classificatorCNN.py

import torch.nn as nn


class CustomClassifier(nn.Module):
    def __init__(self):
        super(CustomClassifier, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2)
        self.dropout1 = nn.Dropout(0.15)

        self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2)

        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)
        self.relu3 = nn.ReLU()

        self.fc1 = nn.Linear(100352, 128)
        self.relu4 = nn.ReLU()

        self.fc2 = nn.Linear(128, 256)
        self.relu5 = nn.ReLU()

        self.fc3 = nn.Linear(256, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.dropout1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = x.view(x.size(0), -1)  # Flatten
        x = self.fc1(x)
        x = self.relu4(x)
        x = self.fc2(x)
        x = self.relu5(x)
        x = self.fc3(x)
        x = self.sigmoid(x)

        return x