 특강 (2주차) 9월14일 [추천시스템, 텍스트분석, GAN]

imports

#
from fastai.collab import * ## 추천시스템
from fastai.text.all import * ## 텍스트분석 
from fastai.vision.all import *  ## GAN (이미지분석) 
from fastai.vision.gan import * ## GAN (이미지생성)
import pandas as pd

이미지 자료분석 실습 (지난시간 복습)

1단계: 데이터의 정리

path = untar_data(URLs.PETS)/'images'
path.ls()
(#7393) [Path('/home/csy/.fastai/data/oxford-iiit-pet/images/Bombay_13.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/beagle_193.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/Ragdoll_8.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/boxer_106.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/keeshond_56.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_162.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/saint_bernard_136.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/staffordshire_bull_terrier_76.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/pug_173.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_117.jpg')...]

every files’ list

fnames = get_image_files(path)
fnames
(#7390) [Path('/home/csy/.fastai/data/oxford-iiit-pet/images/Bombay_13.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/beagle_193.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/Ragdoll_8.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/boxer_106.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/keeshond_56.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_162.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/saint_bernard_136.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/staffordshire_bull_terrier_76.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/pug_173.jpg'),Path('/home/csy/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_117.jpg')...]

image files’ list

ImageDataLoaders.from_name_func??
Signature:
ImageDataLoaders.from_name_func(
    path,
    fnames,
    label_func,
    valid_pct=0.2,
    seed=None,
    item_tfms=None,
    batch_tfms=None,
    bs=64,
    val_bs=None,
    shuffle=True,
    device=None,
)
Source:   
    @classmethod
    def from_name_func(cls, path, fnames, label_func, **kwargs):
        "Create from the name attrs of `fnames` in `path`s with `label_func`"
        if sys.platform == 'win32' and isinstance(label_func, types.LambdaType) and label_func.__name__ == '<lambda>':
            # https://medium.com/@jwnx/multiprocessing-serialization-in-python-with-pickle-9844f6fa1812
            raise ValueError("label_func couldn't be lambda function on Windows")
        f = using_attr(label_func, 'name')
        return cls.from_path_func(path, fnames, f, **kwargs)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/vision/data.py
Type:      method

def f(fname):
    if fname[0].isupper():
        return 'cat'
    else:
        return 'dog'
f(x) = x+ 1
lambda x : x+1
fnames[0]
Path('/home/csy/.fastai/data/oxford-iiit-pet/images/Bombay_13.jpg')
f = lambda fname: 'cat' if fname[0].isupper() else 'dog'
f('s')
'dog'
f('D')
'cat'
dls = ImageDataLoaders.from_name_func(
    path, 
    fnames,
    lambda fname: 'cat' if fname[0].isupper() else 'dog'
    item_tfms=Resize(224)) 
dls = ImageDataLoaders.from_name_func(
    path, 
    fnames,
    f, # f대신 (lambda fname: 'cat' if fname[0].isupper() else 'dog') 를 넣어도 가능
    item_tfms=Resize(224)) # 사이즈가 달라서 통일
dls.show_batch()

2단계: lrnr 오브젝트 생성

cnn_learner??
Signature:
cnn_learner(
    dls,
    arch,
    normalize=True,
    n_out=None,
    pretrained=True,
    config=None,
    loss_func=None,
    opt_func=<function Adam at 0x7fcb70042550>,
    lr=0.001,
    splitter=None,
    cbs=None,
    metrics=None,
    path=None,
    model_dir='models',
    wd=None,
    wd_bn_bias=False,
    train_bn=True,
    moms=(0.95, 0.85, 0.95),
    cut=None,
    n_in=3,
    init=<function kaiming_normal_ at 0x7fcbc439a8b0>,
    custom_head=None,
    concat_pool=True,
    lin_ftrs=None,
    ps=0.5,
    first_bn=True,
    bn_final=False,
    lin_first=False,
    y_range=None,
)
Source:   
@delegates(create_cnn_model)
def cnn_learner(dls, arch, normalize=True, n_out=None, pretrained=True, config=None,
                # learner args
                loss_func=None, opt_func=Adam, lr=defaults.lr, splitter=None, cbs=None, metrics=None, path=None,
                model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95,0.85,0.95),
                # other model args
                **kwargs):
    "Build a convnet style learner from `dls` and `arch`"
    if config:
        warnings.warn('config param is deprecated. Pass your args directly to cnn_learner.')
        kwargs = {**config, **kwargs}
    meta = model_meta.get(arch, _default_meta)
    if normalize: _add_norm(dls, meta, pretrained)
    if n_out is None: n_out = get_c(dls)
    assert n_out, "`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`"
    model = create_cnn_model(arch, n_out, pretrained=pretrained, **kwargs)
    splitter=ifnone(splitter, meta['split'])
    learn = Learner(dls=dls, model=model, loss_func=loss_func, opt_func=opt_func, lr=lr, splitter=splitter, cbs=cbs,
                   metrics=metrics, path=path, model_dir=model_dir, wd=wd, wd_bn_bias=wd_bn_bias, train_bn=train_bn,
                   moms=moms)
    if pretrained: learn.freeze()
    # keep track of args for loggers
    store_attr('arch,normalize,n_out,pretrained', self=learn, **kwargs)
    return learn
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/vision/learner.py
Type:      function

!cat ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/vision/learner.py

이 코드로 존재하는 함수의 정의 확인 가능

어디 소속된 함수인지 확인 하기 위해

fastai에 소속된 cnn_leaner,따라서 fastai를 import해야 나타나지.

lrnr = cnn_learner(dls,resnet34,metrics=error_rate)
lrnr.dls.show_batch()

id(lrnr.dls)
140510181797744
id(dls)
140510181797744

주소가 같다. 같은 역할

lrnr에 dls가 소속되어 있다고 생각(?) - 포스트잇을 위에 덧붙인다 생각

3단계: lrnr.학습()

학습하는 fine_tune 이외에 여러가지 있음 - fine_tune 학습된 일부는 유지하고 바꿀 부분만 학습시키는 법: transfer learning

for exampel: cnn의 1d에서는 끝에만 학습

lrnr.fine_tune(1)
epoch train_loss valid_loss error_rate time
0 0.155603 0.014394 0.006766 00:10
epoch train_loss valid_loss error_rate time
0 0.051081 0.008424 0.002706 00:11
  • fine_tune()은 모든 가중치를 학습하는 것이 아니라 일부만 학습하는 것임.
  • fine_tune()이외이 방법으로 학습할 수도 있음.

