import torch


import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter

class Net1(nn.Module):
    def __init__(self):
        super(Net1,self).__init__()
        self.conv1=nn.Conv2d(1,10,kernel_size=5)
        self.conv2=nn.Conv2d(10,20,kernel_size=5)
        self.conv2_drop=nn.Dropout2d()
        self.fc1=nn.Linear(320,50)
        self.fc2=nn.Linear(50,10)
        self.bn=nn.BatchNorm2d(20)

    def forward(self,x):
        x=F.max_pool2d(self.conv1(x),2)
        x=F.relu(x)+F.relu(-x)
        x=F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)),2))
        x=self.bn(x)
        x=x.view(-1,320)
        x=F.relu(self.fc1(x))
        x=F.dropout(x,training=self.training)
        x=self.fc2(x)
        x=F.softmax(x,dim=1)
        return x

dummy_input=torch.rand(13,1,28,28)
model=Net1()
with SummaryWriter(comment='Net1') as w:
    w.add_graph(model,(dummy_input,))