IT/AI
텐서플로 2.0 시작하기: 초보자용
심장과영혼
2022. 12. 3. 20:42
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AI를 이용하기 위해서 텐서플로를 사용하기로 결정하고 본격적으로 알아가보기로 합니다.
저는 AI를 이용한 주식투자에 관심이 있습니다.
생각이 확장되는대로 한번 제가 공부해 가는 것을 여기에 올려가 보도록 하겠습니다.
텐서플로 2.0 시작하기: 초보자용
이걸 하려다 보니 일단 저는 환경이 윈도즈인데 WSL(윈도우에서 리눅스를 지원하는 환경)이 생겼고, 그 환경에서 텐서플로의 원활한 지원이 된다고 해서 먼저 WSL의 구축을 하였습니다. 저와는 다른 환경에 계신분들은 또 다른 방법으로 환경을 구축하시면 되겠습니다.
이 짧은 소개 글은 Keras를 사용하여 다음을 수행합니다.
- 사전에 빌드한 데이터세트를 로드합니다.
- 이미지를 분류하는 신경망 머신 러닝 모델을 빌드합니다.
- 이 신경망을 훈련합니다.
- 모델의 정확도를 평가합니다.
TensorFlow 설정하기
시작하려면 TensorFlow를 프로그램으로 가져옵니다.
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
각 명령문의 실행결과는 아래쪽 이미지에 다 올려놓았습니다.
MNIST 데이터셋을 로드하여 준비합니다. 샘플 값을 정수에서 부동소수로 변환합니다:
참고: 자체 개발 환경을 사용하는 경우에 TensorFlow 2 패키지를 설치하려면 최신 pip로 업그레이드했는지 확인합니다. 자세한 내용은 설치 가이드를 참조합니다.
데이터세트 로드하기
MNIST 데이터세트를 로드하고 준비합니다. 샘플 데이터를 정수에서 부동 소수점 숫자로 변환합니다.
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
머신 러닝 모델 빌드하기
층을 차례대로 쌓아 tf.keras.Sequential 모델을 만듭니다. 훈련에 사용할 옵티마이저(optimizer)와 손실 함수를 선택합니다:
모델의 구축에 대한 방법은 오늘 주제는 아닙니다. 오늘은 전반적으로 진행하는 방법에 대한 예시입니다.
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
[3]
predictions = model(x_train[:1]).numpy()
predictions
tf.nn.softmax 함수는 다음과 같이 이러한 로짓을 각 클래스에 대한 확률로 변환합니다.
tf.nn.softmax(predictions).numpy()
참고: tf.nn.softmax 함수를 네트워크의 마지막 레이어에 대한 활성화 함수로 베이킹할 수 있습니다. 이렇게 하면 모델 출력을 더 직접적으로 해석할 수 있지만 이 접근법은 소프트맥스 출력을 사용할 경우 모든 모델에 대해 정확하고 수치적으로 안정적인 손실 계산을 제공하는 것이 불가능하므로 권장하지 않습니다.
losses.SparseCategoricalCrossentropy를 사용하여 로짓의 벡터와 True 인덱스를 사용하고 각 예시에 대해 스칼라 손실을 반환하는 훈련용 손실 함수를 정의합니다.
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
이 손실은 실제 클래스의 음의 로그 확률과 같습니다. 모델이 올바른 클래스를 확신하는 경우 손실은 0입니다.
이 훈련되지 않은 모델은 무작위에 가까운 확률(각 클래스에 대해 1/10)을 제공하므로 초기 손실은 -tf.math.log(1/10) ~= 2.3에 근접해야 합니다.
loss_fn(y_train[:1], predictions).numpy()
훈련을 시작하기 전에 Keras Model.compile을 사용하여 모델을 구성하고 컴파일합니다. optimizer 클래스를 adam으로 설정하고 loss를 앞에서 정의한 loss_fn 함수로 설정합니다. metrics 매개변수를 accuracy로 설정하여 모델에 대해 평가할 메트릭을 지정합니다.
model.compile(optimizer='adam',
loss=loss_fn,
metrics=['accuracy'])
모델 훈련 및 평가하기
모델을 훈련하고 평가합니다:
model.fit(x_train, y_train, epochs=5)
Model.evaluate 메서드는 일반적으로 "Validation-set" 또는 "Test-set"에서 모델 성능을 확인합니다.
