RuntimeError: The size of tensor a (64) must match the size of tensor b (128) at non-singleton dimension 0

[Raj10Gupta]

@torch.no_grad()
def evaluate(model,val_loader):
model.eval()
outputs = [model.validation_step(batch) for batch in val_loader]
return model.validation_epoch_end(outputs)

def fit(epochs,lr,model,train_loader,val_loader,opt_func = torch.optim.SGD):
history = []
optimizer = opt_func(model.parameters(),lr)
for epoch in range(epochs):
model.train()
train_losses = []
for batch in train_loader:
loss = model.training_step(batch)
train_losses.append(loss)
loss.backward()
optimizer.step()
optimizer.zero_grad()

    result = evaluate(model,val_loader)
    result['train_loss'] = torch.stack(train_losses).mean().item()
    model.epoch_end(epoch,result)
    history.append(result)
return history    

---

RuntimeError Traceback (most recent call last)
in
----> 1 history = fit(epochs,learning_rate, model,train_loader,test_loader,opt_func = optimizer)

in fit(epochs, lr, model, train_loader, val_loader, opt_func)
18 optimizer.zero_grad()
19
---> 20 result = evaluate(model,val_loader)
21 result['train_loss'] = torch.stack(train_losses).mean().item()
22 model.epoch_end(epoch,result)

/opt/conda/lib/python3.7/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
24 def decorate_context(*args, **kwargs):
25 with self.class():
---> 26 return func(*args, **kwargs)
27 return cast(F, decorate_context)
28

in evaluate(model, val_loader)
2 def evaluate(model,val_loader):
3 model.eval()
----> 4 outputs = [model.validation_step(batch) for batch in val_loader]
5 return model.validation_epoch_end(outputs)
6

in (.0)
2 def evaluate(model,val_loader):
3 model.eval()
----> 4 outputs = [model.validation_step(batch) for batch in val_loader]
5 return model.validation_epoch_end(outputs)
6

in validation_step(self, batch)
14 out = self(images)
15 loss = F.cross_entropy(out,labels)
---> 16 acc = accuracy(out,labels)
17 return {'val_loss': loss.detach(), 'val_acc': acc}
18

in accuracy(outputs, val_loader)
1 def accuracy(outputs, val_loader):
2 _,preds = torch.max(outputs,dim=1)
----> 3 return torch.tensor(torch.sum(preds == labels).item() / len(preds))
4
5 class MalariaPrediction(nn.Module):

/opt/conda/lib/python3.7/site-packages/torch/tensor.py in wrapped(*args, **kwargs)
25 return handle_torch_function(wrapped, args, *args, **kwargs)
26 try:
---> 27 return f(*args, **kwargs)
28 except TypeError:
29 return NotImplemented

RuntimeError: The size of tensor a (64) must match the size of tensor b (128) at non-singleton dimension 0

[Raj10Gupta]

If you read it ,
pls send it solution
or share it to respective individual or community