Digit Recognizer is a library where the algorithm Multilayer Perceptron is implemented with intent to teach a neural network learn how to recognize handwritten numbers based on MNIST dataset. It's important remember, this is a basic implementation where there's NO OPTIMIZATION at algorithm, in other words, the training is SLOW but the results are satisfactory.
The book used as support material is: HAYKIN, Simon. Redes Neurais: Princípios e Prática.

A definition according to MNIST database of handwritten digits:
Is a database of handwritten digits, has a training set of 60,000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image (28 x 28). It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

$ pip install -r requirements.txt

A brief explanation of each method of this repository.

• NeuralNetwork.py

  • The constructor NeuralNetwork receives as argument the number of epochs and learning rate. e.g.

  NeuralNetwork(10, 0.5)

  • Add a layer to the network with quantity of neurons passed as argument.

  def add(self, num_nodes)

  • Receives the values of inputs and outputs of training dataset.

  def train(self, inputs, targets)

  • Receives a value and return the output of neural network.

  def query(self, in_list)

  • Receives an test input and output and return the precision of network.

  def acc(self, in_list, out_list)

  • Receives a name and save the neural network at the working directory.

  def save(self, file_name)

  • Receives a path of saved neural network and load into memory.

  def load(self, path_json)

• Image.py

  • Receives an output of mnist as array and return the corresponding value.

  def arrayToNum(array)

  • Receives an mninst array and the value generated for the network, then plots the image and print the value.

  plotNum(mnist_num, output_value)

• LoadMNIST.py

  • Receives the path of MNIST files and return the arrays of training and testing normalizeds.

  def generateData(path)