convolutional_nn.py

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


class ConvolutionalNN(nn.Module):
    def __init__(self):
        super(ConvolutionalNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 20, kernel_size=5, stride=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(20, 20, kernel_size=5, stride=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.drop_out = nn.Dropout()
        self.fc1 = nn.Linear(16280, 500)
        self.fc2 = nn.Linear(500, 30)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.drop_out(out)
        out = self.fc1(out)
        out = self.fc2(out)
        return F.log_softmax(out, dim=1)