훈련된 이미지 분류기는 이 데이터셋에서 약 98%의 정확도를 달성합니다.
모델이 확률을 반환하도록 하려면 다음과 같이 훈련된 모델을 래핑하고 여기에 소프트맥스를 첨부할 수 있습니다.
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WSL 환경에서 실행한 결과
WINDOWS에서 WSL을 실행합니다.
WSL의 설치와 실행은 아래를 참고하세요
2022.12.03 - [IT] - WSL을 사용하여 Windows에 Linux 설치
WSL을 사용하여 Windows에 Linux 설치
그냥 윈도우에서 리눅스를 쓸수 있는 게 더 편해졌습니다. 저도 하다가 몇가지 시행착오가 있어서 여기 글을 올려놓습니다. ================================================== WSL(WSL 2)을 사용하여 Windows에 Li
iotnbigdata.tistory.com
2022.12.03 - [IT] - WSL/WSL 2 개발 환경 설정하기
WSL/WSL 2 개발 환경 설정하기
WSL에 대해서 조금 더 알아봅니다. ========================================== WSL 개발 환경을 설정하기 위한 모범 사례에 대한 단계별 가이드입니다. 명령을 실행하여 Ubuntu를 사용하거나 다른 Linux 배포를
iotnbigdata.tistory.com
이제 WSL을 실행합니다.
`PS C:\Users\boas9> wsl
one@DESKTOP-G28A5LG:/mnt/c/Users/boas9$ ls -al
total 1259132
drwxrwxrwx 1 root root 4096 Dec 3 18:43 .
dr-xr-xr-x 1 root root 4096 Dec 3 02:20 ..
drwxrwxrwx 1 root root 4096 Nov 5 21:14 .android
drwxrwxrwx 1 root root 4096 Jul 4 00:55 .cache
drwxrwxrwx 1 root root 4096 Jul 4 00:55 .eclipse
drwxrwxrwx 1 root root 4096 Dec 3 18:23 .idlerc
drwxrwxrwx 1 root root 4096 Dec 3 18:41 .ipynb_checkpoints
drwxrwxrwx 1 root root 4096 Dec 3 18:41 .ipython
drwxrwxrwx 1 root root 4096 Jul 4 01:02 .lemminx
drwxrwxrwx 1 root root 4096 Jul 4 01:01 .m2
drwxrwxrwx 1 root root 4096 Jul 23 16:25 .vscode
-rwxrwxrwx 1 root root 32 Jul 23 17:09 .vuerc
drwxrwxrwx 1 root root 4096 Dec 3 02:41 '3D Objects'
drwxrwxrwx 1 root root 4096 Dec 3 02:18 AppData
lrwxrwxrwx 1 root root 34 Dec 3 02:12 'Application Data' -> /mnt/c/Users/boas9/AppData/Roaming
drwxrwxrwx 1 root root 4096 Dec 3 02:41 Contacts
lrwxrwxrwx 1 root root 62 Dec 3 02:12 Cookies -> /mnt/c/Users/boas9/AppData/Local/Microsoft/Windows/INetCookies
drwxrwxrwx 1 root root 4096 Dec 3 18:08 Desktop
drwxrwxrwx 1 root root 4096 Dec 3 02:41 Documents
drwxrwxrwx 1 root root 4096 Dec 3 18:23 Downloads
drwxrwxrwx 1 root root 4096 Dec 3 02:41 Favorites
drwxrwxrwx 1 root root 4096 Dec 3 16:34 IntelGraphicsProfiles
drwxrwxrwx 1 root root 4096 Dec 3 02:41 Links
lrwxrwxrwx 1 root root 32 Dec 3 02:12 'Local Settings' -> /mnt/c/Users/boas9/AppData/Local