4단계: lrnr.예측()

(방법1) lrnr.predict() 함수를 이용

lrnr.predict('2022-09-07-dogs.jpeg') # 방법1-1
#lrnr.predict(PILImage.create('2022-09-07-dogs.jpeg')) # 방법1-2
#lrnr.predict(path.ls()[0]) # 방법1-3
('dog', TensorBase(1), TensorBase([2.2932e-05, 9.9998e-01]))

컴퓨터가 이해하기 쉬운 방법인 1-2번째 방법

lrnr.predict(PILImage.create('2022-09-07-dogs.jpeg'))
('dog', TensorBase(1), TensorBase([2.2932e-05, 9.9998e-01]))
lrnr.predict(path.ls()[1])
('dog', TensorBase(1), TensorBase([2.4945e-08, 1.0000e+00]))
dir(lrnr.model)

directory에 _call_있으면 함수처럼 사용 가능

(방법2) lrnr.model(X) 를 이용: X의 shape이 (?,3,224,224)의 형태의 텐서이어야함

type(dls.one_batch())
tuple

끝에 괄호로 묶여 있으면 tuple

X,y = dls.one_batch() # 방법2
lrnr.model(X[0:1]) 
TensorBase([[-8.3588,  7.0462]], device='cuda:0', grad_fn=<AliasBackward0>)
X[1].shape
torch.Size([3, 224, 224])
X[:2].shape
torch.Size([2, 3, 224, 224])
X.shape
torch.Size([64, 3, 224, 224])

image의 사이즈 224 * 224 - 3개의 채널 - 64개 - image, 입력

y.shape
torch.Size([64])
  • 예측값
y[:3]
TensorCategory([1, 1, 0], device='cuda:0')
lrnr.model(X[0])

오류 뜬다. - torch.Size([3, 224, 224]) - shape을 - torch.Size([?, 3, 224, 224]) - 이런 식으로 만들어주자, 입력

lrnr.model(X[:3])
TensorBase([[ -8.3605,   7.0472],
        [ -4.4236,   5.1110],
        [ 14.0977, -13.0582]], device='cuda:0', grad_fn=<AliasBackward0>)
lrnr.model(X)
TensorBase([[ -8.3595,   7.0465],
        [ -4.4246,   5.1111],
        [ 14.0959, -13.0577],
        [ -6.4868,   7.2289],
        [ -3.4974,   2.0202],
        [ -7.1135,   6.2276],
        [ -4.2407,   2.9429],
        [ -7.0260,   6.4789],
        [ -6.5011,   5.1029],
        [ -7.4927,   4.9038],
        [ -5.7292,   5.0113],
        [ -9.6244,   5.7399],
        [ -6.8247,   3.4742],
        [ 17.2742, -12.6829],
        [ -4.0548,   2.6589],
        [ 16.3894, -14.2360],
        [ -3.8864,   5.6632],
        [ -5.1192,   6.0355],
        [ 11.3016, -13.4798],
        [ -8.1850,   7.5925],
        [  8.3147,  -5.9946],
        [ -8.0415,   8.4349],
        [ -9.6461,   8.3790],
        [ -5.4923,   5.8070],
        [ 12.1504,  -9.3661],
        [ -7.7945,   6.7907],
        [ -5.0291,   3.4955],
        [ 13.8045, -11.3889],
        [ -4.5400,   5.1561],
        [ 16.5360, -13.3928],
        [ -4.0467,   3.3478],
        [ -5.8401,   7.2492],
        [  6.9878,  -4.8408],
        [ -8.0189,   6.0578],
        [ -7.7578,   4.7063],
        [ -5.0351,   4.5309],
        [  6.0511,  -4.1623],
        [ -8.4919,   8.1300],
        [ -5.9893,   5.8341],
        [ -7.0671,   6.2901],
        [ 17.0369, -13.7746],
        [ -6.7633,   5.5232],
        [ -7.3533,   7.6700],
        [ -8.3923,   6.6368],
        [ 13.2212, -10.2649],
        [ 14.7573, -11.7938],
        [ -6.6409,   5.6934],
        [ -6.5882,   4.9800],
        [ -5.2839,   5.3899],
        [ -5.7066,   4.9765],
        [ -5.8099,   3.8355],
        [ -8.5055,   7.2022],
        [ -8.7006,   4.5980],
        [ -5.4901,   4.5288],
        [ -7.6612,   7.1533],
        [ 15.9380, -16.2778],
        [  7.9763,  -7.1954],
        [ 13.4158, -10.9864],
        [ -4.9234,   2.9219],
        [ -4.0274,   4.1298],
        [ 16.8217, -16.0985],
        [ -8.6418,   7.1085],
        [ -5.9216,   6.0076],
        [ -5.3720,   3.9876]], device='cuda:0', grad_fn=<AliasBackward0>)

\(y\) : 왼쪽이 크면 0, 오른쪽이 크면 1 - 둘다 음수인 건 없네? - 왼쪽이 양수면 0 오른쪽이 양수면 1로 생각 가능하겠다.

프로그래밍 과정

프로그래밍 과정 overview

- overview

  1. dls 오브젝트 생성

  2. lrnr 오브젝트 생성

  3. lrnr.학습()

  4. lrnr.예측()

이미지분석, 추천시스템, 텍스트분석, GAN 분석과정 비교

- 비교

이미지분석(CNN) 추천시스템 텍스트분석 GAN
1단계 ImageDataLoaders CollabDataLoaders TextDataLoaders DataBlock -> dls
2단계 cnn_learner() collab_learner() language_model_learner() GANLearner.wgan()
3단계 lrnr.fine_tune(1) lrnr.fit() lrnr.fit() lrnr.fit()
4단계 lrnr.predict(), lrnr.model(X) lrnr.model(X) lrnr.predict()

추천시스템 실습

1단계

github에서 해당 파일의 raw click하여 주소 가져오기

!wget https://raw.githubusercontent.com/guebin/DL2022/master/_notebooks/2022-09-08-rcmd_view.csv

