I tried to define the loss function as follows but there seems to be missing arguments: y_true and y_pred. However, I haven’t been able to figure out what to use there. Help, please

```
# Define a BinaryCrossentropy loss function. Use from_logits=True
loss_function=tf.keras.metrics.binary_crossentropy(from_logits=True)
```

Have a look on this page:

I did, my problem is that idk what exactly to write in place of y_true and y_pred. I understand if you can’t directly answer to this but could you give me a hint pleas?

I think the pages explains it pretty well:

## Args

`y_true`

Ground truth values. shape = `[batch_size, d0, .. dN]`

.

`y_pred`

The predicted values. shape = `[batch_size, d0, .. dN]`

.

y_true are the labels an y_pred are the predictions.

I solved it. The problem was not in the arguments but in the syntax: I wrote `tf.keras.metrics.binary_crossentropy`

instead of `tf.python.keras.losses.BinaryCrossentropy`

. With that change there is no need to specify y_true and y_pred

Hint is in the notebook. It says:

```
# Define a BinaryCrossentropy loss function. Use from_logits=True
loss_function=None
```

But you are using metrics.

Update: I am glad you solve it on your own.

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