drwxrwxrwx 1 root root 4096 Dec 3 02:41 Music
lrwxrwxrwx 1 root root 28 Dec 3 02:12 'My Documents' -> /mnt/c/Users/boas9/Documents
-rwxrwxrwx 1 root root 1835008 Dec 3 16:33 NTUSER.DAT
-rwxrwxrwx 1 root root 65536 Dec 3 02:12 NTUSER.DAT{62e8232f-7263-11ed-9e2c-e51e056272fb}.TM.blf
-rwxrwxrwx 1 root root 524288 Dec 3 02:12 NTUSER.DAT{62e8232f-7263-11ed-9e2c-e51e056272fb}.TMContainer00000000000000000001.regtrans-ms
-rwxrwxrwx 1 root root 524288 Dec 3 02:12 NTUSER.DAT{62e8232f-7263-11ed-9e2c-e51e056272fb}.TMContainer00000000000000000002.regtrans-ms
lrwxrwxrwx 1 root root 70 Dec 3 02:12 NetHood -> '/mnt/c/Users/boas9/AppData/Roaming/Microsoft/Windows/Network Shortcuts'
drwxrwxrwx 1 root root 4096 Dec 3 02:42 OneDrive
drwxrwxrwx 1 root root 4096 Dec 3 17:27 Pictures
drwxrwxrwx 1 root root 4096 Jul 4 02:09 Postman
-rwxrwxrwx 1 root root 146333528 Jul 1 23:35 Postman-win64-9.22.2-Setup.exe
lrwxrwxrwx 1 root root 70 Dec 3 02:12 PrintHood -> '/mnt/c/Users/boas9/AppData/Roaming/Microsoft/Windows/Printer Shortcuts'
lrwxrwxrwx 1 root root 59 Dec 3 02:12 Recent -> /mnt/c/Users/boas9/AppData/Roaming/Microsoft/Windows/Recent
drwxrwxrwx 1 root root 4096 Dec 3 02:41 'Saved Games'
drwxrwxrwx 1 root root 4096 Dec 3 02:42 Searches
lrwxrwxrwx 1 root root 59 Dec 3 02:12 SendTo -> /mnt/c/Users/boas9/AppData/Roaming/Microsoft/Windows/SendTo
lrwxrwxrwx 1 root root 62 Dec 3 02:12 Templates -> /mnt/c/Users/boas9/AppData/Roaming/Microsoft/Windows/Templates
-rwxrwxrwx 1 root root 2458 Dec 3 18:43 Untitled.ipynb
drwxrwxrwx 1 root root 4096 Dec 3 02:41 Videos
-rwxrwxrwx 1 root root 518729147 Jul 3 20:48 eclipse-jee-2022-06-R-win32-x86_64.zip
-rwxrwxrwx 1 root root 159469568 Apr 9 2022 jdk-18_windows-x64_bin.msi
-rwxrwxrwx 1 root root 461000704 Jul 3 20:40 mysql-installer-community-8.0.29.0.msi
-rwxrwxrwx 1 root root 294912 Dec 3 02:12 ntuser.dat.LOG1
-rwxrwxrwx 1 root root 569344 Dec 3 02:12 ntuser.dat.LOG2
-rwxrwxrwx 1 root root 20 Dec 3 02:41 ntuser.ini
drwxrwxrwx 1 root root 4096 Jul 4 00:54 sts4
one@DESKTOP-G28A5LG:/mnt/c/Users/boas9$
> WSL 환경에 파이썬을 설치합니다.