위와 같이 wget사용하면 주소의 data 바로 다운 가능

df_view = pd.read_csv('https://raw.githubusercontent.com/guebin/DL2022/master/_notebooks/2022-09-08-rcmd_view.csv')
df_view
커피1 커피2 커피3 커피4 커피5 커피6 커피7 커피8 커피9 커피10 홍차1 홍차2 홍차3 홍차4 홍차5 홍차6 홍차7 홍차8 홍차9 홍차10
0 4.149209 NaN NaN 4.078139 4.033415 4.071871 NaN NaN NaN NaN 1.142659 1.109452 NaN 0.603118 1.084308 NaN 0.906524 NaN NaN 0.903826
1 4.031811 NaN NaN 3.822704 NaN NaN NaN 4.071410 3.996206 NaN NaN 0.839565 1.011315 NaN 1.120552 0.911340 NaN 0.860954 0.871482 NaN
2 4.082178 4.196436 NaN 3.956876 NaN NaN NaN 4.450931 3.972090 NaN NaN NaN NaN 0.983838 NaN 0.918576 1.206796 0.913116 NaN 0.956194
3 NaN 4.000621 3.895570 NaN 3.838781 3.967183 NaN NaN NaN 4.105741 1.147554 NaN 1.346860 NaN 0.614099 1.297301 NaN NaN NaN 1.147545
4 NaN NaN NaN NaN 3.888208 NaN 3.970330 3.979490 NaN 4.010982 NaN 0.920995 1.081111 0.999345 NaN 1.195183 NaN 0.818332 1.236331 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
95 0.511905 1.066144 NaN 1.315430 NaN 1.285778 NaN 0.678400 1.023020 0.886803 NaN 4.055996 NaN NaN 4.156489 4.127622 NaN NaN NaN NaN
96 NaN 1.035022 NaN 1.085834 NaN 0.812558 NaN 1.074543 NaN 0.852806 3.894772 NaN 4.071385 3.935935 NaN NaN 3.989815 NaN NaN 4.267142
97 NaN 1.115511 NaN 1.101395 0.878614 NaN NaN NaN 1.329319 NaN 4.125190 NaN 4.354638 3.811209 4.144648 NaN NaN 4.116915 3.887823 NaN
98 NaN 0.850794 NaN NaN 0.927884 0.669895 NaN NaN 0.665429 1.387329 NaN NaN 4.329404 4.111706 3.960197 NaN NaN NaN 3.725288 4.122072
99 NaN NaN 1.413968 0.838720 NaN NaN 1.094826 0.987888 NaN 1.177387 3.957383 4.136731 NaN 4.026915 NaN NaN 4.164773 4.104276 NaN NaN

100 rows × 20 columns

컴퓨터가 좋아하는 타입은 아님

  • row0 - row49 에 해당하는 유저는 커피를 선호
  • row50 - row99 에 해당하는 유저는 홍차를 선호

위의 자료는 비효율적, tidy data로 바꿔주자, 아래와 같이 정리함으로써 저장할 data도 줄어든다.

df = pd.read_csv('https://raw.githubusercontent.com/guebin/DL2022/master/_notebooks/2022-09-08-rcmd_anal.csv')
df
user item rating item_name
0 1 15 1.084308 홍차5
1 1 1 4.149209 커피1
2 1 11 1.142659 홍차1
3 1 5 4.033415 커피5
4 1 4 4.078139 커피4
... ... ... ... ...
995 100 18 4.104276 홍차8
996 100 17 4.164773 홍차7
997 100 14 4.026915 홍차4
998 100 4 0.838720 커피4
999 100 7 1.094826 커피7

1000 rows × 4 columns

  • 컴퓨터는 이러한 형태를 더 분석하기 좋아한다.
!cat 파일명

data 도 확인 가능하다

!wget https://raw.githubusercontent.com/guebin/DL2022/master/_notebooks/2022-09-08-rcmd_anal.csv
!cat Real_estate_valuation_data_set.csv
df.item.unique(),df.user.unique()
# 유저는 1~100 으로 아이템은 1~20으로 번호가 매겨져 있음 
(array([15,  1, 11,  5,  4, 14,  6, 20, 12, 17,  8,  9, 13, 19, 18, 16,  2,
         3, 10,  7]),
 array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
         14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,
         27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,
         40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,
         53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,
         66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,
         79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,
         92,  93,  94,  95,  96,  97,  98,  99, 100]))

item, user 번호 확인

CollabDataLoaders.from_df??
Signature:
CollabDataLoaders.from_df(
    ratings,
    valid_pct=0.2,
    user_name=None,
    item_name=None,
    rating_name=None,
    seed=None,
    path='.',
    bs=64,
    val_bs=None,
    shuffle=True,
    device=None,
)
Source:   
    @delegates(DataLoaders.from_dblock)
    @classmethod
    def from_df(cls, ratings, valid_pct=0.2, user_name=None, item_name=None, rating_name=None, seed=None, path='.', **kwargs):
        "Create a `DataLoaders` suitable for collaborative filtering from `ratings`."
        user_name   = ifnone(user_name,   ratings.columns[0])
        item_name   = ifnone(item_name,   ratings.columns[1])
        rating_name = ifnone(rating_name, ratings.columns[2])
        cat_names = [user_name,item_name]
        splits = RandomSplitter(valid_pct=valid_pct, seed=seed)(range_of(ratings))
        to = TabularCollab(ratings, [Categorify], cat_names, y_names=[rating_name], y_block=TransformBlock(), splits=splits)
        return to.dataloaders(path=path, **kwargs)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/collab.py
Type:      method

dls=CollabDataLoaders.from_df(df)

batch 데이터들의 group

dls.show_batch()
user item rating
0 59 14 3.986921
1 23 3 4.393831
2 43 15 1.022492
3 15 16 0.857821
4 81 11 3.892794
5 8 1 4.194341
6 6 18 1.124469
7 41 20 1.019717
8 10 18 0.789071
9 2 4 3.822704

학습 전

X,y= dls.one_batch()
type(X)
torch.Tensor
type(y)
torch.Tensor
type(dls.one_batch())
tuple
X[0],y[0]
(tensor([74,  5]), tensor([1.0687]))

