A simple architecture is used to classification task of Fashion MNIST dataset. This model achieves ~90% accuracy.
usage: cnn.py [-h] [-bs BATCH_SIZE] [-nbw NUMBER_WORKER] [-ep EPOCHES]
[-mn MODEL_NAME] [-lr LEARNING_RATE]
{train,test,demo}
positional arguments:
{train,test,demo} Different mode of script: train|test|demo
optional arguments:
-h, --help show this help message and exit
-bs BATCH_SIZE, --batch_size BATCH_SIZE
Batch size will used in training phase.
-nbw NUMBER_WORKER, --number_worker NUMBER_WORKER
number of worker used on prepare data.
-ep EPOCHES, --epoches EPOCHES
number of epoches to run.
-mn MODEL_NAME, --model_name MODEL_NAME
name of model without extension, the extension is
added automatically.
-lr LEARNING_RATE, --learning_rate LEARNING_RATE
learning rate of training.