From 0685116e08bb9f4aa705311160be05193cab2d8a Mon Sep 17 00:00:00 2001 From: mike2ox Date: Sun, 21 Oct 2018 01:24:57 +0900 Subject: [PATCH 01/21] =?UTF-8?q?#32=20:=20master=EC=97=90=20=EC=9E=88?= =?UTF-8?q?=EB=8A=94=20issue=5F32=20commit=20=EC=82=AD=EC=A0=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...g_a_simple_keras_deep_learning_rest_api.md | 287 ------------------ media/32_0.png | Bin 37495 -> 0 bytes media/32_1.png | Bin 33106 -> 0 bytes media/32_2.jpg | Bin 67904 -> 0 bytes 4 files changed, 287 deletions(-) delete mode 100644 32_building_a_simple_keras_deep_learning_rest_api.md delete mode 100644 media/32_0.png delete mode 100644 media/32_1.png delete mode 100644 media/32_2.jpg diff --git a/32_building_a_simple_keras_deep_learning_rest_api.md b/32_building_a_simple_keras_deep_learning_rest_api.md deleted file mode 100644 index 327f151..0000000 --- a/32_building_a_simple_keras_deep_learning_rest_api.md +++ /dev/null @@ -1,287 +0,0 @@ -## Keras 모델을 REST API로 배포해보기(Building a simple Keras + deep learning REST API) -[원문](https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html) -> 이 글은 Adrian Rosebrock이 작성한 안내 게시글로 Keras 모델을 REST API로 제작하는 간단한 방법을 안내하고 있습니다. - -* Keras -* REST API -* flask - -### 개요 -이번 튜토리얼에서, 우리는 케라스 모델을 가지고 REST API로 베포하는 간단한 방법을 설명합니다. - -이 글에 있는 예시들은 자체 딥러닝 API를 구축하는 템플릿/스타트 포인트 역할을 합니다. 코드를 확장하고 API 엔드포인트의 확장성과 견고성이 얼마나 필요한지에 따라 코드를 맞춤화할 수 있습니다. - -특히, 아래 항목들을 배울 수 있습니다 : -- 인퍼런스(inference)에 효율적으로 사용되도록 Keras 모델을 메모리에 불러오는(혹은 불러오지 않는) 방법 -- Flask 웹 프레임워크를 사용하여 API 엔드포인트를 만드는 방법 -- JSON-ify 모델을 사용하여 예측하고 클라이언트에게 결과를 반환하는 방법 -- cURL과 python을 사용하여 Keras REST API를 호출하는 방법 - -이번 튜토리얼이 끝날 때, Keras REST API를 만드는데 사용되는 구성 요소들을 잘 이해할 것입니다. - -이번 튜토리얼에서 제시한 코드를 자신만의 딥러닝 REST API의 스타트 포인트로 자유롭게 사용하세요. - -**참고 : 이번 글에서 다루는 방법은 교육용입니다. 고로 생산 수준이나 과부하 상태에서 확장할 수 있는 건 아닙니다. 만약 메세지 큐와 배치 기능을 활용한 Keras REST API를 해보고 싶다면 [이 튜토리얼](https://www.pyimagesearch.com/2018/01/29/scalable-keras-deep-learning-rest-api/)을 참조하세요.** - ---- -#### 개발 환경 구축 -우선 Keras가 컴퓨터에 이미 구성 / 설치되어 있다고 가정하려 합니다. 만약 아닐 경우, [공식 설치 지침](https://keras.io/#installation)에 따라 Keras를 설치하세요. - -여기서부터, python 웹 프레임워크인 [Flask](http://flask.pocoo.org/)를 설치해야 API 엔드포인트를 구축할 수 있습니다. 또한, API도 사용할 수 있도록 [요청](http://docs.python-requests.org/en/master/)이 필요합니다. - -관련 pip 설치 명령어는 다음과 같습니다. - -```bash - $ pip install flask gevent requests pillow -``` - -#### Keras REST API 설계 -우리의 Keras REST API는 `run_keras_server.py`라는 단일 파일에 자체적으로 포함되어 있습니다. 단순화를 위해 단일 파일안에 설치하도록 했습니다. 구현도 쉽게 모듈화 할 수 있습니다. - -`run_keras_server.py`에서 3가지 함수를 발견하실 수 있습니다 : -- `load_model` : 학습된 Keras 모델을 불러오고 인퍼런스를 위해 준비하는데 사용합니다. -- `prepare_image` : 이 함수는 예측을 위해 입력 이미지를 신경망을 통해 전달하기 전에 작동합니다. 만약 이미지 데이터로 작업하지 않는다면, 파일 이름이 더 일반적인 `prepare_datapoint`로 변경하고 필요한 경우 스케일링/정규화를 적용하는 것이 좋습니다. -- `predict` : 요청에서 수신 데이터를 분류하고 결과를 클라이언트에게 반환할 API의 실제 엔트포인트입니다. - -이번 튜토리얼의 전체 코드는 [이곳](https://github.com/jrosebr1/simple-keras-rest-api)에서 보실 수 있습니다. - -```python -# 필수 패키지를 import합니다. -from keras.applications import ResNet50 -from keras.preprocessing.image import img_to_array -from keras.applications import imagenet_utils -from PIL import Image -import numpy as np -import flask -import io - -# Flask 애플리케이션과 Keras 모델을 초기화합니다. -app = flask.Flask(__name__) -model = None -``` - -첫 번째 코드 조각은 필요한 패키지를 가져오고 Flask 애플리케이션과 Keras 모델을 초기화합니다. - -아래는 `load_model` 함수 정의입니다. - -```python -def load_model(): - # 미리 학습된 Keras 모델을 불러옵니다(여기서 우리는 ImageNet으로 학습되고 - # Keras에서 제공하는 모델을 사용합니다. 하지만 쉽게 하기위해 - # 당신이 설계한 신경망으로 대체할 수 있습니다.) - global model - model = ResNet50(weights="imagenet") -``` -함수 이름에서 알 수 있듯이, 이 함수는 신경망을 인스턴스화하고 디스크에서 가중치를 불러오는 역할을 띕니다. - -단순하게 하기 위해, ImageNet 데이터 세트로 미리 학삽된 ResNet50 구조를 활용하려 합니다. - -클라이언트로부터 오는 데이터를 예측하기 전에, 사전에 데이터를 준비하고 처리하는 과정이 필요합니다. - -```python -def prepare_image(image, target): - # 만약 이미지가 RGB가 아니라면, RGB로 변환해줍니다. - if image.mode != "RGB": - image = image.convert("RGB") - - # 입력 이미지 사이즈를 재정의하고 사전 처리를 진행합니다. - image = image.resize(target) - image = img_to_array(image) - image = np.expand_dims(image, axis=0) - image = imagenet_utils.preprocess_input(image) - - # 처리된 이미지를 반환합니다. - return image -``` -이 함수는 : -- 입력 이미지를 받고 -- (필요하다면) RGB로 이미지를 변환하고 -- 224x224 픽셀 사이즈로 이미지를 재정의하고 (ResNet의 입력 차원에 맞게) -- 평균 감산과 스케일링을 통해 배열 사전 처리합니다. - -다시 말해, 모델을 통해 입력 데이터를 전달하기 전에 필요한 사전 처리, 스케일링, 정규화를 기반으로 함수를 수정해야 합니다. - -이제 `predict` 함수를 정의할 준비가 됐습니다. 이 함수는 `/predict` 엔트포인트로 어떤 요청들을 처리합니다. - -```python -@app.route("/predict", methods=["POST"]) -def predict(): - # initialize the data dictionary that will be returned from the - # view - data = {"success": False} - - # ensure an image was properly uploaded to our endpoint - if flask.request.method == "POST": - if flask.request.files.get("image"): - # read the image in PIL format - image = flask.request.files["image"].read() - image = Image.open(io.BytesIO(image)) - - # preprocess the image and prepare it for classification - image = prepare_image(image, target=(224, 224)) - - # classify the input image and then initialize the list - # of predictions to return to the client - preds = model.predict(image) - results = imagenet_utils.decode_predictions(preds) - data["predictions"] = [] - - # loop over the results and add them to the list of - # returned predictions - for (imagenetID, label, prob) in results[0]: - r = {"label": label, "probability": float(prob)} - data["predictions"].append(r) - - # indicate that the request was a success - data["success"] = True - - # return the data dictionary as a JSON response - return flask.jsonify(data) -``` -`data` 딕셔너리는 클라이언트에게 반환하길 희망하는 데이터를 저장하는데 사용합니다. 