  • 99번 user가 13번 아이템을 먹었을때 평점 4.3294

  • 64번 유저가 15번 아이템을 먹었을때 평점을 4.1146 주었음

2단계

collab_learner??
Signature:
collab_learner(
    dls,
    n_factors=50,
    use_nn=False,
    emb_szs=None,
    layers=None,
    config=None,
    y_range=None,
    loss_func=None,
    opt_func=<function Adam at 0x7f6f5cbebca0>,
    lr=0.001,
    splitter=<function trainable_params at 0x7f6f7682d0d0>,
    cbs=None,
    metrics=None,
    path=None,
    model_dir='models',
    wd=None,
    wd_bn_bias=False,
    train_bn=True,
    moms=(0.95, 0.85, 0.95),
)
Source:   
@delegates(Learner.__init__)
def collab_learner(dls, n_factors=50, use_nn=False, emb_szs=None, layers=None, config=None, y_range=None, loss_func=None, **kwargs):
    "Create a Learner for collaborative filtering on `dls`."
    emb_szs = get_emb_sz(dls, ifnone(emb_szs, {}))
    if loss_func is None: loss_func = MSELossFlat()
    if config is None: config = tabular_config()
    if y_range is not None: config['y_range'] = y_range
    if layers is None: layers = [n_factors]
    if use_nn: model = EmbeddingNN(emb_szs=emb_szs, layers=layers, **config)
    else:      model = EmbeddingDotBias.from_classes(n_factors, dls.classes, y_range=y_range)
    return Learner(dls, model, loss_func=loss_func, **kwargs)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/collab.py
Type:      function

lrnr = collab_learner(dls,y_range=(0,5)) # y_range는 평점의 범위

y는 평점이니까 0~5까지의 범위를 넣어주자

3단계

lrnr.fit(30) # 총 30번 정도 해야 적합이 잘된다.
epoch train_loss valid_loss time
0 2.319048 2.344653 00:00
1 2.308136 2.343053 00:00
2 2.294945 2.327852 00:00
3 2.277872 2.286632 00:00
4 2.242915 2.204543 00:00
5 2.190223 2.074773 00:00
6 2.110882 1.897575 00:00
7 2.002486 1.683303 00:00
8 1.865617 1.440904 00:00
9 1.705019 1.189762 00:00
10 1.528594 0.947479 00:00
11 1.343253 0.728591 00:00
12 1.158638 0.542451 00:00
13 0.982688 0.394331 00:00
14 0.821111 0.282930 00:00
15 0.678422 0.203212 00:00
16 0.556185 0.148874 00:00
17 0.453426 0.112210 00:00
18 0.368528 0.088727 00:00
19 0.299861 0.073288 00:00
20 0.244360 0.064172 00:00
21 0.200107 0.058580 00:00
22 0.164968 0.055078 00:00
23 0.137080 0.052871 00:00
24 0.115055 0.051715 00:00
25 0.097788 0.051180 00:00
26 0.084044 0.051137 00:00
27 0.073312 0.050811 00:00
28 0.064564 0.050948 00:00
29 0.057734 0.051064 00:00

loss가 2.3에서 0.47으로 떨어지는 모습

4단계

- 이미 있는 데이터를 예측

- 하나의 배치 전체를 예측

lrnr.model(X)

만 넣으면 에러뜬다.

!nvidia-smi
Tue Sep 20 23:48:21 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 495.46       Driver Version: 495.46       CUDA Version: 11.5     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:65:00.0 Off |                  N/A |
| 30%   51C    P2   130W / 420W |  12684MiB / 24268MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     37424      C   ...onda3/envs/csy/bin/python     3419MiB |
|    0   N/A  N/A    293359      C   ...onda3/envs/csy/bin/python     4789MiB |
|    0   N/A  N/A    378609      C   ...onda3/envs/csy/bin/python     2605MiB |
|    0   N/A  N/A    378623      C   ...onda3/envs/csy/bin/python     1869MiB |
+-----------------------------------------------------------------------------+

GPU 확인 가능

GPU아님 CPU로 올리자

yhat=lrnr.model(X.to("cuda:0"))
yhat
tensor([1.0097, 3.9209, 4.0095, 1.0768, 1.0179, 3.9701, 1.0862, 4.0802, 4.0528,
        1.0718, 3.9230, 0.9994, 0.9662, 0.9122, 0.9745, 4.0083, 0.9989, 4.1045,
        4.1632, 4.0724, 3.9754, 0.9565, 4.0757, 4.0317, 4.0740, 1.0779, 3.9354,
        0.9951, 3.9031, 1.0241, 4.0253, 4.0965, 4.0368, 4.0944, 1.0856, 4.1450,
        4.0549, 4.0072, 0.8689, 4.0659, 3.9192, 3.9501, 4.0449, 0.9437, 1.0582,
        0.9584, 4.0409, 4.0453, 1.0675, 0.9406, 1.0740, 0.9307, 0.9885, 3.9951,
        3.9118, 4.1501, 0.8893, 0.8946, 3.9687, 1.0579, 4.1036, 3.9685, 1.0809,
        1.0768], device='cuda:0', grad_fn=<AddBackward0>)
y.reshape(-1)
tensor([1.0687, 3.8248, 3.8609, 1.0261, 0.8480, 4.4577, 1.1617, 4.3921, 4.2632,
        0.7725, 4.0136, 1.2705, 0.8435, 1.0225, 0.8010, 4.1617, 1.0468, 4.1678,
        4.5026, 4.0560, 3.8630, 0.9917, 4.0591, 3.7022, 4.1746, 1.3469, 3.7943,
        1.0213, 3.7841, 0.8235, 4.0407, 3.9853, 4.1260, 4.1900, 1.0309, 4.1798,
        3.9636, 3.7450, 0.6707, 4.0318, 4.1648, 4.1057, 3.9359, 0.7864, 1.2067,
        0.8126, 4.0661, 4.1786, 1.3155, 0.9504, 1.1084, 0.8396, 0.8503, 4.0655,
        3.8489, 4.0402, 0.7891, 0.9279, 4.1935, 0.9436, 4.4777, 4.0123, 1.0577,
        0.8246])
  • lrnr.model()은 GPU메모리에 존재하고 X는 일반메모리에 존재하므로 X를 GPU메모리로 옮겨주어야 함
  • X.to(“cuda:0”)을 통하여 X를 GPU메모리로 옮기는 작업을 수행할 수 있다.

