Projects for students

• Final update: 2019. 05. 23.
• All right reserved @ ModuLabs 2019

Getting Started

Prerequisites

• TensorFlow above version 1.13
• Python 3.6 (recommend Anaconda)
• Python libraries:
  • numpy, matplotblib, pandas
  • PIL, imageio for images
  • fix_yahoo_finance for stock market prediction
• Jupyter notebook
• Ubuntu, OS X and Windows OS

CNN projects (Image segmentation)

Task

• GIANA dataset으로 위내시경 이미지에서 용종을 segmentation 해보자.
• 데이터 불러오기를 제외한 딥러닝 트레이닝 과정을 직접 구현해보는 것이 목표 입니다.
• This code is borrowed from TensorFlow tutorials/Image Segmentation which is made of tf.keras.layers and tf.enable_eager_execution().
• You can see the detail description tutorial link

Dataset

• I use below dataset instead of carvana-image-masking-challenge dataset in TensorFlow Tutorials which is a kaggle competition dataset.
  • carvana-image-masking-challenge dataset: Too large dataset (14GB)
• Gastrointestinal Image ANAlys Challenges (GIANA) Dataset (345MB)
  • Train data: 300 images with RGB channels (bmp format)
  • Train lables: 300 images with 1 channels (bmp format)
  • Image size: 574 x 500
• Training시 image size는 256으로 resize

Baseline code

• Dataset: train, test로 split
• Input data shape: (batch_size, 256, 256, 3)
• Output data shape: (batch_size, 256, 256, 1)
• Architecture:
  • 간단한 Encoder-Decoder 구조
  • U-Net 구조
  • tf.keras.layers 사용
• Training
  • tf.data.Dataset 사용
  • model.fit() 사용 for weight update
• Evaluation
  • MeanIOU: Image Segmentation에서 많이 쓰이는 evaluation measure
  • tf.version 1.13 API: tf.metrics.mean_iou
    • tf.enable_eager_execution()이 작동하지 않음
    • 따라서 예전 방식대로 tf.Session()을 이용하여 작성하거나 아래와 같이 2.0 version으로 작성하여야 함
  • tf.version 2.0 API: tf.keras.metrics.MeanIoU

Try some techniques

• Change model architectures (Custom model)
  • Try another models (U-Net 모델)
• Various regularization methods

RNN projects (Sentiment classification)

Task

• IMDB 영화사이트에서 50000개의 영화평을 가지고 positive/negative인지 구분해보자.
• 데이터 불러오기를 제외한 딥러닝 트레이닝 과정을 직접 구현해보는 것이 목표 입니다.

Dataset

• IMDB datasets

Base code

• Dataset: train, val, test로 split
• Input data shape: (batch_size, max_sequence_length)
• Output data shape: (batch_size, 1)
• Architecture:
  • RNN을 이용한 간단한 classification 모델 가이드
  • Embedding - SimpleRNN - Dense (with Sigmoid)
  • tf.keras.layers 사용
• Training
  • model.fit 사용
• Evaluation
  • model.evaluate 사용 for test dataset

Try some techniques

• Training-epochs 조절
• Change model architectures (Custom model)
  • Use another cells (LSTM, GRU, etc.)
  • Use dropout layers
• Embedding size 조절
  • 또는 one-hot vector로 학습
• Number of words in the vocabulary 변화
• pad 옵션 변화
• Data augmentation (if possible)

Authors

• Il Gu Yi