Handwritten-Digit-Recognizer

It Recognizes the handwritten digits, Which can be useful in automating tasks where humans are needed to read handwritten documents.

1. Import the libraries and load the dataset

    First, we are going to import all the modules that we are going to need for training our model. 
    The Keras library already contains some datasets and MNIST is one of them. 
    So we can easily import the dataset and start working with it. 
    The mnist.load_data() method returns us the training data, its labels and also the testing data and its labels.
   

2. Preprocess the data

    The image data cannot be fed directly into the model so we need to perform some operations 
    and process the data to make it ready for our neural network. 
    The dimension of the training data is (60000,28,28). 
    The CNN model will require one more dimension so we reshape the matrix to shape (60000,28,28,1).
   

3. Create the model

    Now we will create our CNN model in Python data science project. 
    A CNN model generally consists of convolutional and pooling layers. 
    It works better for data that are represented as grid structures, 
    this is the reason why CNN works well for image classification problems. 
    The dropout layer is used to deactivate some of the neurons and while training, 
    it reduces offer fitting of the model. We will then compile the model with the Adadelta optimizer.
   

4. Train the model

    The model.fit() function of Keras will start the training of the model. It takes the training data, validation data, epochs, and batch size.
   
    It takes some time to train the model. After training, we save the weights and model definition in the ‘Handwritten-digit-mnist-model.h5’ file.
   

5. Evaluate the model

    We have 10,000 images in our test dataset which will be used to evaluate how good our model works. 
    The testing data was not involved in the training of the data therefore, it is new data for our model. 
    The MNIST dataset is well balanced so we can get around 99% accuracy.