I am new to pytorch. I am constructing simple pytorch model using with nn.sequential and using layers. I am getting different number of parameters. Can someone guide or explain the reason for different number of parameters in model. My implementation is as
import torch
import torch.nn as nn
from pytorch_model_summary import summary

class Net1(nn.Module):
    def __init__(self):
        super(Net1, self).__init__()
        self.Seqconv= nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=2, padding= 1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True)
            )
    def  forward(self,x):       
        x= self.Seqconv(x)
        return x

class Net2(nn.Module):
    def __init__(self):
        super(Net2, self).__init__()
        self.conv=nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(3,3), 
                            stride=2, padding=1)
        self.bn  = nn.BatchNorm2d(32)
        self.relu = nn.ReLU()
        
        
    def  forward(self,x):       
        x= self.relu(self.bn(self.conv(x)))        
        return x


x= torch.randn(1,3,224,224)
print(summary(Net1(), x, show_input=True))
# Yield Total params: 928

print(summary(Net2(), x, show_input=True))
# Yield Total params: 960