# Implementing BCEWithLogitsLoss from pytorch in keras

I have a model that I am trying to train on a dataset which has a class imbalance. The problem is a multilabel classification problem (each sample has 1 or more labels). I also have weights for each class which I have calculated for my dataset. I did see this implementation: BCEWithLogitsLoss in Keras

This is the equivalent in pytorch:

```
criterion = nn.BCEWithLogitsLoss(pos_weight=trainset.labels_weights.to(DEVICE))
```

so I tried passing this to my model:

```
def get_weighted_loss(weights):
def weighted_loss(y_true, y_pred):
xent = tf.compat.v2.losses.BinaryCrossentropy(from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE)
weighted_loss = tf.reduce_mean(xent(y_true, y_pred) * weights)
return weighted_loss
```

and compiling the model as so:

```
model.compile(optimizer=optim, loss=get_weighted_loss(list(train_generatorLat.labels_weights.values())), metrics=[full_multi_label_metric])
```

where `list(train_generatorLat.labels_weights.values())`

is a list of floats (weights) for each of the classes ranging from 1.0 to 5.0 where a weight of 1 is given to labels with the most examples and 5.0 to labels with the least examples

but I get the following error:

```
AttributeError Traceback (most recent call last)
<ipython-input-108-98496152ec7d> in <module>
----> 1 model.compile(optimizer=optim, loss=get_weighted_loss(list(train_generatorLat.labels_weights.values())), metrics=[full_multi_label_metric])
2 model.summary()
/gpfs/ysm/project/kl533/conda_envs/dlnn/lib/python3.6/site-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
340 with K.name_scope(self.output_names[i] + '_loss'):
341 output_loss = weighted_loss(y_true, y_pred,
--> 342 sample_weight, mask)
343 if len(self.outputs) > 1:
344 self.metrics_tensors.append(output_loss)
/gpfs/ysm/project/kl533/conda_envs/dlnn/lib/python3.6/site-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask)
415 if weights is not None:
416 # reduce score_array to same ndim as weight array
--> 417 ndim = K.ndim(score_array)
418 weight_ndim = K.ndim(weights)
419 score_array = K.mean(score_array,
/gpfs/ysm/project/kl533/conda_envs/dlnn/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in ndim(x)
617 ```
618 """
--> 619 dims = x.get_shape()._dims
620 if dims is not None:
621 return len(dims)
AttributeError: 'NoneType' object has no attribute 'get_shape'
```

Any ideas on how I would go about doing this?

## Answers 1

Last layer should have a

`'sigmoid'`

activation.In

`compile`

your loss should be`loss='binary_crossentropy'`

.In

`fit`

or`fit_generator`

you will pass`class_weight=dictionary_of_weights`

.Where

`dictionary_of_weights`

is something like:being

`n+1`

the number of classes.