one@DESKTOP-G28A5LG:/mnt/c/Users/boas9$ sudo apt-get install python3-pip
[sudo] password for one:
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
libfwupdplugin1 libxmlb1
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
binutils binutils-common binutils-x86-64-linux-gnu build-essential cpp cpp-9 dpkg-dev
fakeroot g++ g++-9 gcc gcc-9 gcc-9-base libalgorithm-diff-perl
libalgorithm-diff-xs-perl libalgorithm-merge-perl libasan5 libatomic1 libbinutils
libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libctf-nobfd0 libctf0 libdpkg-perl
libexpat1-dev libfakeroot libfile-fcntllock-perl libgcc-9-dev libgomp1 libisl22
libitm1 liblsan0 libmpc3 libpython3-dev libpython3.8-dev libquadmath0 libstdc++-9-dev
libtsan0 libubsan1 linux-libc-dev make manpages-dev python-pip-whl python3-dev
python3-wheel python3.8-dev zlib1g-dev
Suggested packages:
binutils-doc cpp-doc gcc-9-locales debian-keyring g++-multilib g++-9-multilib
gcc-9-doc gcc-multilib autoconf automake libtool flex bison gdb gcc-doc gcc-9-multilib
glibc-doc bzr libstdc++-9-doc make-doc
The following NEW packages will be installed:
binutils binutils-common binutils-x86-64-linux-gnu build-essential cpp cpp-9 dpkg-dev
fakeroot g++ g++-9 gcc gcc-9 gcc-9-base libalgorithm-diff-perl
libalgorithm-diff-xs-perl libalgorithm-merge-perl libasan5 libatomic1 libbinutils
libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libctf-nobfd0 libctf0 libdpkg-perl
libexpat1-dev libfakeroot libfile-fcntllock-perl libgcc-9-dev libgomp1 libisl22
libitm1 liblsan0 libmpc3 libpython3-dev libpython3.8-dev libquadmath0 libstdc++-9-dev
libtsan0 libubsan1 linux-libc-dev make manpages-dev python-pip-whl python3-dev
python3-pip python3-wheel python3.8-dev zlib1g-dev
0 upgraded, 50 newly installed, 0 to remove and 0 not upgraded.
Need to get 52.2 MB of archives.
After this operation, 228 MB of additional disk space will be used.
Do you want to continue? [Y/n] y
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Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 make amd64 4.2.1-1.2 [162 kB]
Get:32 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdpkg-perl all 1.19.7ubuntu3.2 [231 kB]
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Get:35 http://archive.ubuntu.com/ubuntu focal/main amd64 libfakeroot amd64 1.24-1 [25.7 kB]
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Get:37 http://archive.ubuntu.com/ubuntu focal/main amd64 libalgorithm-diff-perl all 1.19.03-2 [46.6 kB]
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Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1-dev amd64 2.2.9-1ubuntu0.6 [116 kB]
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Get:42 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-dev amd64 3.8.10-0ubuntu1~20.04.5 [3951 kB]
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WSL 환경에 텐서플로를 설치합니다.
one@DESKTOP-G28A5LG:/mnt/c/Users/boas9$ python3 -m pip install tensorflow
Collecting tensorflow
Downloading tensorflow-2.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (588.3 MB)
|████████████████████████████████| 588.3 MB 70 kB/s
Collecting protobuf<3.20,>=3.9.2
Downloading protobuf-3.19.6-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)
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Collecting tensorboard<2.12,>=2.11
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Collecting keras<2.12,>=2.11.0
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Collecting tensorflow-io-gcs-filesystem>=0.23.1; platform_machine != "arm64" or platform_system != "Darwin"
Downloading tensorflow_io_gcs_filesystem-0.28.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.4 MB)
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Collecting typing-extensions>=3.6.6
Downloading typing_extensions-4.4.0-py3-none-any.whl (26 kB)
Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.14.0)
Collecting google-pasta>=0.1.1
Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB)
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Collecting flatbuffers>=2.0
Downloading flatbuffers-22.11.23-py2.py3-none-any.whl (26 kB)
Collecting astunparse>=1.6.0
Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting grpcio<2.0,>=1.24.3
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Collecting absl-py>=1.0.0
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Collecting opt-einsum>=2.3.2
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Collecting tensorflow-estimator<2.12,>=2.11.0
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Collecting h5py>=2.9.0
Downloading h5py-3.7.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
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Collecting numpy>=1.20
Downloading numpy-1.23.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)
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Collecting packaging
Downloading packaging-21.3-py3-none-any.whl (40 kB)
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Collecting wrapt>=1.11.0