이 함수엔 예측의 성공 여부를 나타내는 부울을 가지고 있습니다. 또한, 이 딕셔너리를 사용하여 들어오는 데이터에 대한 예측 결과를 저장합니다. - -들어오는 데이터를 승인하기위해 다음 사항을 확인해야 합니다: - -- 요청 방법은 POST(이미지, JSON, 인코딩된 데이터 등을 포함하여 엔트포인트로 임의의 데이터를 보낼수 있도록 함)입니다. -- POST를 통해 이미지가 파일 속성으로 전달되었습니다. - -그런 다음 그 데이터를 가지고 다음을 진행합니다: -- PIL 형식으로 읽어옵니다. -- 사전 처리를 처리합니다. -- 신경망을 통해 데이터를 전달합니다. -- 결과를 반복하고 그 결과들을 각각 `data["predictions"]`에 추가합니다. -- JSON 형태으로 클라이언트에게 응답을 반환합니다. - -```python -# if this is the main thread of execution first load the model and -# then start the server -if __name__ == "__main__": - print(("* Loading Keras model and Flask starting server..." - "please wait until server has fully started")) - load_model() - app.run() -``` - -#### REST API에서 Keras 모델을 불러오지 않는 방법 - -```python -# ensure an image was properly uploaded to our endpoint -if request.method == "POST": - if request.files.get("image"): - # read the image in PIL format - image = request.files["image"].read() - image = Image.open(io.BytesIO(image)) - - # preprocess the image and prepare it for classification - image = prepare_image(image, target=(224, 224)) - - # load the model - model = ResNet50(weights="imagenet") - - # classify the input image and then initialize the list - # of predictions to return to the client - preds = model.predict(image) - results = imagenet_utils.decode_predictions(preds) - data["predictions"] = [] -``` - - -#### Keras REST API를 시작하기 - -```bash -$ python run_keras_server.py -Using TensorFlow backend. - * Loading Keras model and Flask starting server...please wait until server has fully started -... - * Running on http://127.0.0.1:5000 -``` - - - -![Not Found](https://raw.githubusercontent.com/KerasKorea/KEKOxTutorial/master/media/32_0.png) - - -![Method Not Allowed](https://raw.githubusercontent.com/KerasKorea/KEKOxTutorial/master/media/32_1.png) - -#### cURL을 사용해서 Keras REST API 테스트하기 - - -![beagle](https://raw.githubusercontent.com/KerasKorea/KEKOxTutorial/master/media/32_2.jpg) - -```bash -$ curl -X POST -F image=@dog.jpg 'http://localhost:5000/predict' -{ - "predictions": [ - { - "label": "beagle", - "probability": 0.9901360869407654 - }, - { - "label": "Walker_hound", - "probability": 0.002396771451458335 - }, - { - "label": "pot", - "probability": 0.0013951235450804234 - }, - { - "label": "Brittany_spaniel", - "probability": 0.001283277408219874 - }, - { - "label": "bluetick", - "probability": 0.0010894243605434895 - } - ], - "success": true -} -``` - -#### Keras REST API 프로그래밍 방식 사용 - -```python -# import the necessary packages -import requests - -# initialize the Keras REST API endpoint URL along with the input -# image path -KERAS_REST_API_URL = "http://localhost:5000/predict" -IMAGE_PATH = "dog.jpg" - -# load the input image and construct the payload for the request -image = open(IMAGE_PATH, "rb").read() -payload = {"image": image} - -# submit the request -r = requests.post(KERAS_REST_API_URL, files=payload).json() - -# ensure the request was successful -if r["success"]: - # loop over the predictions and display them - for (i, result) in enumerate(r["predictions"]): - print("{}. {}: {:.4f}".format(i + 1, result["label"], - result["probability"])) - -# otherwise, the request failed -else: - print("Request failed") -``` - -```bash -$ python simple_request.py -1. beagle: 0.9901 -2. Walker_hound: 0.0024 -3. pot: 0.0014 -4. Brittany_spaniel: 0.0013 -5. bluetick: 0.0011 -``` - ---- - -### 참고 -* [PyImageSearch](https://www.pyimagesearch.com/) -* [Flask 웹 프레임워크](http://docs.python-requests.org/en/master/) - -> 이 글은 2018 컨트리뷰톤에서 [`Contributue to Keras`](https://github.com/KerasKorea/KEKOxTutorial) 프로젝트로 진행했습니다. -> Translator : [mike2ox](https://github.com/mike2ox) (Moonhyeok Song) -> Translator Email : \ No newline at end of file diff --git a/media/32_0.png b/media/32_0.png deleted file mode 100644 index da2bdeb28be1e39b60fa5ad900060c88f50a916d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 37495 zcmagF1yr2NvIYvl-Q5Q#xVsDt?h+h=1$QU7yAw!o2=49>+%*t15Zs;M@`hyZbMAWU z-uGDlg5jU)>gw+5uBxx9Ba{`TP!I_bAs`@7WTYijAt0d0As`^t;9X!L4X4)wNu-6y*6$>}{EiUTK&; zY#qR@As_@rJRFQntj%1=jm^w0?SufQZCwCzOH(0$CYJ)Mf`ho3g{8EYlbM>AqPmHf zwF#doKtz~a(1Rb$z}C#gh}^^0#?G1FLkRGPFF*M6>uVMO`5zJ&Yazg2p|li~$;IuR z%*eTzxtUDZIC#l<_?X$a_;}el8OhmM*|=F)`B>OEnONEQS^4-`*~tI-0f2jRGX21> 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=?UTF-8?q?#42=20:=20=EC=A0=84=EC=B2=B4=20=ED=95=AD?= =?UTF-8?q?=EB=AA=A9=20=EB=B0=8F=20=EC=9D=B4=EB=AF=B8=EC=A7=80,=20code=20?= =?UTF-8?q?=EC=99=84=EB=A3=8C.=20=EB=B3=B8=EB=AC=B8=20=EB=82=B4=EC=9A=A9?= =?UTF-8?q?