Init for the autoencoder class

autoencoder_init.py

class Autoencoder(nn.Module):
    def __init__(self, epochs=100, batchSize=128, learningRate=1e-3):
        super(Autoencoder, self).__init__()
        # Encoder Network
        self.encoder = nn.Sequential(nn.Linear(784, 128),
                                     nn.ReLU(True),
                                     nn.Linear(128, 64),
                                     nn.ReLU(True),
                                     nn.Linear(64, 12),
                                     nn.ReLU(True),
                                     nn.Linear(12, 3))
        # Decoder Network
        self.decoder = nn.Sequential(nn.Linear(3, 12),
                                     nn.ReLU(True),
                                     nn.Linear(12, 64),
                                     nn.ReLU(True),
                                     nn.Linear(64, 128),
                                     nn.ReLU(True),
                                     nn.Linear(128, 784),
                                     nn.Tanh())

        self.epochs = epochs
        self.batchSize = batchSize
        self.learningRate = learningRate

        # Data + Data Loaders
        self.imageTransforms = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5])
        ])
        self.data = MNIST('./Data', transform=self.imageTransforms)
        self.dataLoader = torch.utils.data.DataLoader(dataset=self.data,
                                                      batch_size=self.batchSize,
                                                      shuffle=True)

        self.optimizer = torch.optim.Adam(self.parameters(), lr=self.learningRate, weight_decay=1e-5)
        self.criterion = nn.MSELoss()