Robust-NN-Training/model.py at master · optimization-for-data-driven-science/Robust-NN-Training · GitHub

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from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn


class ConvNet(nn.Module):

    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, kernel_size=5)
        self.conv2 = nn.Conv2d(20, 50, kernel_size=5)
        self.fc1 = nn.Linear(800, 500)
        self.fc2 = nn.Linear(500, 10)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(2)
        self._init_weights()

    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, (nn.Conv2d)):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 1 / m.bias.numel())
            if isinstance(m, (nn.Linear)):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 1 / m.bias.numel())
            # print(m)
            # print(type(m))
            # nn.init.xavier_normal_(m.weight)
            # nn.init.constant_(m.bias, 1 / m.bias.numel())

    def forward(self, x):
        x = self.maxpool(self.relu(self.conv1(x)))
        x = self.maxpool(self.relu(self.conv2(x)))
        x = x.view(-1, 800)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)

        return x