=20=EB=B2=88=EC=97=AD=20=EC=9D=B4=EC=96=B4=EC=84=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...h_as_your_first_deep_learning_framework.md | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 42_keras_or_pytorch_as_your_first_deep_learning_framework.md diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md new file mode 100644 index 0000000..fddadbb --- /dev/null +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -0,0 +1,85 @@ +## Keras vs PyTorch 어떤 플랫폼을 선택해야 할까?(keras or pytorch as your first deep learning framework) +[원문](https://deepsense.ai/keras-or-pytorch/) +> 문서 간략 소개 + +* Keras +* PyTorch +* framework + +### 소개 +![Keras_vs_PyTorch](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) + +### 좋아, 근데 다른 프레임워크는 어때? + +### Keras vs PyTorch : 쉬운 사용법과 유연성 + +#### Keras + +```python +model = Sequential() +model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) +model.add(MaxPool2D()) +model.add(Conv2D(16, (3, 3), activation='relu')) +model.add(MaxPool2D()) +model.add(Flatten()) +model.add(Dense(10, activation='softmax')) +``` + +#### PyTorch + +```python +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + + self.conv1 = nn.Conv2d(3, 32, 3) + self.conv2 = nn.Conv2d(32, 16, 3) + self.fc1 = nn.Linear(16 * 6 * 6, 10) + self.pool = nn.MaxPool2d(2, 2) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 6 * 6) + x = F.log_softmax(self.fc1(x), dim=-1) + + return x + +model = Net() +``` +#### 요약 + +### Keras vs PyTorch : 대중성과 학습자료 접근성 + + +![Percentof ML papers that mention...](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_1.png) + + +#### 요약 + +### Keras vs PyTorch : 디버깅과 introspection + +#### 요약 + +### Keras vs PyTorch : 모델을 추출하고 다른 플랫폼과의 호환성 + +#### 요약 + +### Keras vs PyTorch : 성능 + +![Tesla p100](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) + +![Tesla K80](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) + +#### 요약 + +### Keras vs PyTorch : 결론 + +### 참고문서 +* [참고 사이트 1]() +* [참고 사이트 2]() + + +> 이 글은 2018 컨트리뷰톤에서 [`Contribute to Keras`](https://github.com/KerasKorea/KEKOxTutorial) 프로젝트로 진행했습니다. +> Translator: [mike2ox](https://github.com/mike2ox)(Moonhyeok Song) +> Translator email : From e2e8d70879f0456916edf6f7c88d39c9a0351132 Mon Sep 17 00:00:00 2001 From: mike2ox Date: Wed, 24 Oct 2018 20:06:41 +0900 Subject: [PATCH 04/21] =?UTF-8?q?#42=20:=20=EB=B2=88=EC=97=AD=20=EC=9E=91?= =?UTF-8?q?=EC=97=85=EC=A4=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...h_as_your_first_deep_learning_framework.md | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index fddadbb..f05a3b4 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -1,13 +1,24 @@ ## Keras vs PyTorch 어떤 플랫폼을 선택해야 할까?(keras or pytorch as your first deep learning framework) [원문](https://deepsense.ai/keras-or-pytorch/) -> 문서 간략 소개 +> 본 글은 딥러닝을 배우는, 가르치는 입장에서 어떤 프레임워크가 좋은지를 Keras와 PyTorch를 비교하며 독자가 선택을 할 수 있게 내용을 전개하고 있다. 원 작성자인 Piotr Migdal과 Rafal Jakubanis은 자신들의 경험을 바탕으로 글을 설명하고 있으므로 더 정확한 선택이 있으리라 생각한다. * Keras * PyTorch * framework +![Keras_vs_PyTorch](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) + +> 본 글을 읽고 있는 그대, 딥러닝을 배우고 싶나요? 그대가 당신 비즈니스에 적용할 지, 다음 프로젝트에 적용할 지, 아니면 그저 시장성 있는 기술을 갖고 싶은 것인지가 중요하다. 배우기 위해 적절한 프레임워크를 선택하는건 그대 목표에 도달하기 위해 중요한 첫 단계이다. + +우리는 강력하게 당신이 Keras나 PyTorch를 선택하길 추천합니다. 그것들은 배우기도, 실험하기도 재밌는 강력한 도구들입니다. 우리는 교사나 학생의 입장에서 둘 다 알고 있습니다. Piotr는 두 프레임워크로 워크숍을 진행했고, Rafal은 현재 배우고 있는 중입니다. + +([Hacker News](https://news.ycombinator.com/item?id=17415321)와 [Reddit](https://www.reddit.com/r/MachineLearning/comments/8uhqol/d_keras_vs_pytorch_in_depth_comparison_of/)에서 논의한 것을 참조하세요.) + ### 소개 -![Keras_vs_PyTorch](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) +Keras와 PyTorch는 데이터 과학자들 사이에서 인기를 얻고있는 딥러닝용 오픈 소스 프레임워크입니다. + +- [Keras](https://keras.io/)는 Tensorflow, CNTK, Theano, MXNet(혹은 Tensorflow안의 tf.contrib)의 상단에서 작동할 수 있는 고급 API입니다. 2015년 3월에 첫 배포를 한 이래로, 쉬운 사용법과 간단한 문법, 빠른 설계 덕분에 인기를 끌고 있습니다. 구글에서 지원하고 있습니다. +- [PyTorch](https://pytorch.org/) ### 좋아, 근데 다른 프레임워크는 어때? @@ -67,9 +78,9 @@ model = Net() ### Keras vs PyTorch : 성능 -![Tesla p100](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) +![Tesla p100](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_2.png) -![Tesla K80](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) +![Tesla K80](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_3.png) #### 요약 From 545dfa561546cb17eec83ac3b053a013b1ea3b49 Mon Sep 17 00:00:00 2001 From: mike2ox Date: Wed, 24 Oct 2018 21:59:08 +0900 Subject: [PATCH 05/21] =?UTF-8?q?#42=20:=20=EC=9D=B8=EA=B8=B0=EC=99=80=20?= =?UTF-8?q?=ED=95=99=EC=8A=B5=EC=9E=90=EB=A3=8C=20=EC=A0=91=EA=B7=BC?= =?UTF-8?q?=EC=84=B1=20=EB=B2=88=EC=97=AD=EC=A4=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...h_as_your_first_deep_learning_framework.md | 44 +++++++++++++++++-- 1 file changed, 40 insertions(+), 4 deletions(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index f05a3b4..213a647 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -17,12 +17,33 @@ ### 소개 Keras와 PyTorch는 데이터 과학자들 사이에서 인기를 얻고있는 딥러닝용 오픈 소스 프레임워크입니다. -- [Keras](https://keras.io/)는 Tensorflow, CNTK, Theano, MXNet(혹은 Tensorflow안의 tf.contrib)의 상단에서 작동할 수 있는 고급 API입니다. 2015년 3월에 첫 배포를 한 이래로, 쉬운 사용법과 간단한 문법, 빠른 설계 덕분에 인기를 끌고 있습니다. 구글에서 지원하고 있습니다. -- [PyTorch](https://pytorch.org/) +- [Keras](https://keras.io/)는 Tensorflow, CNTK, Theano, MXNet(혹은 Tensorflow안의 tf.contrib)의 상단에서 작동할 수 있는 고수준 API입니다. 