This repo builds a convolutional neural network based on LENET from scratch to recognize the MNIST Database of handwritten digits.
This example is only based on the python library numpy to implement convolutional layers, maxpooling layers and fully-connected layers, also including backpropagation and gradients descent to train the network and cross entropy to evaluate the loss.
python main.py
In the main.py, you can modify the learning rate, epoch and batch size to train the CNN from scratch and evaluate the result. Besides, there is a provided pretrained weight file pretrained_weights.pkl.
Loadind data......
Preparing data......
Training Lenet......
=== Epoch: 0/1 === Iter:32 === Loss: 2.33 === BAcc: 0.09 === TAcc: 0.09 === Remain: 2 Hrs 32 Mins 35 Secs ===
=== Epoch: 0/1 === Iter:64 === Loss: 2.32 === BAcc: 0.06 === TAcc: 0.08 === Remain: 2 Hrs 32 Mins 37 Secs ===
=== Epoch: 0/1 === Iter:96 === Loss: 2.29 === BAcc: 0.06 === TAcc: 0.07 === Remain: 2 Hrs 31 Mins 49 Secs ===
=== Epoch: 0/1 === Iter:128 === Loss: 2.28 === BAcc: 0.12 === TAcc: 0.09 === Remain: 2 Hrs 35 Mins 49 Secs ===
=== Epoch: 0/1 === Iter:160 === Loss: 2.34 === BAcc: 0.03 === TAcc: 0.07 === Remain: 2 Hrs 31 Mins 48 Secs ===
=== Epoch: 0/1 === Iter:192 === Loss: 2.33 === BAcc: 0.09 === TAcc: 0.08 === Remain: 2 Hrs 31 Mins 14 Secs ===
=== Epoch: 0/1 === Iter:224 === Loss: 2.29 === BAcc: 0.16 === TAcc: 0.09 === Remain: 2 Hrs 32 Mins 3 Secs ===
=== Epoch: 0/1 === Iter:256 === Loss: 2.30 === BAcc: 0.16 === TAcc: 0.10 === Remain: 2 Hrs 31 Mins 47 Secs ===
=== Epoch: 0/1 === Iter:288 === Loss: 2.32 === BAcc: 0.09 === TAcc: 0.10 === Remain: 2 Hrs 31 Mins 58 Secs ===
...
python app.py
This is the demo to predict handwritten digits based on the python api flask to build a localhost website.
- learning rate: 0.01
- batch size: 100
- training accuracy: 0.94
- loss
https://medium.com/deep-learning-g/build-lenet-from-scratch-7bd0c67a151e
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