- 하나의 유저가 하나의 아이템을 선택했다고 가정하고 예측 (주어진 자료중에서 예측)

X.shape
torch.Size([64, 2])
X[0:1]
tensor([[74,  5]])

- 1번 user가 커피2 마셨을때 - 예상: 4점 근처

lrnr.model(X[0:1].to("cuda:0"))
tensor([1.0097], device='cuda:0', grad_fn=<AddBackward0>)
lrnr.model(tensor([[1,2]]).to("cuda:0"))
tensor([3.9337], device='cuda:0', grad_fn=<AddBackward0>)
  • 18번 유저가 5번 아이템(커피)를 먹는다면?
lrnr.model(X[0:1].to("cuda:0"))
tensor([1.0097], device='cuda:0', grad_fn=<AddBackward0>)
  • 평점은 4.1128정도 될것

- 하나의 유저가 하나의 아이템을 선택했다고 가정하고 예측 (주어지지 않은 자료중에서 예측)

X[0:1]
tensor([[74,  5]])
Xnew = torch.tensor([[1,  2]])
lrnr.model(Xnew.to("cuda:0"))
tensor([3.9337], device='cuda:0', grad_fn=<AddBackward0>)

텍스트분석 실습

timeseries 와 text 순서가 중요! - 가장 잘 응용할 수 있는 게 chatbot챗봇 - 나는 \(\to\) 학교에 \(\to\) 갔다.

timeseries는 뒤를 정확히 맞춰야 하지만, text는 그렇지 않..?

1단계

df = pd.DataFrame({'text':['h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??']*20000})
df
text
0 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
1 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
2 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
3 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
4 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
... ...
19995 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
19996 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
19997 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
19998 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??
19999 h e l l o . h e l l o ! h e l l o ? h e l l o !! h e l l o ??

20000 rows × 1 columns

TextDataLoaders.from_df??
Signature:
TextDataLoaders.from_df(
    df,
    path='.',
    valid_pct=0.2,
    seed=None,
    text_col=0,
    label_col=1,
    label_delim=None,
    y_block=None,
    text_vocab=None,
    is_lm=False,
    valid_col=None,
    tok_tfm=None,
    tok_text_col='text',
    seq_len=72,
    backwards=False,
    bs=64,
    val_bs=None,
    shuffle=True,
    device=None,
)
Source:   
    @classmethod
    @delegates(DataLoaders.from_dblock)
    def from_df(cls, df, path='.', valid_pct=0.2, seed=None, text_col=0, label_col=1, label_delim=None, y_block=None,
                text_vocab=None, is_lm=False, valid_col=None, tok_tfm=None, tok_text_col="text", seq_len=72, backwards=False, **kwargs):
        "Create from `df` in `path` with `valid_pct`"
        blocks = [TextBlock.from_df(text_col, text_vocab, is_lm, seq_len, backwards, tok=tok_tfm)]
        if y_block is None and not is_lm:
            blocks.append(MultiCategoryBlock if is_listy(label_col) and len(label_col) > 1 else CategoryBlock)
        if y_block is not None and not is_lm: blocks += (y_block if is_listy(y_block) else [y_block])
        splitter = RandomSplitter(valid_pct, seed=seed) if valid_col is None else ColSplitter(valid_col)
        dblock = DataBlock(blocks=blocks,
                           get_x=ColReader(tok_text_col),
                           get_y=None if is_lm else ColReader(label_col, label_delim=label_delim),
                           splitter=splitter)
        return cls.from_dblock(dblock, df, path=path, seq_len=seq_len, **kwargs)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/text/data.py
Type:      method

is_lm = False

다음 자료를 예측하고 싶을때

is_lm = True

  • classification을 수행하고 싶을 때

  • 생성에 목적

  • is_lm: text의 생성에 관심이 있다면 True로 설정할 것

dls = TextDataLoaders.from_df(df,text_col='text',is_lm=True) 
dls.show_batch()
text text_
0 xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o .
1 ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o
2 ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l
3 o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l
4 l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e
5 l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h
6 e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos
7 h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ?
8 ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ? ? xxbos h e l l o . h e l l o ! h e l l o ? h e l l o ! ! h e l l o ?

위의 결과에서 xxbos는 하나의 내용이 끝나고 다른 내용이 시작된다는 의미

2단계

language_model_learner??
Signature:
language_model_learner(
    dls,
    arch,
    config=None,
    drop_mult=1.0,
    backwards=False,
    pretrained=True,
    pretrained_fnames=None,
    loss_func=None,
    opt_func=<function Adam at 0x7fcb70042550>,
    lr=0.001,
    splitter=<function trainable_params at 0x7fcb79d04940>,
    cbs=None,
    metrics=None,
    path=None,
    model_dir='models',
    wd=None,
    wd_bn_bias=False,
    train_bn=True,
    moms=(0.95, 0.85, 0.95),
)
Source:   
@delegates(Learner.__init__)
def language_model_learner(dls, arch, config=None, drop_mult=1., backwards=False, pretrained=True, pretrained_fnames=None, **kwargs):
    "Create a `Learner` with a language model from `dls` and `arch`."
    vocab = _get_text_vocab(dls)
    model = get_language_model(arch, len(vocab), config=config, drop_mult=drop_mult)
    meta = _model_meta[arch]
    learn = LMLearner(dls, model, loss_func=CrossEntropyLossFlat(), splitter=meta['split_lm'], **kwargs)
    url = 'url_bwd' if backwards else 'url'
    if pretrained or pretrained_fnames:
        if pretrained_fnames is not None:
            fnames = [learn.path/learn.model_dir/f'{fn}.{ext}' for fn,ext in zip(pretrained_fnames, ['pth', 'pkl'])]
        else:
            if url not in meta:
                warn("There are no pretrained weights for that architecture yet!")
                return learn
            model_path = untar_data(meta[url] , c_key='model')
            try: fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
            except IndexError: print(f'The model in {model_path} is incomplete, download again'); raise
        learn = learn.load_pretrained(*fnames)
    return learn
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/text/learner.py
Type:      function

lrnr = language_model_learner(dls, AWD_LSTM)

3단계

lrnr.fit(5)
epoch train_loss valid_loss time
0 1.926327 0.851078 00:15
1 1.030813 0.366239 00:15
2 0.636483 0.251018 00:15
3 0.478914 0.209533 00:15
4 0.431652 0.190482 00:15

4단계

lrnr.predict('h e',n_words=30)
'h e l l o ? h e l l o ! h e l l o ! h e l l o ! h e l l o ? ?'

GAN intro

- 저자: 이안굿펠로우 (이름이 특이함. 좋은친구..) - 천재임 - 지도교수가 요수아 벤지오

- 논문 NIPS, 저는 이 논문 읽고 소름돋았어요.. - https://arxiv.org/abs/1406.2661 (현재시점, 38751회 인용되었음 \(\to\) 48978회 인용..)

- 최근 10년간 머신러닝 분야에서 가장 혁신적인 아이디어이다. (얀르쿤, 2014년 시점..)

