I am learning about domain adaptation.

I picked a problem where I need to train my NN on SVHN and test on MNIST.

First issue is that SVHN is RGB and MNIST is grayscale. I resolved it by using this transform

`transforms.Lambda(lambda x: x.repeat(3, 1, 1))`

.

I successfully train my model, and when I need to test it I get the following error:

`ValueError: Expected input batch_size (64) to match target batch_size (100).`

Here is my network configuration:

```
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(500, 50)
self.fc2 = nn.Linear(50, 10)
```

Here is my forward method:

```
def forward(self, x):
print("1", x.shape)
x = F.relu(F.max_pool2d(self.conv1(x), 2))
print("2", x.shape)
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
print("3", x.shape)
x = x.view(-1, 500)
print("4", x.shape)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
```

Running it on train, I get

```
1 torch.Size([50, 3, 32, 32])
2 torch.Size([50, 10, 14, 14])
3 torch.Size([50, 20, 5, 5])
4 torch.Size([50, 500])
```

But on test, I get this, after which I get the specified error:

```
1 torch.Size([7, 3, 32, 32])
2 torch.Size([7, 10, 14, 14])
3 torch.Size([7, 20, 5, 5])
4 torch.Size([7, 500])
1 torch.Size([100, 3, 28, 28])
2 torch.Size([100, 10, 12, 12])
3 torch.Size([100, 20, 4, 4])
4 torch.Size([64, 500])
```

I am also attaching the train and test methods used:

```
def train( model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test( model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
```

Any help appreciated.