<!--email_off-->## 1. Custom Loss Function &amp; Recompiling in Keras/TensorFlow

**Short answer:**  
You do **not** recompile the model just because you *defined* a custom loss function.  
You *do* need to compile (or recompile) the model **with** that custom loss function before training.

### When to recompile:
- ✅ **Yes, recompile** if your model was already compiled with a *different* loss, and you want to switch to your custom loss.
- ✅ **Yes, recompile** if you want to change other compile-time settings (optimizer, metrics, etc.).
- ❌ **No recompile needed** if you are simply defining the function and haven't compiled the model yet — you just pass it to `model.compile(loss=my_custom_loss)`.
- ❌ **No recompile needed** if you tweak the *internals* of the custom loss function *after* training has started — but you’ll need to retrain because the loss changed.

### Example — defining and using a custom loss:

```python
import tensorflow as tf

def custom_mse(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_true - y_pred))

model = tf.keras.Sequential([...])

# Compile with the custom loss
model.compile(optimizer='adam', loss=custom_mse, metrics=['mae'])

model.fit(x_train, y_train, epochs=10)
```

If you later change the loss function:

```python
# Recompile with a different custom loss
model.compile(optimizer='adam', loss=another_custom_loss)
model.fit(...)  # retrain
```

&gt; ⚠️ **Note:** Recompiling resets the optimizer state (learning rate schedule, momentum, etc.), but it does **not** reset the model weights. You still need to retrain.

---

## 2. Default Loss Function in PyTorch

**Short answer:**  
**PyTorch does not have a default loss function.**  
You must explicitly choose and specify a loss function — there is no automatic/loss "built-in" applied to a model.

### Why there is no default:
- PyTorch separates the **model** from the **training logic**. The loss is just another module/function you pick.
- You define the loss inside your training loop manually.

### Common loss functions in `torch.nn`:

| Problem type                | Typical loss                       |
|-----------------------------|------------------------------------|
| Binary classification       | `nn.BCELoss`, `nn.BCEWithLogitsLoss` |
| Multi-class classification  | `nn.CrossEntropyLoss`              |
| Regression                  | `nn.MSELoss`, `nn.L1Loss`          |
| Contrastive / embedding     | `nn.TripletMarginLoss`, `nn.CosineEmbeddingLoss` |

### Example — specifying a loss manually:

```python
import torch
import torch.nn as nn

model = nn.Linear(10, 2)
criterion = nn.CrossEntropyLoss()   # you choose this
optimizer = torch.optim.Adam(model.parameters())

for epoch in range(10):
    optimizer.zero_grad()
    outputs = model(x_batch)
    loss = criterion(outputs, y_batch)   # loss applied manually
    loss.backward()
    optimizer.step()
```

If you *don't* specify a loss in the training loop, `loss.backward()` isn't called and the model never learns.  
So: **there is no default loss — you must define it.**

---

## Summary

| Question                                      | Answer |
|-----------------------------------------------|--------|
| Should I recompile the model after defining a custom loss? | Only if the model was already compiled — then yes, recompile **with** the new loss. If you haven't compiled yet, just pass it at compile time. |
| Does PyTorch have a default loss function?    | **No.** You must choose and specify a loss function explicitly (e.g., `nn.CrossEntropyLoss`, `nn.MSELoss`). |

If you want a specific example for your use case (regression, classification, etc.), let me know and I can tailor one!<!--/email_off-->