Downloading wrapt-1.14.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (81 kB)
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Collecting termcolor>=1.1.0
Downloading termcolor-2.1.1-py3-none-any.whl (6.2 kB)
Collecting gast<=0.4.0,>=0.2.1
Downloading gast-0.4.0-py3-none-any.whl (9.8 kB)
Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from tensorflow) (45.2.0)
Collecting libclang>=13.0.0
Downloading libclang-14.0.6-py2.py3-none-manylinux2010_x86_64.whl (14.1 MB)
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Collecting werkzeug>=1.0.1
Downloading Werkzeug-2.2.2-py3-none-any.whl (232 kB)
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Collecting google-auth<3,>=1.6.3
Downloading google_auth-2.15.0-py2.py3-none-any.whl (177 kB)
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Requirement already satisfied: wheel>=0.26 in /usr/lib/python3/dist-packages (from tensorboard<2.12,>=2.11->tensorflow) (0.34.2)
Collecting tensorboard-plugin-wit>=1.6.0
Downloading tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)
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Collecting markdown>=2.6.8
Downloading Markdown-3.4.1-py3-none-any.whl (93 kB)
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Collecting google-auth-oauthlib<0.5,>=0.4.1
Downloading google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)
Collecting tensorboard-data-server<0.7.0,>=0.6.0
Downloading tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)
|████████████████████████████████| 4.9 MB 42.1 MB/s
Requirement already satisfied: requests<3,>=2.21.0 in /usr/lib/python3/dist-packages (from tensorboard<2.12,>=2.11->tensorflow) (2.22.0)
Collecting pyparsing!=3.0.5,>=2.0.2
Downloading pyparsing-3.0.9-py3-none-any.whl (98 kB)
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Collecting MarkupSafe>=2.1.1
Downloading MarkupSafe-2.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/lib/python3/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (0.2.1)
Collecting rsa<5,>=3.1.4; python_version >= "3.6"
Downloading rsa-4.9-py3-none-any.whl (34 kB)
Collecting cachetools<6.0,>=2.0.0
Downloading cachetools-5.2.0-py3-none-any.whl (9.3 kB)
Collecting importlib-metadata>=4.4; python_version < "3.10"
Downloading importlib_metadata-5.1.0-py3-none-any.whl (21 kB)
Collecting requests-oauthlib>=0.7.0
Downloading requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)
Requirement already satisfied: pyasn1>=0.1.3 in /usr/lib/python3/dist-packages (from rsa<5,>=3.1.4; python_version >= "3.6"->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (0.4.2)
Requirement already satisfied: zipp>=0.5 in /usr/lib/python3/dist-packages (from importlib-metadata>=4.4; python_version < "3.10"->markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow) (1.0.0)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow) (3.1.0)
Installing collected packages: protobuf, MarkupSafe, werkzeug, grpcio, rsa, cachetools, google-auth, tensorboard-plugin-wit, absl-py, importlib-metadata, markdown, requests-oauthlib, google-auth-oauthlib, tensorboard-data-server, numpy, tensorboard, keras, tensorflow-io-gcs-filesystem, typing-extensions, google-pasta, flatbuffers, astunparse, opt-einsum, tensorflow-estimator, h5py, pyparsing, packaging, wrapt, termcolor, gast, libclang, tensorflow
WARNING: The scripts pyrsa-decrypt, pyrsa-encrypt, pyrsa-keygen, pyrsa-priv2pub, pyrsa-sign and pyrsa-verify are installed in '/home/one/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The script markdown_py is installed in '/home/one/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The script google-oauthlib-tool is installed in '/home/one/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The scripts f2py, f2py3 and f2py3.8 are installed in '/home/one/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The script tensorboard is installed in '/home/one/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The scripts estimator_ckpt_converter, import_pb_to_tensorboard, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert, toco and toco_from_protos are installed in '/home/one/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed MarkupSafe-2.1.1 absl-py-1.3.0 astunparse-1.6.3 cachetools-5.2.0 flatbuffers-22.11.23 gast-0.4.0 google-auth-2.15.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.51.1 h5py-3.7.0 importlib-metadata-5.1.0 keras-2.11.0 libclang-14.0.6 markdown-3.4.1 numpy-1.23.5 opt-einsum-3.3.0 packaging-21.3 protobuf-3.19.6 pyparsing-3.0.9 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.11.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.11.0 tensorflow-estimator-2.11.0 tensorflow-io-gcs-filesystem-0.28.0 termcolor-2.1.1 typing-extensions-4.4.0 werkzeug-2.2.2 wrapt-1.14.1
> 파이썬을 실행합니다.
one@DESKTOP-G28A5LG:/mnt/c/Users/boas9$ python3
Python 3.8.10 (default, Jun 22 2022, 20:18:18)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
위 예제 문장들을 차례대로 실행하고 결과값을 확인합니다.
