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# tf.keras.losses.BinaryCrossentropy

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

Inherits From: `Loss`

### Used in the notebooks

Use this cross-entropy loss for binary (0 or 1) classification applications. The loss function requires the following inputs:

• `y_true` (true label): This is either 0 or 1.
• `y_pred` (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents a logit, (i.e, value in [-inf, inf] when `from_logits=True`) or a probability (i.e, value in [0., 1.] when `from_logits=False`).

Recommended Usage: (set `from_logits=True`)

With `tf.keras` API:

``````model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
....
)
``````

As a standalone function:

````# Example 1: (batch_size = 1, number of samples = 4)`
`y_true = [0, 1, 0, 0]`
`y_pred = [-18.6, 0.51, 2.94, -12.8]`
`bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)`
`bce(y_true, y_pred).numpy()`
`0.865`
```
````# Example 2: (batch_size = 2, number of samples = 4)`
`y_true = [[0, 1], [0, 0]]`
`y_pred = [[-18.6, 0.51], [2.94, -12.8]]`
`# Using default 'auto'/'sum_over_batch_size' reduction type.`
`bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)`
`bce(y_true, y_pred).numpy()`
`0.865`
`# Using 'sample_weight' attribute`
`bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()`
`0.243`
`# Using 'sum' reduction` type.`
`bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,`
`    reduction=tf.keras.losses.Reduction.SUM)`
`bce(y_true, y_pred).numpy()`
`1.730`
`# Using 'none' reduction type.`
`bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,`
`    reduction=tf.keras.losses.Reduction.NONE)`
`bce(y_true, y_pred).numpy()`
`array([0.235, 1.496], dtype=float32)`
```

Default Usage: (set `from_logits=False`)

````# Make the following updates to the above "Recommended Usage" section`
`# 1. Set `from_logits=False``
`tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')`
`# 2. Update `y_pred` to use probabilities instead of logits`
`y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]`
```

`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]).
`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.
`axis` The axis along which to compute crossentropy (the features axis). Defaults to -1.
`reduction` 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` Name for the op. Defaults to 'binary_crossentropy'.

## Methods

### `from_config`

View source

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