02_PyTorch_Classification

[dwang4002]

Here is the code:
`torch.manual_seed(42)
torch.cuda.manual_seed(42)

Set the number of epochs

epochs = 100

Put data to target device

X_train, y_train = X_train.to(device), y_train.to(device)
X_test, y_test = X_test.to(device), y_test.to(device)

Build training and evaluation loop

for epoch in range(epochs):

Training

model_0.train()

#1. Forward pass
y_logits = model_0(X_train).squeeze()
y_pred = torch.round(torch.sigmoid(y_logits))

#2. Calculate accuracy/loss
loss = loss_fn(y_logits,
y_train)
acc = accuracy_fn(y_true=y_train,
y_pred=y_pred)

3. Optimizer zero grad

optimizer.zero_grad()

#4. Loss backward
loss.backward()

5. Optimzer step

optimizer.step()

Testing

model_0.eval()
with torch.inference_mode():
#1. Forward pass
test_logits = model_0(X_test).squeeze()
test_pred = torch.round(torch.sigmoid(test_logits))

# 2. Calculate loss/acc
test_loss = loss_fn(test_logits, 
                    y_test)
test_acc = accuracy_fn(y_true=y_test,
                       y_pred=test_pred)

Print out what's happening

if epoch % 10 == 0:
print(f"Epoch: {epoch} | Loss: {loss:.5f} | Acc: {acc:.2f} | Test loss: {test_loss:.5f} | Test Acc: {test_acc:.2f}")`

When I run it, I get:
`---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in <cell line: 13>()
48 # Print out what's happening
49 if epoch % 10 == 0:
---> 50 print(f"Epoch: {epoch} | Loss: {loss:.5f} | Acc: {acc:.2f} | Test loss: {test_loss:.5f} | Test Acc: {test_acc:.2f}")

/usr/local/lib/python3.10/dist-packages/torch/_tensor.py in format(self, format_spec)
964 if self.dim() == 0 and not self.is_meta and type(self) is Tensor:
965 return self.item().format(format_spec)
--> 966 return object.format(self, format_spec)
967
968 @_handle_torch_function_and_wrap_type_error_to_not_implemented

TypeError: unsupported format string passed to Tensor.format`

[LuluW8071]

print(f"Epoch: {epoch} | Loss: {loss.item():.5f} | Acc: {acc.item():.2f} | Test loss: {test_loss.item():.5f} | Test Acc: {test_acc.item():.2f}")

This will convert the tensor values to Python numbers allowing them to be formatted correctly in the print statement