/Deep-Learnig-with-MNIST

Digit recognizer trained on MNIST dataset using Deep Neural Nets (MLP, CNN and RNN)

Primary LanguagePython

Digit Recognizer using Deep Learning

Digit recognizer trained on MNIST dataset using TensorFlow 1.0 and Keras in Python 3.6.0.

Data Source : http://yann.lecun.com/exdb/mnist/

References :

https://www.tensorflow.org/get_started/mnist/pros

https://github.com/aymericdamien/TensorFlow-Examples

https://keras.io/getting-started/sequential-model-guide/

https://github.com/fchollet/keras/tree/master/examples

What is in this repo

mnist-tf-mlp.py, mnist-tf-cnn.py, mnist-tf-rnn.py

  • Implementation of deep neural nets (multi-layer perceptrons, convoluntional neural net and recurrent neural net) are trained against 55000 handwritten digit images using TensorFlow 1.0.
  • Evluation is made on the test set of 10000 handwritten digit images.

mnist-keras-mlp.py, mnist-keras-cnn.py, mnist-keras-rnn.py

  • Implementation of deep neural nets (multi-layer perceptrons, convoluntional neural net and recurrent neural net) are trained against 60000 handwritten digit images using Keras.
  • Evluation is made on the test set of 10000 handwritten digit images.

Model Evaluation

Methods Test accuracy (TensorFlow) Test accuracy (Keras)
Multi-layer perceptrons 94.4.% 97.6% (epochs=10)
Convolutional neural network 98.0.% 98.8% (epochs=5)
Recurrent neural network (LSTM) 97.6.% 97.5% (epochs=5)