Same Network gives different losses

username: thesekyi
cooked: <p>I have two networks which are the same. One network uses <code>nn.Sequential</code> and the other uses a combination of  different <code>nn.X</code>. However, when I train them on the same image, network <code>A</code> seem to converge but network <code>B</code> doesn’t</p>
<p><strong>Network A</strong></p>
<pre><code class="lang-auto">class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5,stride=1)
        self.pool  = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5,stride=1)
        self.fc1   = nn.Linear(16 * 4 * 4, 120)
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 3)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
</code></pre>
<p><strong>Network B</strong></p>
<pre><code class="lang-auto">import torch
from torch import nn

import functools
import operator

class CNN(nn.Module):
    """Basic Pytorch CNN implementation"""

    def __init__(self, in_channels, out_channels, input_dim):
        nn.Module.__init__(self)
        self.features = nn.Sequential(
            nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5, stride=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),

            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
        )

        num_features_before_fcnn = functools.reduce(operator.mul, list(self.features(torch.rand(1, *input_dim)).shape))

        self.classifier = nn.Sequential(
            nn.Linear(in_features=num_features_before_fcnn, out_features=120),
            nn.Linear(in_features=120, out_features=84),
            nn.Linear(in_features=84, out_features=out_channels),

        )

    def forward(self, x):
        out = self.features(x)
        out = out.view(-1, 16 * 4 * 4)  # flatten the vector
        out = self.classifier(out)
        return out
</code></pre>
<p><strong>Training loop</strong></p>
<pre><code class="lang-auto">criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

def train(criterion,model,dl,optimizer,epoch,device,log_freq):
    lost_lst = []
    running_loss = 0.0
    for i in range(epoch):
        for batch_idx,(data,target) in enumerate(dl):
            data,target = data.to(device),target.to(device)
            optimizer.zero_grad()
        
            output = model(data)
            loss = criterion(output,target).to(device)
            loss.backward()
            optimizer.step()
            
            running_loss += loss.item()
            if i % log_freq == 0:
                print(f"Epoch {i} batch index {batch_idx} loss {running_loss/log_freq}")
                lost_lst.append(running_loss)
                running_loss = 0.0
    return lost_lst         
``
I maintained all other things(optimizer,epoch,criterion etc)  for training in both cases
Am I missing something?</code></pre>

username: hash-ir
cooked: <p>Are you sure they are the same networks? In network B, the linear layers are not followed by any activation unlike in network A. This essentially means that there is a one big linear layer which makes your classifier.</p>

username: thesekyi
cooked: <p>You’re right. I missed out on that. So essential, the replica of A is</p>
<pre><code class="lang-auto">import torch
from torch import nn

import functools
import operator

class CNN(nn.Module):
    """Basic Pytorch CNN implementation"""

    def __init__(self, in_channels, out_channels, input_dim):
        nn.Module.__init__(self)
        self.features = nn.Sequential(
            nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5, stride=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),

            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
        )

        num_features_before_fcnn = functools.reduce(operator.mul, list(self.features(torch.rand(1, *input_dim)).shape))

        self.classifier = nn.Sequential(
            nn.Linear(in_features=num_features_before_fcnn, out_features=120),
            nn.ReLU(inplace=True),
            nn.Linear(in_features=120, out_features=84),
            nn.ReLU(inplace=True),
            nn.Linear(in_features=84, out_features=out_channels),

        )

    def forward(self, x):
        out = self.features(x)
        out = out.view(-1, 16 * 4 * 4)  # flatten the vector
        out = self.classifier(out)
        return out
</code></pre>