MNISTClassifier_02.py

def __init__(self, train_data_dir,batch_size=128,test_data_dir=None, num_workers=4):
        '''Constructor method 
        Parameters:
        train_data_dir (string): path of training dataset to be used either for training and validation
        batch_size (int): number of images per batch. Defaults to 128.
        test_data_dir (string): path of testing dataset to be used after training. Optional.
        num_workers (int): number of processes used by data loader. Defaults to 4.
        '''

        # Invoke constructor
        super(MNISTClassifier, self).__init__()

        # Set up class attributes
        self.batch_size = batch_size
        self.train_data_dir = train_data_dir
        self.test_data_dir = test_data_dir
        self.num_workers = num_workers

        # Define network layers as class attributes to be used
        self.conv_layer_1 = torch.nn.Sequential(
        # The first block is made of a convolutional layer (3 channels, 28x28 images and a kernel mask of 5),
        torch.nn.Conv2d(3,28, kernel_size=5),
        # a non linear activation function
        torch.nn.ReLU(),
        # a maximization layer, with mask of size 2
        torch.nn.MaxPool2d(kernel_size=2))

        # A second block is equal to the first, except for input size which is different
        self.conv_layer_2 = torch.nn.Sequential(
        torch.nn.Conv2d(28,10, kernel_size=2),
        torch.nn.ReLU(),
        torch.nn.MaxPool2d(kernel_size=2))

        # A dropout layer, useful to reduce network overfitting
        self.dropout1=torch.nn.Dropout(0.25)

        # A fully connected layer to reduce dimensionality
        self.fully_connected_1=torch.nn.Linear(250,18)

        # Another fine tuning dropout layer to make network fine tune
        self.dropout2=torch.nn.Dropout(0.08)

        # The final fully connected layer wich output maps to the number of desired classes
        self.fully_connected_2=torch.nn.Linear(18,10)