2015년 3월에 첫 배포를 한 이래로, 쉬운 사용법과 간단한 문법, 빠른 설계 덕분에 인기를 끌고 있습니다. 이 도구는 구글에서 지원받고 있습니다. +- [PyTorch](https://pytorch.org/)는 2016년 10월에 배포된, 배열 표현식으로 직접 작업 저수준 API입니다. 작년에 큰 관심을 끌었고, 학술 연구에서 선호되는 솔루션, 맞춤 표현식으로 최적화하는 딥러닝 어플리케이션이 되어가고 있습니다. 이 도구는 페이스북에서 지원받고 있습니다. + +우리가 두 프레임워크([참조](https://www.reddit.com/r/MachineLearning/comments/6bicfo/d_keras_vs_PyTorch/))의 핵심 상세 내용을 논의하기 전에 당신을 실망시키고자 합니다. - '어떤 툴이 더 좋은가?'에 대한 정답은 없습니다. 선택은 절대적으로 당신의 기술적 지식, 필요성 그리고 기대에 달렸습니다. 본 글은 당신이 처음으로 두 프레임워크 중 한가지를 선택할 때 도움이 될 아이디어를 제공해주는데 목적을 두고 있습니다. + +#### 요약하자면 +Keras는 플러그 & 플레이 정신에 맞게, 표준 레이어로 실험하고 입문하기 쉬울 겁니다. + +PyTorch는 수학적으로 연관된 더 많은 사용자들을 위해 더 유연하고 저수준의 접근성을 제공합니다. + ### 좋아, 근데 다른 프레임워크는 어때? +Tensorflow는 대중적인 딥러닝 프레임워크입니다. 그러나, 원시 Tensorflowsms 계산 그래프 구축을 장황하고 모호하게 추상화하고 있습니다. 일단 딥러닝의 기초 지식을 알고 있다면 문제가 되지 않습니다. 하지만, 새로 입문하는 사람에겐 공식적으로 지원되는 인터페이스로써 Keras를 사용하는게 더 쉽고 생산적일 겁니다. + +[수정 : 최근, Tensorflow에서 [Eager Execution](https://www.tensorflow.org/versions/r1.9/programmers_guide/keras)를 소개했는데 이는 모든 python 코드를 실행하고 초보자에게 보다 직관적으로 모델을 학습시킬 수 있게 해줍니다.(특히 tf.keras API를 사용할 때!)] + +그대가 어떤 Theano 튜토리얼을 찾았지만, 이는 더 이상 활발한 개발이 이뤄지지지 않습니다. Caffe는 유연성이 부족하지만, Torch는 Lua를 사용합니다. MXNet, Chainer 그리고 CNTK는 현재 대중적이지 않습니다. + ### Keras vs PyTorch : 쉬운 사용법과 유연성 +Keras와 PyTorch는 작동에 대한 추상화 단게에서 다릅니다. + +Keras는 딥러닝에 사용되는 레이어와 연산자들을 neat(레코 크기의 블럭)로 감싸고, 데이터 과학자의 입장에서 딥러닝 복잡성을 추상화하는 고수준 API입니다. + +PyTorch는 유저들에게 맞춤 레이어를 작성하고 수학적 최적화 작업을 볼 수 있게 자율성을 주도록 해주는 저수준 환경을 제공합니다. 더 복잡한 구조 개발은 python의 모든 기능을 사용하고 모든 기능의 내부에 접근하는 것보다 간단합니다. + +어떻게 Keras와 PyTorch로 간단한 컨볼루션 신경망을 정의할 지를 head-to-head로 비교해보자. #### Keras @@ -58,12 +79,27 @@ class Net(nn.Module): model = Net() ``` + +위 코드 블럭은 두 프레임워크의 차이를 약간 맛보게 해줍니다. 모델을 학습하기 위해, PyTorch는 20줄의 코드가 필요한 반면, Keras는 단일 코드만 필요했습니다. GPU 가속화 사용은 Keras에선 암묵적으로 처리되지만, PyTorch는 CPU와 GPU간 데이터 전송할 때 요구합니다. + +만약 초보자라면, Keras는 명확한 이점을 보일 것입니다. Keras는 실제로 읽기 쉽고 간결해 구현 단계에서의 세부 사항을 건너뛰는 동시에 그대의 첫번째 end-to-end 딥러닝 모델을 빠르게 설계하도록 해줄겁니다. 그러나, 이런 세부 사항을 뛰어넘는 건 당신의 딥러닝 작업에서 계산이 필요한 블럭의 내부 작업 탐색에 제한이 됩니다. PyTorch를 사용하는 건 당신에게 역전파처럼 핵심 딥러닝 개념과 학습 단계의 나머지 부분에 대해 생각할 것들을 제공합니다. + +PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않는다. 이는 초심자들이 사용하는 중요한 딥러닝 도구이다. 능숙한 데이터 과학자들에게도 마찬가지다. 예를 들면, Kaggle에서 열린 `the Dstl Satellite Imagery Feature Detection`에서 상위 3팀이 그들의 솔루션에 Keras를 사용하였다. 반면, 4등인 [우리](https://blog.deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/#_ga=2.53479528.114026073.1540369751-2000517400.1540369751)는 PyTorch와 Keras를 혼합해서 사용하였다. + +당신의 딥러닝 어플리케이션이 Keras가 제공하는 것 이상의 유연성을 필요하는 지 파악하는 건 가치가 있다. 그대의 필요에 따라, Keras는 [가장 적은 힘의 규칙](https://en.wikipedia.org/wiki/Rule_of_least_power)에 입각하는 좋은 방법이 될 수 있다. + #### 요약 +- Keras : 좀 더 간결한 API +- PyTorch : 더 유연하고, 딥러닝 개념을 깁게 이해하는데 도움을 줌 + +### Keras vs PyTorch : 인기와 학습자료 접근성 +프레임워크의 인기는 단지 유용성의 대리만은 아니다. 작업 코드가 있는 튜토리얼, 리포지토리 그리고 단체 토론 등 커뮤니티 지원도 중요합니다. 2018년 6월 현재, Keras와 PyTorch는 GitHub과 arXiv 논문에서 인기를 누리고 있습니다.(Keras를 언급한 대부분의 논문들은 Tensorflow 백엔드 또한 언급하고 있습니다.) KDnugget에 따르면, Keras와 PyTorch는 가장 빠르게 성장하는 [데이터 과학 도구들](https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html)입니다. -### Keras vs PyTorch : 대중성과 학습자료 접근성 +![Percentof ML papers that mention...](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_1.png) +> 지난 6년간 43k개의 ML논문을 기반으로, arxiv 논문들에서 딥러닝 프레임워크에 대한 언급에 대한 자료입니다. Tensorflow는 전체 논문의 14.3%, PyTorch는 4.7%, Keras 4.0%, Caffe 3.8%, Theano 2.3%, Torch 1.5. MXNet/chainer/cntk는 1% 이하로 언급되었습니다. [참조](https://t.co/YOYAvc33iN) - Andrej Karpathy (@karpathy) -![Percentof ML papers that mention...](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_1.png) +두 프레임워크는 만족스러운 참고문서를 갖고 있지만, PyTorch는 강력한 커뮤니티 지원을 제공합니다. #### 요약 From 11585f6884896f7191a067015af51ddd2634a1c4 Mon Sep 17 00:00:00 2001 From: mike2ox Date: Thu, 25 Oct 2018 21:17:05 +0900 Subject: [PATCH 06/21] =?UTF-8?q?#42=20:=201=EC=B0=A8=20=EB=B2=88=EC=97=AD?= =?UTF-8?q?=20=EC=99=84=EB=A3=8C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...h_as_your_first_deep_learning_framework.md | 49 +++++++++++++++++-- 1 file changed, 45 insertions(+), 4 deletions(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index 213a647..ebff174 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -99,32 +99,73 @@ PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않는다. > 지난 6년간 43k개의 ML논문을 기반으로, arxiv 논문들에서 딥러닝 프레임워크에 대한 언급에 대한 자료입니다. Tensorflow는 전체 논문의 14.3%, PyTorch는 4.7%, Keras 4.0%, Caffe 3.8%, Theano 2.3%, Torch 1.5. MXNet/chainer/cntk는 1% 이하로 언급되었습니다. [참조](https://t.co/YOYAvc33iN) - Andrej Karpathy (@karpathy) -두 프레임워크는 만족스러운 참고문서를 갖고 있지만, PyTorch는 강력한 커뮤니티 지원을 제공합니다. +두 프레임워크는 만족스러운 참고 문서를 갖고 있지만, PyTorch는 강력한 커뮤니티 지원을 제공합니다. 