- 무슨내용? 생성모형

생성모형이란? (쉬운 설명)

만들수 없다면 이해하지 못한 것이다, 리처드 파인만 (천재 물리학자)

- 사진속에 들어있는 동물이 개인지 고양이인지 맞출수 있는 기계와 개와 고양이를 그릴수 있는 기계중 어떤것이 더 시각적보에 대한 이해가 깊다고 볼수 있는가?

- 진정으로 인공지능이 이미지를 이해했다면, 이미지를 만들수도 있어야 한다. \(\to\) 이미지를 생성하는 모형을 만들어보자 \(\to\) 성공

GAN의 응용분야

- 내가 찍은 사진이 피카소의 화풍으로 표현된다면? - https://www.lgsl.kr/sto/stories/60/ALMA2020070001

- 퀸의 라이브에이드가 4k로 나온다면?

- 1920년대 서울의 모습이 칼라로 복원된다면?

- 딥페이크: 유명인의 가짜 포르노, 가짜뉴스, 협박(거짓기소)

- 게임영상 (파이널판타지)

- 거북이의 커버..

- 너무 많아요…..

생성모형이란? 통계학과 버전의 설명

제한된 정보만으로 어떤 문제를 풀 때, 그 과정에서 원래의 문제보다 일반적인 문제를 풀지 말고, 가능한 원래의 문제를 직접 풀어야한다. 배프닉 (SVM 창시자)

- 이미지 \(\boldsymbol{x}\)가 주어졌을 경우 라벨을 \(y\)라고 하자.

- 이미지를 보고 라벨을 맞추는 일은 \(p(y| \boldsymbol{x})\)에 관심이 있다.

- 이미지를 생성하는 일은 \(p(\boldsymbol{x},y)\)에 관심이 있는것이다.

y의 평균적인 확률이 나올떄 x로 y 를 예측할 수 있다고 한단

- 데이터의 생성확률 \(p(\boldsymbol{x},y)\)을 알면 클래스의 사후확률 \(p(y|\boldsymbol{x})\)를 알 수 있음. (아래의 수식 참고) 하지만 역은 불가능

\[p(y|x) = \frac{p(x,y)}{p(x)} = \frac{p(x,y)}{\sum_{y}p(x,y)} \]

  • 즉 이미지를 생성하는일은 분류문제보다 더 어려운 일이라 해석가능

분류할 수 았다는게 생성할 수 있다는 건 아니니까

- 따라서 배프닉의 원리에 의하면 식별적 분류가 생성적 분류보다 바람직한 접근법이라 할 수 있음.

- 하지만 다양한 현실문제에서 생성모형이 유용할때가 많다.

GAN의 원리

- GAN은 생성모형중 하나임

- GAN의 원리는 경찰과 위조지폐범이 서로 선의의(?) 경쟁을 통하여 서로 발전하는 모형으로 설명할 수 있다.

The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency. Competition in this game drives both teams to improve their methods until the counterfeits are indistiguishable from the genuine articles.

- 서로 적대적인(adversarial) 네트워크(network)를 동시에 학습시켜 가짜이미지를 만든다(generate)

- 무식한 상황극..

위조범: 가짜돈을 만들어서 부자가 되어야지! (가짜돈을 그림)

경찰: (위조범이 만든 돈을 보고) 이건 가짜다!

위조범: 걸렸군.. 더 정교하게 만들어야지..

경찰: 이건 진짠가?… –> 상사에게 혼남. 그것도 구분못하냐고

위조범: 더 정교하게 만들자..

경찰: 더 판별능력을 업그레이드 하자!

반복..

- 굉장히 우수한 경찰조차도 진짜와 가짜를 구분하지 못할때(=진짜 이미지를 0.5의 확률로만 진짜라고 말할때 = 가짜 이미지를 0.5의 확률로만 가짜라고 말할때) 학습을 멈춘다.

GAN 실습

1단계

path = untar_data(URLs.MNIST_SAMPLE)
DataBlock??
Init signature:
DataBlock(
    blocks=None,
    dl_type=None,
    getters=None,
    n_inp=None,
    item_tfms=None,
    batch_tfms=None,
    *,
    get_items=None,
    splitter=None,
    get_y=None,
    get_x=None,
)
Source:        
class DataBlock():
    "Generic container to quickly build `Datasets` and `DataLoaders`"
    get_x=get_items=splitter=get_y = None
    blocks,dl_type = (TransformBlock,TransformBlock),TfmdDL
    _methods = 'get_items splitter get_y get_x'.split()
    _msg = "If you wanted to compose several transforms in your getter don't forget to wrap them in a `Pipeline`."
    def __init__(self, blocks=None, dl_type=None, getters=None, n_inp=None, item_tfms=None, batch_tfms=None, **kwargs):
        blocks = L(self.blocks if blocks is None else blocks)
        blocks = L(b() if callable(b) else b for b in blocks)
        self.type_tfms = blocks.attrgot('type_tfms', L())
        self.default_item_tfms  = _merge_tfms(*blocks.attrgot('item_tfms',  L()))
        self.default_batch_tfms = _merge_tfms(*blocks.attrgot('batch_tfms', L()))
        for b in blocks:
            if getattr(b, 'dl_type', None) is not None: self.dl_type = b.dl_type
        if dl_type is not None: self.dl_type = dl_type
        self.dataloaders = delegates(self.dl_type.__init__)(self.dataloaders)
        self.dls_kwargs = merge(*blocks.attrgot('dls_kwargs', {}))
        self.n_inp = ifnone(n_inp, max(1, len(blocks)-1))
        self.getters = ifnone(getters, [noop]*len(self.type_tfms))
        if self.get_x:
            if len(L(self.get_x)) != self.n_inp:
                raise ValueError(f'get_x contains {len(L(self.get_x))} functions, but must contain {self.n_inp} (one for each input)\n{self._msg}')
            self.getters[:self.n_inp] = L(self.get_x)
        if self.get_y:
            n_targs = len(self.getters) - self.n_inp
            if len(L(self.get_y)) != n_targs:
                raise ValueError(f'get_y contains {len(L(self.get_y))} functions, but must contain {n_targs} (one for each target)\n{self._msg}')
            self.getters[self.n_inp:] = L(self.get_y)
        if kwargs: raise TypeError(f'invalid keyword arguments: {", ".join(kwargs.keys())}')
        self.new(item_tfms, batch_tfms)
    def _combine_type_tfms(self): return L([self.getters, self.type_tfms]).map_zip(
        lambda g,tt: (g.fs if isinstance(g, Pipeline) else L(g)) + tt)
    def new(self, item_tfms=None, batch_tfms=None):
        self.item_tfms  = _merge_tfms(self.default_item_tfms,  item_tfms)
        self.batch_tfms = _merge_tfms(self.default_batch_tfms, batch_tfms)
        return self
    @classmethod
    def from_columns(cls, blocks=None, getters=None, get_items=None, **kwargs):
        if getters is None: getters = L(ItemGetter(i) for i in range(2 if blocks is None else len(L(blocks))))
        get_items = _zip if get_items is None else compose(get_items, _zip)
        return cls(blocks=blocks, getters=getters, get_items=get_items, **kwargs)
    def datasets(self, source, verbose=False):
        self.source = source                     ; pv(f"Collecting items from {source}", verbose)
        items = (self.get_items or noop)(source) ; pv(f"Found {len(items)} items", verbose)
        splits = (self.splitter or RandomSplitter())(items)
        pv(f"{len(splits)} datasets of sizes {','.join([str(len(s)) for s in splits])}", verbose)
        return Datasets(items, tfms=self._combine_type_tfms(), splits=splits, dl_type=self.dl_type, n_inp=self.n_inp, verbose=verbose)
    def dataloaders(self, source, path='.', verbose=False, **kwargs):
        dsets = self.datasets(source, verbose=verbose)
        kwargs = {**self.dls_kwargs, **kwargs, 'verbose': verbose}
        return dsets.dataloaders(path=path, after_item=self.item_tfms, after_batch=self.batch_tfms, **kwargs)
    _docs = dict(new="Create a new `DataBlock` with other `item_tfms` and `batch_tfms`",
                 datasets="Create a `Datasets` object from `source`",
                 dataloaders="Create a `DataLoaders` object from `source`")
File:           ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/data/block.py
Type:           type
Subclasses:     

