import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.normalization import GroupNorm

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.bn1 = torch.nn.GroupNorm(32,32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.bn2 = torch.nn.GroupNorm(32,64)
        self.conv2_drop = nn.Dropout2d(p=0.25)
        self.fc1 = nn.Linear(9216, 128) 
        self.fc2 = nn.Linear(128, 47)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2_drop(F.max_pool2d(self.conv2(x), 2)))

        x = x.view(-1, 9216)
        x = F.relu(self.fc1(x))

        x = self.fc2(x)
        return x
