cnn_model.py

import torch.nn as nn
import torch.nn.functional as F
import torch


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels = 3,
                               out_channels = 6,
                               kernel_size = 3,
                               stride = 1,
                               padding = 0)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(in_channels = 6,
                               out_channels = 16,
                               kernel_size = 3,
                               stride = 1,
                               padding = 0)
        self.fc1 = nn.Linear(16 * 30 * 30, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 2)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 30 * 30)
        x = torch.sigmoid(self.fc1(x))
        x = torch.sigmoid(self.fc2(x))
        x = F.softmax(self.fc3(x))
        return x