TransformBlock
fastai.data.block.TransformBlock
ImageBlock
<function fastai.vision.data.ImageBlock(cls=<class 'fastai.vision.core.PILImage'>)>
generate_noise
<function fastai.vision.gan.generate_noise(fn, size=100)>
dblock = DataBlock(blocks=(TransformBlock,ImageBlock),
          get_x = generate_noise,
          get_items=get_image_files,
          item_tfms=Resize(32))
dls = dblock.dataloaders(path) 
dls.show_batch()

2단계

basic_generator??
Signature:
basic_generator(
    out_size,
    n_channels,
    in_sz=100,
    n_features=64,
    n_extra_layers=0,
    ks=3,
    stride=1,
    padding=None,
    bias=None,
    ndim=2,
    norm_type=<NormType.Batch: 1>,
    bn_1st=True,
    act_cls=<class 'torch.nn.modules.activation.ReLU'>,
    transpose=False,
    init='auto',
    xtra=None,
    bias_std=0.01,
    dilation: Union[int, Tuple[int, int]] = 1,
    groups: int = 1,
    padding_mode: str = 'zeros',
    device=None,
    dtype=None,
)
Source:   
@delegates(ConvLayer.__init__)
def basic_generator(out_size, n_channels, in_sz=100, n_features=64, n_extra_layers=0, **kwargs):
    "A basic generator from `in_sz` to images `n_channels` x `out_size` x `out_size`."
    cur_size, cur_ftrs = 4, n_features//2
    while cur_size < out_size:  cur_size *= 2; cur_ftrs *= 2
    layers = [AddChannels(2), ConvLayer(in_sz, cur_ftrs, 4, 1, transpose=True, **kwargs)]
    cur_size = 4
    while cur_size < out_size // 2:
        layers.append(ConvLayer(cur_ftrs, cur_ftrs//2, 4, 2, 1, transpose=True, **kwargs))
        cur_ftrs //= 2; cur_size *= 2
    layers += [ConvLayer(cur_ftrs, cur_ftrs, 3, 1, 1, transpose=True, **kwargs) for _ in range(n_extra_layers)]
    layers += [nn.ConvTranspose2d(cur_ftrs, n_channels, 4, 2, 1, bias=False), nn.Tanh()]
    return nn.Sequential(*layers)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/vision/gan.py
Type:      function

basic_critic??
Signature:
basic_critic(
    in_size,
    n_channels,
    n_features=64,
    n_extra_layers=0,
    norm_type=<NormType.Batch: 1>,
    ks=3,
    stride=1,
    padding=None,
    bias=None,
    ndim=2,
    bn_1st=True,
    act_cls=<class 'torch.nn.modules.activation.ReLU'>,
    transpose=False,
    init='auto',
    xtra=None,
    bias_std=0.01,
    dilation: Union[int, Tuple[int, int]] = 1,
    groups: int = 1,
    padding_mode: str = 'zeros',
    device=None,
    dtype=None,
)
Source:   
@delegates(ConvLayer.__init__)
def basic_critic(in_size, n_channels, n_features=64, n_extra_layers=0, norm_type=NormType.Batch, **kwargs):
    "A basic critic for images `n_channels` x `in_size` x `in_size`."
    layers = [ConvLayer(n_channels, n_features, 4, 2, 1, norm_type=None, **kwargs)]
    cur_size, cur_ftrs = in_size//2, n_features
    layers += [ConvLayer(cur_ftrs, cur_ftrs, 3, 1, norm_type=norm_type, **kwargs) for _ in range(n_extra_layers)]
    while cur_size > 4:
        layers.append(ConvLayer(cur_ftrs, cur_ftrs*2, 4, 2, 1, norm_type=norm_type, **kwargs))
        cur_ftrs *= 2 ; cur_size //= 2
    init = kwargs.get('init', nn.init.kaiming_normal_)
    layers += [init_default(nn.Conv2d(cur_ftrs, 1, 4, padding=0), init), Flatten()]
    return nn.Sequential(*layers)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/vision/gan.py
Type:      function

counterfeiter = basic_generator(32,n_channels=3,n_extra_layers=1)
police = basic_critic(32,n_channels=3,n_extra_layers=1)
  • 32는 사이즈
  • 채널은 컬러이면 3이지만 이건 흑백이라도 3으로 표현해봄
GANLearner.wgan??
Signature:
GANLearner.wgan(
    dls,
    generator,
    critic,
    switcher=None,
    clip=0.01,
    switch_eval=False,
    gen_first=False,
    show_img=True,
    cbs=None,
    metrics=None,
    loss_func=None,
    opt_func=<function Adam at 0x7fcb70042550>,
    lr=0.001,
    splitter=<function trainable_params at 0x7fcb79d04940>,
    path=None,
    model_dir='models',
    wd=None,
    wd_bn_bias=False,
    train_bn=True,
    moms=(0.95, 0.85, 0.95),
)
Source:   
    @classmethod
    def wgan(cls, dls, generator, critic, switcher=None, clip=0.01, switch_eval=False, **kwargs):
        "Create a WGAN from `data`, `generator` and `critic`."
        if switcher is None: switcher = FixedGANSwitcher(n_crit=5, n_gen=1)
        return cls(dls, generator, critic, _tk_mean, _tk_diff, switcher=switcher, clip=clip, switch_eval=switch_eval, **kwargs)
File:      ~/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/vision/gan.py
Type:      method

lrnr = GANLearner.wgan(dls,counterfeiter,police)