>>> import tensorflow as tf
2022-12-03 18:55:49.020539: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-03 18:55:49.132243: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2022-12-03 18:55:49.136025: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-12-03 18:55:49.136071: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-12-03 18:55:49.739860: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-03 18:55:49.739943: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-03 18:55:49.739950: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
>>> print("TensorFlow version:", tf.__version__)
TensorFlow version: 2.11.0
>>> mnist = tf.keras.datasets.mnist
>>> (x_train, y_train), (x_test, y_test) = mnist.load_data()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 0s 0us/step
>>> x_train, x_test = x_train / 255.0, x_test / 255.0
>>> model = tf.keras.models.Sequential([
... tf.keras.layers.Flatten(input_shape=(28, 28)),
... tf.keras.layers.Dense(128, activation='relu'),
... tf.keras.layers.Dropout(0.2),
... tf.keras.layers.Dense(10, activation='softmax')
... ])
2022-12-03 19:06:19.863517: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-12-03 19:06:19.863564: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2022-12-03 19:06:19.863579: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (DESKTOP-G28A5LG): /proc/driver/nvidia/version does not exist
2022-12-03 19:06:19.863798: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
>>> model.compile(optimizer='adam',
... loss='sparse_categorical_crossentropy',
... metrics=['accuracy'])
>>> model.fit(x_train, y_train, epochs=5)
Epoch 1/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.2943 - accuracy: 0.9144
Epoch 2/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.1424 - accuracy: 0.9574
Epoch 3/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.1085 - accuracy: 0.9665
Epoch 4/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0895 - accuracy: 0.9718
Epoch 5/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0753 - accuracy: 0.9759
<keras.callbacks.History object at 0x7fe6ac7e42e0>
>>> model.fit(x_train, y_train, epochs=5)
.evaluate(x_test, y_test, verbose=2)Epoch 1/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0657 - accuracy: 0.9792
Epoch 2/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0589 - accuracy: 0.9809
Epoch 3/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0536 - accuracy: 0.9826
Epoch 4/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0494 - accuracy: 0.9837
Epoch 5/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0448 - accuracy: 0.9852
<keras.callbacks.History object at 0x7fe6584afc10>
>>>
>>> model.evaluate(x_test, y_test, verbose=2)
313/313 - 1s - loss: 0.0738 - accuracy: 0.9785 - 503ms/epoch - 2ms/step
[0.07380447536706924, 0.9785000085830688]
>>> probability_model = tf.keras.Sequential([
... model,
... tf.keras.layers.Softmax()
... ])
>>> probability_model(x_test[:5])
<tf.Tensor: shape=(5, 10), dtype=float32, numpy=
array([[0.08533678, 0.08533677, 0.08533684, 0.08533688, 0.08533677,
0.08533677, 0.08533677, 0.23196891, 0.08533677, 0.08533679],
[0.08533682, 0.08533734, 0.23196809, 0.08533682, 0.08533682,
0.08533682, 0.08533682, 0.08533682, 0.08533682, 0.08533682],
[0.08534653, 0.2318142 , 0.08534732, 0.08534654, 0.08534744,
0.08534654, 0.08534655, 0.08541019, 0.08534808, 0.08534655],
[0.231831 , 0.08534548, 0.0854026 , 0.08534565, 0.08534549,
0.08534551, 0.08534762, 0.08534563, 0.08534551, 0.08534557],
[0.08533975, 0.08533959, 0.08533959, 0.08533959, 0.2319243 ,
0.08533959, 0.0853396 , 0.08534116, 0.08533959, 0.08535728]],
dtype=float32)>
여러분의 발전을 기원합니다.
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