参加比赛期间设计的基于pytorch的深度学习特征分类器
Configuration: Python 3.6.9, Pytorch 1.7.1
model name: Self-Attention Based Multi-model Fusion Prediction Model.
self-attention mechanism is leveraged to fuse features got by sub-model.
python fit.py [-parameters]
parameters description:
-data_path
path of data used for fitting the model, e.g. ./data/trainingdata.csv
-random_seed
random seed
-lr
learning rate, default=0.01
-val_p
proportion of partition validation set, default=0.2
-batch_size
how many data batched for training, default=256:
-device
where to train the model, default='cuda'
-model_num
number of submodels(MLP\CNN etc. Classifier), default=5
-input_size
dimention of input featrue (number of column of input_file), default=28
-hidden_size
hidden state's dimention of linear layer, default=64
-output_size
whole model's output vector dimention, default=5
-dropout_p
probability applied by dropout layer, default=0.2
-model_save_dir
model parameter save dir, default='./model/'
-train_balance
whether to enhance data, default=True
python predict.py [-parameters]
parameters description:
-model_path
model path
-test_data
test data path
-save_dir
dir of prediction results, default="./data/testing_data/
-input_size
dimention of input featrue (number of column of input_file), default=28
the prediction result will be stored in save_dir