3단계

- lrnr.fit(10) 진행

lrnr.fit(10)
/home/csy/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/callback/core.py:51: UserWarning: You are shadowing an attribute (generator) that exists in the learner. Use `self.learn.generator` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
/home/csy/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/callback/core.py:51: UserWarning: You are shadowing an attribute (critic) that exists in the learner. Use `self.learn.critic` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
/home/csy/anaconda3/envs/csy/lib/python3.8/site-packages/fastai/callback/core.py:51: UserWarning: You are shadowing an attribute (gen_mode) that exists in the learner. Use `self.learn.gen_mode` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
epoch train_loss valid_loss gen_loss crit_loss time
0 -0.543594 0.420719 0.420719 -0.754515 00:04
1 -0.578704 0.376830 0.376830 -0.759121 00:04
2 -0.581667 0.335058 0.335058 -0.765852 00:04
3 -0.581913 0.350422 0.350422 -0.766256 00:04
4 -0.576919 0.239790 0.239790 -0.757070 00:04
5 -0.568187 0.165856 0.165856 -0.738596 00:04
6 -0.561763 0.312277 0.312277 -0.738768 00:04
7 -0.545816 0.312626 0.312626 -0.735054 00:04
8 -0.530404 0.315626 0.315626 -0.713279 00:04
9 -0.552664 0.292665 0.292665 -0.719266 00:04 
lrnr.show_results()

- lrnr.fit(10) 추가로 진행 // 총20회

lrnr.fit(10)
epoch train_loss valid_loss gen_loss crit_loss time
0 -0.534434 0.329432 0.329432 -0.738360 00:04
1 -0.491044 0.241687 0.241687 -0.282900 00:04
2 -0.430823 0.247032 0.247032 -0.631827 00:04
3 -0.509287 0.228638 0.228638 -0.702186 00:04
4 -0.541639 0.306787 0.306787 -0.737486 00:04
5 -0.490239 0.270219 0.270219 -0.686973 00:04
6 -0.456657 0.370165 0.370165 -0.651278 00:04
7 -0.375928 0.254674 0.254674 -0.463629 00:04
8 -0.505262 0.241540 0.241540 -0.706440 00:04
9 -0.511837 0.264010 0.264010 -0.717528 00:04 
lrnr.show_results()

- lrnr.fit(10) 추가로 진행 // 총30회

lrnr.fit(10)
epoch train_loss valid_loss gen_loss crit_loss time
0 -0.389076 0.203898 0.203898 -0.610006 00:04
1 -0.404953 0.248211 0.248211 -0.564691 00:04
2 -0.399689 0.157126 0.157126 -0.475484 00:04
3 -0.412959 0.160083 0.160083 -0.628447 00:04
4 -0.419133 0.140253 0.140253 -0.315640 00:04
5 -0.412665 0.360084 0.360084 -0.504751 00:04
6 -0.419645 0.331901 0.331901 -0.627747 00:04
7 -0.393825 0.099620 0.099620 -0.479805 00:04
8 -0.383802 0.332651 0.332651 -0.485545 00:04
9 -0.329964 0.066743 0.066743 -0.331843 00:04 
lrnr.show_results()

- lrnr.fit(10) 추가로 진행 // 총 60회

lrnr.fit(30)
epoch train_loss valid_loss gen_loss crit_loss time
0 -0.280188 0.083489 0.083489 -0.421842 00:04
1 -0.211743 0.066232 0.066232 -0.485185 00:04
2 -0.548622 0.439374 0.439374 -0.729976 00:04
3 -0.184136 0.166024 0.166024 -0.196536 00:04
4 -0.180048 0.283176 0.283176 -0.343643 00:04
5 -0.082062 -0.111767 -0.111767 -0.232821 00:06
6 -0.134064 -0.252754 -0.252754 -0.056792 00:05
7 -0.024693 -0.019944 -0.019944 -0.060462 00:04
8 -0.067052 -0.176633 -0.176633 -0.087246 00:05
9 -0.051849 0.077216 0.077216 -0.063890 00:04
10 -0.062414 0.576616 0.576616 0.007565 00:04
11 -0.028339 -0.177623 -0.177623 -0.165999 00:04
12 -0.285967 0.256777 0.256777 -0.486597 00:04
13 -0.040034 0.200722 0.200722 -0.002714 00:05
14 -0.082319 -0.218245 -0.218245 -0.076548 00:04
15 -0.126090 -0.120631 -0.120631 -0.311712 00:04
16 -0.120472 0.024194 0.024194 -0.165129 00:04
17 -0.213207 0.447029 0.447029 -0.441171 00:04
18 -0.104892 0.076353 0.076353 -0.305898 00:04
19 -0.077636 -0.229590 -0.229590 -0.206540 00:04
20 -0.037347 -0.169197 -0.169197 -0.070061 00:04
21 -0.063813 -0.283316 -0.283316 0.009801 00:04
22 -0.037806 -0.101751 -0.101751 -0.020896 00:04
23 -0.057209 -0.012665 -0.012665 -0.095574 00:04
24 -0.036946 0.090177 0.090177 -0.049521 00:04
25 -0.050363 -0.206716 -0.206716 -0.035576 00:04
26 -0.047856 0.052171 0.052171 -0.017636 00:04
27 -0.009292 -0.027788 -0.027788 -0.003629 00:04
28 -0.032223 -0.223866 -0.223866 -0.011261 00:04
29 -0.028316 -0.006388 -0.006388 -0.024545 00:05 
lrnr.show_results()

4단계 (없음)