해당 커뮤니티 게시판은 당신이 난관에 부딪쳤거나 참고 문서나 스택오버플로우는 당신이 필요로 하는 정답이 없다면 방문하기 좋은 곳이다. +anecdotally, 우리는 특정 신경망 구조에서 초심자 수준의 딥러닝 코스를 PyTorch보다 Keras로 더 쉽게 접근할 수 있다는 걸 발견했습니다. Keras에서 제공하는 코드의 가독성과 실험을 쉽게 해주는 장점으로 인해, Keras는 딥러닝 열광자, 튜터, 고수준의 kaggle 우승자들에 의해 많이 쓰이게 될 겁니다. + +Keras 자료와 딥러닝 코스의 예시로, ["Starting deep learning hands-on: image classification on CIFAR-10"](https://blog.deepsense.ai/deep-learning-hands-on-image-classification/#_ga=2.52232937.114026073.1540369751-2000517400.1540369751)와 ["Deep Learning with Rython"](https://www.manning.com/books/deep-learning-with-python)를 참조하십시오. PyTorch 자료로는, 신경망의 내부 작업을 학습하는데 더 도던적이고 포괄적인 접근법을 제공하는 공식 튜토리얼을 추천합니다. PyTorch에 대한 전반적인 내용을 보려면, 이 [문서](http://www.goldsborough.me/ml/ai/python/2018/02/04/20-17-20-a_promenade_of_pytorch/)를 참조하세요. #### 요약 +- Keras : 튜토리얼이나 재사용 가능한 코드로의 접근성이 좋음 +- PyTorch : 뛰어난 커뮤니티와 활발한 개발 -### Keras vs PyTorch : 디버깅과 introspection +### Keras vs PyTorch : 디버깅과 코드 복기(introspection) +추상화에서 많은 계산 조각들을 묶어주는 Keras는 문제를 발생시키는 외부 코드 라인을 고정시키는 게 어렵습니다. 좀 더 장황하게 구성된 프레임워크인 PyTorch는 우리의 스크립트 실행을 따라갈 수 있게 해줍니다. 이건 Numpy를 디버깅하는 것과 유사합니다. 우리는 쉽게 코드안의 모든 객체들에 접근할 수 있고, 어디서 오류가 발생하는 지 알려 주는 상태(혹은 기본 python식 디버깅)를 출력할 수 있습니다. +Keras로 기본 신경망을 만든 사용자들은 PyTorch 사용자들보다 잘못된 방향으로 갈 가능성이 적습니다. 하지만 일단 잘못되기 시작하면, 많이 힘들고 종종 막힌 코드 라인을 찾기 힘듭니다. PyTorch는 모델의 목잡성과 관련없이 보다 직접적이고 컨볼루션이 아닌 디버깅 경험을 제공합니다. 또한, 의심스러운 경우 PyTorch 레포를 쉽게 조회해 코드를 읽어볼 수 있습니다. #### 요약 +- PyTorch : 더 좋은 디버깅 기능을 제공 +- Keras : (잠재적으로) 단순 신경망 디버깅 빈도수 감소 ### Keras vs PyTorch : 모델을 추출하고 다른 플랫폼과의 호환성 +생산에서 학습된 모델을 내보내고 배포하는 옵션은 무엇인가요? + +PyTorch는 python기반으로 휴대할 수 없는 pickle에 모델을 저장하지만, Keras는 JSON + H5 파일을 사용하는 안전한 접근 방식의 장점을 활용합니다.(일반적으로 Keras에 저장하는게 더 어렵습니다.) 또한 [R에도 Keras](https://keras.rstudio.com/)가 있습니다. 이 경우, R을 사용하여 데이터 분석팀과 협력해야 할 수도 있습니다. + +Tensorflow에서 실행되는 Keras는 [모바일용 Tensorflow](https://www.tensorflow.org/mobile/mobile_intro)(혹은 [Tensorflow Lite](https://www.tensorflow.org/mobile/tflite/index))를 통해 모바일 플랫폼에 구축할 수 있는 다양한 솔리드 옵션을 제공합니다. [Tensorflow.js](https://js.tensorflow.org/) 혹은 [Keras.js](https://github.com/transcranial/keras-js)를 사용하여 멋진 웹 애플리케이션을 배포할 수 있습니다. 예를 들어, Piotr와 그의 학생들이 만든, [시험 공포증 유발 요소를 탐지하는 딥러닝 브라우저 플러그인](https://github.com/cytadela8/trypophobia)를 보세요. + +PyTorch 모델을 추출하는 건 python 코드때문에 더 부담되기에, 현재 많이 추천하는 접근방식은 [ONNX](https://pytorch.org/docs/master/onnx.html)를 사용하여 PyTorch 모델을 Caffe2로 변환하는 것입니다. + #### 요약 +- Keras : (Tensorflow backend를 통해) 더 많은 개발 옵션을 제공하고, 모델을 쉽게 추출할 수 있음. ### Keras vs PyTorch : 성능 +> 미리 측정된 최적화는 프로그래밍에서 모든 악의 근원입니다. - Donald Knuth + +대부분의 인스턴스에서, 속도 측정에서의 차이는 프레임워크 선택을 위한 주요 요점은 아닙니다.(특히, 학습할 때) GPU 시간은 데이터 과학자의 시간보다 더 인색합니다. 게다가, 학습하는 동안 발생하는 성능의 병목현상은 실패한 실험이나, 최적화하지 않은 신경망이나 데이터 로딩(loading)이 원인일 수 있습니다. 완벽을 위해, 여전히 우리는 해당 주제를 다뤄야할 compel을 느낍니다. 우리는 두 가지 비교사항을 제안합니다. + +- [Tensorflow, Keras 그리고 PyTorch를 비교](https://wrosinski.github.io/deep-learning-frameworks/) by Wojtek Rosinski +- [딥러닝 프레임워크들에 대한 비교 : 로제타 스톤식 접근](https://github.com/ilkarman/DeepLearningFrameworks/) by Microsoft +> 더 상세한 multi-GPU 프레임워크 비교를 보려면, [이 글](https://medium.com/@iliakarmanov/multi-gpu-rosetta-stone-d4fa96162986)을 참조하세요 + +PyTorch는 Tensorflow만큼 빠르며, RNN에선 잠재적으로 더 빠릅니다. Keras는 지속적으로 더 느립니다. 위의 첫 번째 비교를 작성한 저자가 지적했듯이, 고성능 프레임워크의 연산 효율성 향상(대부분 PyTorc와 Tensorflow)은 빠른 개발 환경과 Keras가 제공하는 실험의 용이성보다 더 중요할 것입니다. ![Tesla p100](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_2.png) ![Tesla K80](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_3.png) #### 요약 +- 학습 속도에 대한 걱정과 달리, PyTorch가 Keras를 능가 ### Keras vs PyTorch : 결론 +Keras와 PyTorch는 배우기위한 첫번째 딥러닝 프레임워크로 좋은 선택입니다. +만약 당신이 수학자, 연구자, 혹은 당신의 모델이 실제로 어떻게 작동하는지 알고 싶다면, PyTorch를 선택하길 권장합니다. 고급 맞춤형 알고리즘(그리고 디버깅)이 필요한 경우(ex. [YOLOv3](https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/) 혹은 [LSTM](https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983)을 사용한 객체 인식) 또는 신경망 이외의 배열 식을 최적화해야 할 경우(ex. [행렬 분해](http://blog.ethanrosenthal.com/2017/06/20/matrix-factorization-in-pytorch/) 혹은 [word2vec](https://adoni.github.io/2017/11/08/word2vec-pytorch/) 알고리즘)에 빛을 발합니다. + + plug & play 프레임워크를 원한다면, Keras는 확실히 더 쉬울 겁니다. 즉, 수학적 구현의 세부 사항들에 많은 시간을 들이지 않고도 모델을 신속하게 제작, 학습 그리고 평가할 수 있습니다. + +수정 : 실제 사례에 대해 코드를 비교하려면, 이 [기사](https://deepsense.ai/keras-vs-pytorch-avp-transfer-learning)를 참조하세요 + +딥러닝의 핵심 개념에 대한 지식은 유동성이 있습니다. 어떤 환경에서 기본사항을 숙지하고나면, 다른 곳에 적용하고 새로운 딥러닝 라이브러리로 전환할 때 이를 시행할 수 있다는 점입니다. + +Keras와 PyTorch에서 간단한 딥러닝 방법을 사용해 보는 것을 권장합니다. 당신이 가장 좋아하고 가장 덜 좋아하는 요소는 무엇입니까? 어떤 프레임워크 경험이 더 마음에 드시나요? + +Keras, Tensorflow 그리고 PyTorch의 딥러닝에 대해 자세히 알고 싶은가요? [맞춤형 교육 서비스](https://deepsense.ai/tailored-team-training-tracks/)를 확인하세요. + ### 참고문서 -* [참고 사이트 1]() -* [참고 사이트 2]() +* [케라스 공식 홈페이지](https://keras.io/) +* [파이토치 공식 홈페이지](https://pytorch.org/) > 이 글은 2018 컨트리뷰톤에서 [`Contribute to Keras`](https://github.com/KerasKorea/KEKOxTutorial) 프로젝트로 진행했습니다. From d7ea179cc3b8e9f1a6b5abb02132073530129c0b Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:51:09 +0900 Subject: [PATCH 07/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index ebff174..c5be071 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -8,7 +8,7 @@ ![Keras_vs_PyTorch](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_0.png) -> 본 글을 읽고 있는 그대, 딥러닝을 배우고 싶나요? 그대가 당신 비즈니스에 적용할 지, 다음 프로젝트에 적용할 지, 아니면 그저 시장성 있는 기술을 갖고 싶은 것인지가 중요하다. 배우기 위해 적절한 프레임워크를 선택하는건 그대 목표에 도달하기 위해 중요한 첫 단계이다. +> 본 글을 읽고 있는 당신, 딥러닝을 배우고 싶나요? 딥러닝을 당신의 사업에 적용하고 싶든, 다음 프로젝트에 적용하고 싶든, 아니면 그저 시장성 있는 기술을 갖고 싶든, 배우기에 적절한 프레임워크를 선택하는 것이 당신의 목표에 도달하기 위해 중요한 첫 단계입니다. 우리는 강력하게 당신이 Keras나 PyTorch를 선택하길 추천합니다. 그것들은 배우기도, 실험하기도 재밌는 강력한 도구들입니다. 