Hi, everyone,

Please help me out the below code snippet for weight decay implementation.

Especially, the part about the new â€śnet.lossesâ€ť which is not used in the previous implementations of custom training loop.

The previous implementations had just the losses accrued by the backward pass as shown below:

```
def train_epoch_ch3(net, train_iter, loss, updater): #@save
"""The training loop defined in Chapter 3."""
# Sum of training loss, sum of training accuracy, no. of examples
metric = Accumulator(3)
for X, y in train_iter:
# Compute gradients and update parameters
with tf.GradientTape() as tape:
y_hat = net(X)
# Keras implementations for loss takes (labels, predictions)
# instead of (predictions, labels) that users might implement
# in this book, e.g. `cross_entropy` that we implemented above
if isinstance(loss, tf.keras.losses.Loss):
l = loss(y, y_hat)
else:
l = loss(y_hat, y)
if isinstance(updater, tf.keras.optimizers.Optimizer):
params = net.trainable_variables
grads = tape.gradient(l, params)
updater.apply_gradients(zip(grads, params))
else:
updater(X.shape[0], tape.gradient(l, updater.params))
# Keras loss by default returns the average loss in a batch
l_sum = l * float(tf.size(y)) if isinstance(
loss, tf.keras.losses.Loss) else tf.reduce_sum(l)
metric.add(l_sum, accuracy(y_hat, y), tf.size(y))
# Return training loss and training accuracy
return metric[0] / metric[2], metric[1] / metric[2]
```

Did the previous implementations not have since there was no sequential model involved and in the MLP implementation, there is?

Also, please **help me out the phrase "losses occurred " during the forward pass.**. I assumed that losses were only applied during backward pass

This is a modification of the previous `train(lambd)`

method defined earlier in the section. In that initial method we add the l2_penalty scaled by the hyperparameter lambd:

`l = loss(net(X), y) + lambd * l2_penalty(w)`

In this concise implementation that term (`lambd + l2_penalty(w)`

) is calculated automatically for us in the dense layer since we initialized with the `kernel_regularizer=tf.keras.regularizers.l2(wd)`

(note the parameter name changed from `lambd`

to `wd`

[weight decay], but is the same hyperparameter).

We access this l2 norm penalty from the net by calling `net.losses`

and adding it to the MeanSquaredError loss.