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# tf.keras.losses.BinaryCrossentropy

Computes the cross-entropy loss between true labels and predicted labels.

Use this cross-entropy loss when there are only two label classes (assumed to be 0 and 1). For each example, there should be a single floating-point value per prediction.

In the snippet below, each of the four examples has only a single floating-pointing value, and both `y_pred` and `y_true` have the shape `[batch_size]`.

#### Standalone usage:

````y_true = [[0., 1.], [0., 0.]]`
`y_pred = [[0.6, 0.4], [0.4, 0.6]]`
`# Using 'auto'/'sum_over_batch_size' reduction type.`
`bce = tf.keras.losses.BinaryCrossentropy()`
`bce(y_true, y_pred).numpy()`
`0.815`
```
````# Calling with 'sample_weight'.`
`bce(y_true, y_pred, sample_weight=[1, 0]).numpy()`
`0.458`
```
``` `# Using 'sum' reduction type.`
`bce = tf.keras.losses.BinaryCrossentropy(`
`    reduction=tf.keras.losses.Reduction.SUM)`
`bce(y_true, y_pred).numpy()`
`1.630`
` `
```
````# Using 'none' reduction type.`
`bce = tf.keras.losses.BinaryCrossentropy(`
`    reduction=tf.keras.losses.Reduction.NONE)`
`bce(y_true, y_pred).numpy()`
`array([0.916 , 0.714], dtype=float32)`
```

Usage with the `tf.keras` API:

``````model.compile(optimizer='sgd', loss=tf.keras.losses.BinaryCrossentropy())
``````

`from_logits` Whether to interpret `y_pred` as a tensor of logit values. By default, we assume that `y_pred` contains probabilities (i.e., values in [0, 1]). **Note - Using from_logits=True may be more numerically stable.
`label_smoothing` Float in [0, 1]. When 0, no smoothing occurs. When > 0, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of `label_smoothing` correspond to heavier smoothing.
`reduction` (Optional) Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training tutorial for more details.
`name` (Optional) Name for the op. Defaults to 'binary_crossentropy'.

## Methods

### `from_config`

View source

Instantiates a `Loss` from its config (output of `get_config()`).

Args
`config` Output of `get_config()`.

Returns
A `Loss` instance.

### `get_config`

View source

Returns the config dictionary for a `Loss` instance.

### `__call__`

View source

Invokes the `Loss` instance.

Args
`y_true` Ground truth values. shape = `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]`
`y_pred` The predicted values. shape = `[batch_size, d0, .. dN]`
`sample_weight` Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each loss element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns
Weighted loss float `Tensor`. If `reduction` is `NONE`, this has shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises
`ValueError` If the shape of `sample_weight` is invalid.