우리는 교사나 학생의 입장에서 둘 다 알고 있습니다. Piotr는 두 프레임워크로 워크숍을 진행했고, Rafal은 현재 배우고 있는 중입니다. From e7d228ecb45075745e1a36c761bc7b8034d00ea1 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:51:30 +0900 Subject: [PATCH 08/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index c5be071..dc5475e 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -18,7 +18,7 @@ Keras와 PyTorch는 데이터 과학자들 사이에서 인기를 얻고있는 딥러닝용 오픈 소스 프레임워크입니다. - [Keras](https://keras.io/)는 Tensorflow, CNTK, Theano, MXNet(혹은 Tensorflow안의 tf.contrib)의 상단에서 작동할 수 있는 고수준 API입니다. 2015년 3월에 첫 배포를 한 이래로, 쉬운 사용법과 간단한 문법, 빠른 설계 덕분에 인기를 끌고 있습니다. 이 도구는 구글에서 지원받고 있습니다. -- [PyTorch](https://pytorch.org/)는 2016년 10월에 배포된, 배열 표현식으로 직접 작업 저수준 API입니다. 작년에 큰 관심을 끌었고, 학술 연구에서 선호되는 솔루션, 맞춤 표현식으로 최적화하는 딥러닝 어플리케이션이 되어가고 있습니다. 이 도구는 페이스북에서 지원받고 있습니다. +- [PyTorch](https://pytorch.org/)는 2016년 10월에 배포된, 배열 표현식으로 직접 작업하는 저수준 API입니다. 작년에 큰 관심을 끌었고, 학술 연구에서 선호되는 솔루션이자, 맞춤 표현식으로 최적화하는 딥러닝 어플리케이션이 되어가고 있습니다. 이 도구는 페이스북에서 지원받고 있습니다. 우리가 두 프레임워크([참조](https://www.reddit.com/r/MachineLearning/comments/6bicfo/d_keras_vs_PyTorch/))의 핵심 상세 내용을 논의하기 전에 당신을 실망시키고자 합니다. - '어떤 툴이 더 좋은가?'에 대한 정답은 없습니다. 선택은 절대적으로 당신의 기술적 지식, 필요성 그리고 기대에 달렸습니다. 본 글은 당신이 처음으로 두 프레임워크 중 한가지를 선택할 때 도움이 될 아이디어를 제공해주는데 목적을 두고 있습니다. From d0440d55402f2ecf083fd96ad90362d3cd277d44 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:51:37 +0900 Subject: [PATCH 09/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index dc5475e..0606057 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -29,7 +29,7 @@ PyTorch는 수학적으로 연관된 더 많은 사용자들을 위해 더 유 ### 좋아, 근데 다른 프레임워크는 어때? -Tensorflow는 대중적인 딥러닝 프레임워크입니다. 그러나, 원시 Tensorflowsms 계산 그래프 구축을 장황하고 모호하게 추상화하고 있습니다. 일단 딥러닝의 기초 지식을 알고 있다면 문제가 되지 않습니다. 하지만, 새로 입문하는 사람에겐 공식적으로 지원되는 인터페이스로써 Keras를 사용하는게 더 쉽고 생산적일 겁니다. +Tensorflow는 대중적인 딥러닝 프레임워크입니다. 그러나, 원시 Tensorflow는 계산 그래프 구축을 장황하고 모호하게 추상화하고 있습니다. 일단 딥러닝의 기초 지식을 알고 있다면 문제가 되지 않습니다. 하지만, 새로 입문하는 사람에겐 공식적으로 지원되는 인터페이스로써 Keras를 사용하는게 더 쉽고 생산적일 겁니다. [수정 : 최근, Tensorflow에서 [Eager Execution](https://www.tensorflow.org/versions/r1.9/programmers_guide/keras)를 소개했는데 이는 모든 python 코드를 실행하고 초보자에게 보다 직관적으로 모델을 학습시킬 수 있게 해줍니다.(특히 tf.keras API를 사용할 때!)] From e6d8fb18b3e28563e15afe2d1168d69b0840b2d8 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:51:41 +0900 Subject: [PATCH 10/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index 0606057..8db327d 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -37,7 +37,7 @@ Tensorflow는 대중적인 딥러닝 프레임워크입니다. 그러나, 원시 ### Keras vs PyTorch : 쉬운 사용법과 유연성 -Keras와 PyTorch는 작동에 대한 추상화 단게에서 다릅니다. +Keras와 PyTorch는 작동에 대한 추상화 단계에서 다릅니다. Keras는 딥러닝에 사용되는 레이어와 연산자들을 neat(레코 크기의 블럭)로 감싸고, 데이터 과학자의 입장에서 딥러닝 복잡성을 추상화하는 고수준 API입니다. From 7d9070f6f5f1f4e5cfdfae9ff54e94a69c061631 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:51:46 +0900 Subject: [PATCH 11/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index 8db327d..abaf0b0 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -43,7 +43,7 @@ Keras는 딥러닝에 사용되는 레이어와 연산자들을 neat(레코 크 PyTorch는 유저들에게 맞춤 레이어를 작성하고 수학적 최적화 작업을 볼 수 있게 자율성을 주도록 해주는 저수준 환경을 제공합니다. 더 복잡한 구조 개발은 python의 모든 기능을 사용하고 모든 기능의 내부에 접근하는 것보다 간단합니다. -어떻게 Keras와 PyTorch로 간단한 컨볼루션 신경망을 정의할 지를 head-to-head로 비교해보자. +어떻게 Keras와 PyTorch로 간단한 컨볼루션 신경망을 정의할 지를 head-to-head로 비교해봅시다. #### Keras From 4d4a067c041b11503b2a5b21f017eb856f2756ba Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:51:59 +0900 Subject: [PATCH 12/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index abaf0b0..b4eb209 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -80,7 +80,7 @@ class Net(nn.Module): model = Net() ``` -위 코드 블럭은 두 프레임워크의 차이를 약간 맛보게 해줍니다. 모델을 학습하기 위해, PyTorch는 20줄의 코드가 필요한 반면, Keras는 단일 코드만 필요했습니다. GPU 가속화 사용은 Keras에선 암묵적으로 처리되지만, PyTorch는 CPU와 GPU간 데이터 전송할 때 요구합니다. +위 코드 블럭은 두 프레임워크의 차이를 약간 맛보게 해줍니다. 모델을 학습하기 위해, PyTorch는 20줄의 코드가 필요한 반면, Keras는 단일 코드만 필요했습니다. GPU 가속화 사용은 Keras에선 암묵적으로 처리되지만, PyTorch는 CPU와 GPU간 데이터를 전송할 때 요구합니다. 만약 초보자라면, Keras는 명확한 이점을 보일 것입니다. Keras는 실제로 읽기 쉽고 간결해 구현 단계에서의 세부 사항을 건너뛰는 동시에 그대의 첫번째 end-to-end 딥러닝 모델을 빠르게 설계하도록 해줄겁니다. 그러나, 이런 세부 사항을 뛰어넘는 건 당신의 딥러닝 작업에서 계산이 필요한 블럭의 내부 작업 탐색에 제한이 됩니다. PyTorch를 사용하는 건 당신에게 역전파처럼 핵심 딥러닝 개념과 학습 단계의 나머지 부분에 대해 생각할 것들을 제공합니다. From e9c3d2b69bb31dedc3277369dd9d1ebf95447211 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:52:15 +0900 Subject: [PATCH 13/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index b4eb209..4283c17 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -84,7 +84,7 @@ model = Net() 만약 초보자라면, Keras는 명확한 이점을 보일 것입니다. Keras는 실제로 읽기 쉽고 간결해 구현 단계에서의 세부 사항을 건너뛰는 동시에 그대의 첫번째 end-to-end 딥러닝 모델을 빠르게 설계하도록 해줄겁니다. 그러나, 이런 세부 사항을 뛰어넘는 건 당신의 딥러닝 작업에서 계산이 필요한 블럭의 내부 작업 탐색에 제한이 됩니다. PyTorch를 사용하는 건 당신에게 역전파처럼 핵심 딥러닝 개념과 학습 단계의 나머지 부분에 대해 생각할 것들을 제공합니다. -PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않는다. 이는 초심자들이 사용하는 중요한 딥러닝 도구이다. 능숙한 데이터 과학자들에게도 마찬가지다. 예를 들면, Kaggle에서 열린 `the Dstl Satellite Imagery Feature Detection`에서 상위 3팀이 그들의 솔루션에 Keras를 사용하였다. 반면, 4등인 [우리](https://blog.deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/#_ga=2.53479528.114026073.1540369751-2000517400.1540369751)는 PyTorch와 Keras를 혼합해서 사용하였다. +PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않습니다. 이는 초심자들이 사용하는 중요한 딥러닝 도구입니다. 