net_fewc.py

import torch

from torch.utils.data import Dataset, DataLoader
from torch import autograd
from torch.autograd import Variable
import torch.nn.functional as F
device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')


class CNNMnist(torch.nn.Module):
    def __init__(self,lamda=0):
        super(CNNMnist, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = torch.nn.Dropout2d()
        self.fc1 = torch.nn.Linear(320, 50)
        self.fc2 = torch.nn.Linear(50, 10)
        self.lamda = lamda

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)