cnn.py

from torch import nn
from torchsummary import summary


class CNNNetwork(nn.Module):

    def __init__(self, dropoutProb):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=16,
                kernel_size=3,
                stride=1,
                padding=2
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
            ,nn.BatchNorm2d(16)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(
                in_channels=16,
                out_channels=32,
                kernel_size=3,
                stride=1,
                padding=2
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
            nn.BatchNorm2d(32),
            nn.Dropout(dropoutProb)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(
                in_channels=32,
                out_channels=64,
                kernel_size=3,
                stride=1,
                padding=2
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
            nn.BatchNorm2d(64),
            nn.Dropout(dropoutProb)
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(
                in_channels=64,
                out_channels=128,
                kernel_size=3,
                stride=1,
                padding=2
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
            nn.BatchNorm2d(128),
            nn.Dropout(dropoutProb)
        )
        self.flatten = nn.Flatten()
        self.linear = nn.Linear(128 * 5 * 4 , 4)
        self.softmax = nn.Softmax(dim=1)


    
    def forward(self, input_data):
        x = self.conv1(input_data)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.flatten(x)
        logits = self.linear(x)
        predictions = self.softmax(logits)
        return predictions


if __name__ == "__main__":
    cnn = CNNNetwork()
    summary(cnn.cuda(), (1, 64, 44))