능숙한 데이터 과학자들에게도 마찬가지입니다. 예를 들면, Kaggle에서 열린 `the Dstl Satellite Imagery Feature Detection`에서 상위 3팀이 그들의 솔루션에 Keras를 사용하였습니다. 반면, 4등인 [우리](https://blog.deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/#_ga=2.53479528.114026073.1540369751-2000517400.1540369751)는 PyTorch와 Keras를 혼합해서 사용하였습니다. 당신의 딥러닝 어플리케이션이 Keras가 제공하는 것 이상의 유연성을 필요하는 지 파악하는 건 가치가 있다. 그대의 필요에 따라, Keras는 [가장 적은 힘의 규칙](https://en.wikipedia.org/wiki/Rule_of_least_power)에 입각하는 좋은 방법이 될 수 있다. From a614b4d39dbc497e3f20cf4bb37e93a7eeac2207 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:52:18 +0900 Subject: [PATCH 14/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index 4283c17..45b37e8 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -86,7 +86,7 @@ model = Net() PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않습니다. 이는 초심자들이 사용하는 중요한 딥러닝 도구입니다. 능숙한 데이터 과학자들에게도 마찬가지입니다. 예를 들면, Kaggle에서 열린 `the Dstl Satellite Imagery Feature Detection`에서 상위 3팀이 그들의 솔루션에 Keras를 사용하였습니다. 반면, 4등인 [우리](https://blog.deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/#_ga=2.53479528.114026073.1540369751-2000517400.1540369751)는 PyTorch와 Keras를 혼합해서 사용하였습니다. -당신의 딥러닝 어플리케이션이 Keras가 제공하는 것 이상의 유연성을 필요하는 지 파악하는 건 가치가 있다. 그대의 필요에 따라, Keras는 [가장 적은 힘의 규칙](https://en.wikipedia.org/wiki/Rule_of_least_power)에 입각하는 좋은 방법이 될 수 있다. +당신의 딥러닝 어플리케이션이 Keras가 제공하는 것 이상의 유연성을 필요하는 지 파악하는 건 가치가 있습니다. 그대의 필요에 따라, Keras는 [가장 적은 힘의 규칙](https://en.wikipedia.org/wiki/Rule_of_least_power)에 입각하는 좋은 방법이 될 수 있습니다. #### 요약 - Keras : 좀 더 간결한 API From f0e3a01de145f6c7d128506585c8c967651f6eb6 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:52:22 +0900 Subject: [PATCH 15/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index 45b37e8..d5fa2ce 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -93,7 +93,7 @@ PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않습니 - PyTorch : 더 유연하고, 딥러닝 개념을 깁게 이해하는데 도움을 줌 ### Keras vs PyTorch : 인기와 학습자료 접근성 -프레임워크의 인기는 단지 유용성의 대리만은 아니다. 작업 코드가 있는 튜토리얼, 리포지토리 그리고 단체 토론 등 커뮤니티 지원도 중요합니다. 2018년 6월 현재, Keras와 PyTorch는 GitHub과 arXiv 논문에서 인기를 누리고 있습니다.(Keras를 언급한 대부분의 논문들은 Tensorflow 백엔드 또한 언급하고 있습니다.) KDnugget에 따르면, Keras와 PyTorch는 가장 빠르게 성장하는 [데이터 과학 도구들](https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html)입니다. +프레임워크의 인기는 단지 유용성의 대리만은 아닙니다. 작업 코드가 있는 튜토리얼, 리포지토리 그리고 단체 토론 등 커뮤니티 지원도 중요합니다. 2018년 6월 현재, Keras와 PyTorch는 GitHub과 arXiv 논문에서 인기를 누리고 있습니다.(Keras를 언급한 대부분의 논문들은 Tensorflow 백엔드 또한 언급하고 있습니다.) KDnugget에 따르면, Keras와 PyTorch는 가장 빠르게 성장하는 [데이터 과학 도구들](https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html)입니다. ![Percentof ML papers that mention...](https://github.com/KerasKorea/KEKOxTutorial/blob/issue_42/media/42_1.png) From 4ce1bcc4465c73d532efd81ce63f67ac675f8447 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:54:24 +0900 Subject: [PATCH 16/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index d5fa2ce..a8ad415 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -101,7 +101,7 @@ PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않습니 두 프레임워크는 만족스러운 참고 문서를 갖고 있지만, PyTorch는 강력한 커뮤니티 지원을 제공합니다. 해당 커뮤니티 게시판은 당신이 난관에 부딪쳤거나 참고 문서나 스택오버플로우는 당신이 필요로 하는 정답이 없다면 방문하기 좋은 곳이다. -anecdotally, 우리는 특정 신경망 구조에서 초심자 수준의 딥러닝 코스를 PyTorch보다 Keras로 더 쉽게 접근할 수 있다는 걸 발견했습니다. Keras에서 제공하는 코드의 가독성과 실험을 쉽게 해주는 장점으로 인해, Keras는 딥러닝 열광자, 튜터, 고수준의 kaggle 우승자들에 의해 많이 쓰이게 될 겁니다. +한 일화를 들자면, 우리는 특정 신경망 구조에서 초심자 수준의 딥러닝 코스를 PyTorch보다 Keras로 더 쉽게 접근할 수 있다는 걸 발견했습니다. Keras에서 제공하는 코드의 가독성과 실험을 쉽게 해주는 장점으로 인해, Keras는 딥러닝 열광자, 튜터, 고수준의 kaggle 우승자들에 의해 많이 쓰이게 될 겁니다. Keras 자료와 딥러닝 코스의 예시로, ["Starting deep learning hands-on: image classification on CIFAR-10"](https://blog.deepsense.ai/deep-learning-hands-on-image-classification/#_ga=2.52232937.114026073.1540369751-2000517400.1540369751)와 ["Deep Learning with Rython"](https://www.manning.com/books/deep-learning-with-python)를 참조하십시오. PyTorch 자료로는, 신경망의 내부 작업을 학습하는데 더 도던적이고 포괄적인 접근법을 제공하는 공식 튜토리얼을 추천합니다. PyTorch에 대한 전반적인 내용을 보려면, 이 [문서](http://www.goldsborough.me/ml/ai/python/2018/02/04/20-17-20-a_promenade_of_pytorch/)를 참조하세요. From 8fed211df731e4227f75f173a8d1f3bd40751832 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:54:35 +0900 Subject: [PATCH 17/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index a8ad415..23397c9 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -123,7 +123,7 @@ Keras로 기본 신경망을 만든 사용자들은 PyTorch 사용자들보다 PyTorch는 python기반으로 휴대할 수 없는 pickle에 모델을 저장하지만, Keras는 JSON + H5 파일을 사용하는 안전한 접근 방식의 장점을 활용합니다.(일반적으로 Keras에 저장하는게 더 어렵습니다.) 또한 [R에도 Keras](https://keras.rstudio.com/)가 있습니다. 이 경우, R을 사용하여 데이터 분석팀과 협력해야 할 수도 있습니다. -Tensorflow에서 실행되는 Keras는 [모바일용 Tensorflow](https://www.tensorflow.org/mobile/mobile_intro)(혹은 [Tensorflow Lite](https://www.tensorflow.org/mobile/tflite/index))를 통해 모바일 플랫폼에 구축할 수 있는 다양한 솔리드 옵션을 제공합니다. [Tensorflow.js](https://js.tensorflow.org/) 혹은 [Keras.js](https://github.com/transcranial/keras-js)를 사용하여 멋진 웹 애플리케이션을 배포할 수 있습니다. 예를 들어, Piotr와 그의 학생들이 만든, [시험 공포증 유발 요소를 탐지하는 딥러닝 브라우저 플러그인](https://github.com/cytadela8/trypophobia)를 보세요. +Tensorflow에서 실행되는 Keras는 [모바일용 Tensorflow](https://www.tensorflow.org/mobile/mobile_intro)(혹은 [Tensorflow Lite](https://www.tensorflow.org/mobile/tflite/index))를 통해 모바일 플랫폼에 구축할 수 있는 다양한 솔리드 옵션을 제공합니다. [Tensorflow.js](https://js.tensorflow.org/) 혹은 [Keras.js](https://github.com/transcranial/keras-js)를 사용하여 멋진 웹 애플리케이션을 배포할 수 있습니다. 