CODEWITHSUNDEEP
pythonkerasdeep-learningloss-function
Sreeram TP
Sreeram TP

Reputation: 11907

BCEWithLogitsLoss in Keras

How to implement BCEWithLogitsLoss in keras and use it as custom loss function while using Tensorflow as backend.

I have used BCEWithLogitsLoss in PyTorch which was defined in torch.

How to implement the same in Keras.?

Upvotes: 3

Views: 4997

Answers (1)

Jindřich
Jindřich

Reputation: 11220

In TensorFlow, you can directly call tf.nn.sigmoid_cross_entropy_with_logits which works both in TensorFlow 1.x and 2.0.

If you want to stick to Keras API, use tf.losses.BinaryCrossentropy and set from_logits=True in the constructor call.

Unlike PyTorch, there are not explicit per-example weights in the API. You can instead set reduction=tf.keras.losses.Reduction.NONE for the loss, do your weighting by explicit multiplication and reduce your loss using tf.reduce_mean.

xent = tf.losses.BinaryCrossEntropy(
    from_logits=True,
    reduction=tf.keras.losses.Reduction.NONE)
loss = tf.reduce_mean(xent(targets, pred) * weights))

Upvotes: 5

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