CNNMnist

CNNMnist is a mini-project to apply convolutional neural network to MNIST dataset. The whole flow contains two parts:

• Pipeline for model training: task_01_cnn_mnist_pipeline.py
• Script to classify an input image by a trained model: task_02_cnn_mnist_predict.py

Usage

• Model training pipeline can be started by task_01_cnn_mnist_pipeline.py -i <input_dataset_dir>
• Recognize the digit on an input image can done by task_02_cnn_mnist_predict.py -i <input_image> -m <trained_model>

Demonstration of the steps above can be found in demo_task_01_cnn_mnist_pipeline.ipynb and demo_task_02_cnn_mnist_predict.ipynb. Details about the model setup, modules of pipeline steps can be found in class CNNMnist.

Demo Result

Based on training history, we can conclude the model from epoch 5 is good for the purpose of this mini-project.

Out best model performs quite consistently across training, validation and test sets without overfitting.

Dataset	Loss	Accuracy
Training set (48k)	0.0644	0.9863
Validation set (12k)	0.0536	0.9859
Test set (10k)	0.0436	0.9874

Given a input image and our trained model, digit on the image is correctly predicted.

CNN Model

Requirement

• Numpy
• Keras and Tensorflow
• Dataset CNNMnist You can run ./data/download_data.sh to download it.