예를 들어, Piotr와 그의 학생들이 만든, [시험 공포증 유발 요소를 탐지하는 딥러닝 브라우저 플러그인](https://github.com/cytadela8/trypophobia)을 보세요. PyTorch 모델을 추출하는 건 python 코드때문에 더 부담되기에, 현재 많이 추천하는 접근방식은 [ONNX](https://pytorch.org/docs/master/onnx.html)를 사용하여 PyTorch 모델을 Caffe2로 변환하는 것입니다. From 759ba187c539d1f50536abeafcd5ec79501c1d10 Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:54:39 +0900 Subject: [PATCH 18/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index 23397c9..b0113cc 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -125,7 +125,7 @@ PyTorch는 python기반으로 휴대할 수 없는 pickle에 모델을 저장하 Tensorflow에서 실행되는 Keras는 [모바일용 Tensorflow](https://www.tensorflow.org/mobile/mobile_intro)(혹은 [Tensorflow Lite](https://www.tensorflow.org/mobile/tflite/index))를 통해 모바일 플랫폼에 구축할 수 있는 다양한 솔리드 옵션을 제공합니다. [Tensorflow.js](https://js.tensorflow.org/) 혹은 [Keras.js](https://github.com/transcranial/keras-js)를 사용하여 멋진 웹 애플리케이션을 배포할 수 있습니다. 예를 들어, Piotr와 그의 학생들이 만든, [시험 공포증 유발 요소를 탐지하는 딥러닝 브라우저 플러그인](https://github.com/cytadela8/trypophobia)을 보세요. -PyTorch 모델을 추출하는 건 python 코드때문에 더 부담되기에, 현재 많이 추천하는 접근방식은 [ONNX](https://pytorch.org/docs/master/onnx.html)를 사용하여 PyTorch 모델을 Caffe2로 변환하는 것입니다. +PyTorch 모델을 추출하는 건 python 코드 때문에 더 부담되기에, 현재 많이 추천하는 접근방식은 [ONNX](https://pytorch.org/docs/master/onnx.html)를 사용하여 PyTorch 모델을 Caffe2로 변환하는 것입니다. #### 요약 - Keras : (Tensorflow backend를 통해) 더 많은 개발 옵션을 제공하고, 모델을 쉽게 추출할 수 있음. From f49a4b67024a90d3fb9c87a9297292f075c9131d Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:55:19 +0900 Subject: [PATCH 19/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index b0113cc..d5c3660 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -133,7 +133,7 @@ PyTorch 모델을 추출하는 건 python 코드 때문에 더 부담되기에, ### Keras vs PyTorch : 성능 > 미리 측정된 최적화는 프로그래밍에서 모든 악의 근원입니다. - Donald Knuth -대부분의 인스턴스에서, 속도 측정에서의 차이는 프레임워크 선택을 위한 주요 요점은 아닙니다.(특히, 학습할 때) GPU 시간은 데이터 과학자의 시간보다 더 인색합니다. 게다가, 학습하는 동안 발생하는 성능의 병목현상은 실패한 실험이나, 최적화하지 않은 신경망이나 데이터 로딩(loading)이 원인일 수 있습니다. 완벽을 위해, 여전히 우리는 해당 주제를 다뤄야할 compel을 느낍니다. 우리는 두 가지 비교사항을 제안합니다. +대부분의 인스턴스에서, 속도 측정에서의 차이는 프레임워크 선택을 위한 주요 요점은 아닙니다.(특히, 학습할 때) 데이터 과학자의 시간이 GPU 시간보다는 더 비싸기 때문입니다. 게다가, 학습하는 동안 발생하는 성능의 병목현상은 실패한 실험이나, 최적화하지 않은 신경망이나, 데이터를 불러오는 과정(loading)이 원인일 수 있습니다. 그럼에도, 제대로 마무리를 하려면, 우리는 해당 주제를 다뤄야 할 필요성을 느낍니다. 우리는 두 가지 비교사항을 제안합니다. - [Tensorflow, Keras 그리고 PyTorch를 비교](https://wrosinski.github.io/deep-learning-frameworks/) by Wojtek Rosinski - [딥러닝 프레임워크들에 대한 비교 : 로제타 스톤식 접근](https://github.com/ilkarman/DeepLearningFrameworks/) by Microsoft From 267b025eb89af2f076b92d0a617af2d274ef89af Mon Sep 17 00:00:00 2001 From: Yeongkyu Kim Date: Thu, 25 Oct 2018 22:55:28 +0900 Subject: [PATCH 20/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md Co-Authored-By: mike2ox --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index d5c3660..a87796f 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -149,7 +149,7 @@ PyTorch는 Tensorflow만큼 빠르며, RNN에선 잠재적으로 더 빠릅니 - 학습 속도에 대한 걱정과 달리, PyTorch가 Keras를 능가 ### Keras vs PyTorch : 결론 -Keras와 PyTorch는 배우기위한 첫번째 딥러닝 프레임워크로 좋은 선택입니다. +Keras와 PyTorch는 배우기 위한 첫번째 딥러닝 프레임워크로 좋은 선택입니다. 만약 당신이 수학자, 연구자, 혹은 당신의 모델이 실제로 어떻게 작동하는지 알고 싶다면, PyTorch를 선택하길 권장합니다. 고급 맞춤형 알고리즘(그리고 디버깅)이 필요한 경우(ex. [YOLOv3](https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/) 혹은 [LSTM](https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983)을 사용한 객체 인식) 또는 신경망 이외의 배열 식을 최적화해야 할 경우(ex. [행렬 분해](http://blog.ethanrosenthal.com/2017/06/20/matrix-factorization-in-pytorch/) 혹은 [word2vec](https://adoni.github.io/2017/11/08/word2vec-pytorch/) 알고리즘)에 빛을 발합니다. plug & play 프레임워크를 원한다면, Keras는 확실히 더 쉬울 겁니다. 즉, 수학적 구현의 세부 사항들에 많은 시간을 들이지 않고도 모델을 신속하게 제작, 학습 그리고 평가할 수 있습니다. From 36f3d3d1e5ac633ab57557622b7de1d055469505 Mon Sep 17 00:00:00 2001 From: moonhyeok song Date: Thu, 25 Oct 2018 23:36:04 +0900 Subject: [PATCH 21/21] Update 42_keras_or_pytorch_as_your_first_deep_learning_framework.md --- 42_keras_or_pytorch_as_your_first_deep_learning_framework.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md index a87796f..01bcfb5 100644 --- a/42_keras_or_pytorch_as_your_first_deep_learning_framework.md +++ b/42_keras_or_pytorch_as_your_first_deep_learning_framework.md @@ -99,7 +99,7 @@ PyTorch보다 간단한 Keras는 더이상 장난감을 의미하진 않습니 > 지난 6년간 43k개의 ML논문을 기반으로, arxiv 논문들에서 딥러닝 프레임워크에 대한 언급에 대한 자료입니다. Tensorflow는 전체 논문의 14.3%, PyTorch는 4.7%, Keras 4.0%, Caffe 3.8%, Theano 2.3%, Torch 1.5. MXNet/chainer/cntk는 1% 이하로 언급되었습니다. [참조](https://t.co/YOYAvc33iN) - Andrej Karpathy (@karpathy) -두 프레임워크는 만족스러운 참고 문서를 갖고 있지만, PyTorch는 강력한 커뮤니티 지원을 제공합니다. 해당 커뮤니티 게시판은 당신이 난관에 부딪쳤거나 참고 문서나 스택오버플로우는 당신이 필요로 하는 정답이 없다면 방문하기 좋은 곳이다. +두 프레임워크는 만족스러운 참고 문서를 갖고 있지만, PyTorch는 강력한 커뮤니티 지원을 제공합니다. 해당 커뮤니티 게시판은 당신이 난관에 부딪쳤거나 참고 문서나 스택오버플로우에 당신이 필요로 하는 정답이 없다면 방문하기 좋은 곳이다. 한 일화를 들자면, 우리는 특정 신경망 구조에서 초심자 수준의 딥러닝 코스를 PyTorch보다 Keras로 더 쉽게 접근할 수 있다는 걸 발견했습니다. Keras에서 제공하는 코드의 가독성과 실험을 쉽게 해주는 장점으로 인해, Keras는 딥러닝 열광자, 튜터, 고수준의 kaggle 우승자들에 의해 많이 쓰이게 될 겁니다.