# tfr.keras.losses.SoftmaxLoss

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/ranking/blob/v0.5.3/tensorflow_ranking/python/keras/losses.py#L755-L829) |

Computes Softmax cross-entropy loss between `y_true` and `y_pred`.

    tfr.keras.losses.SoftmaxLoss(
        reduction: tf.losses.Reduction = tf.losses.Reduction.AUTO,
        name: Optional[str] = None,
        lambda_weight: Optional[losses_impl._LambdaWeight] = None,
        temperature: float = 1.0,
        ragged: bool = False
    )

For each list of scores `s` in `y_pred` and list of labels `y` in `y_true`:

    loss = - sum_i y_i * log(softmax(s_i))

#### Standalone usage:

    y_true = [[1., 0.]]
    y_pred = [[0.6, 0.8]]
    loss = tfr.keras.losses.SoftmaxLoss()
    loss(y_true, y_pred).numpy()
    0.7981389

    # Using ragged tensors
    y_true = tf.ragged.constant([[1., 0.], [0., 1., 0.]])
    y_pred = tf.ragged.constant([[0.6, 0.8], [0.5, 0.8, 0.4]])
    loss = tfr.keras.losses.SoftmaxLoss(ragged=True)
    loss(y_true, y_pred).numpy()
    0.83911896

Usage with the `compile()` API:

    model.compile(optimizer='sgd', loss=tfr.keras.losses.SoftmaxLoss())

#### Definition:

\\\[
\\mathcal{L}(\\{y\\}, \\{s\\}) = - \\sum_i y_i
\\log\\left(\\frac{\\exp(s_i)}{\\sum_j \\exp(s_j)}\\right)
\\\]

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| ## Args ||
|---|---|
| `reduction` | (Optional) The [`tf.keras.losses.Reduction`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Reduction) to use (see [`tf.keras.losses.Loss`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Loss)). |
| `name` | (Optional) The name for the op. |
| `lambda_weight` | (Optional) A lambdaweight to apply to the loss. Can be one of [`tfr.keras.losses.DCGLambdaWeight`](https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/losses/DCGLambdaWeight), [`tfr.keras.losses.NDCGLambdaWeight`](https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/losses/NDCGLambdaWeight), or, [`tfr.keras.losses.PrecisionLambdaWeight`](https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/losses/PrecisionLambdaWeight). |
| `temperature` | (Optional) The temperature to use for scaling the logits. |
| `ragged` | (Optional) If True, this loss will accept ragged tensors. If False, this loss will accept dense tensors. |

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## Methods

### `from_config`

[View source](https://github.com/tensorflow/ranking/blob/v0.5.3/tensorflow_ranking/python/keras/losses.py#L742-L752)

    @classmethod
    from_config(
        config, custom_objects=None
    )

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

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

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| Returns ||
|---|---|
| A `Loss` instance. ||

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### `get_config`

[View source](https://github.com/tensorflow/ranking/blob/v0.5.3/tensorflow_ranking/python/keras/losses.py#L732-L740)

    get_config() -> Dict[str, Any]

Returns the config dictionary for a `Loss` instance.

### `__call__`

[View source](https://github.com/tensorflow/ranking/blob/v0.5.3/tensorflow_ranking/python/keras/losses.py#L821-L829)

    __call__(
        y_true: https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/model/TensorLike,
        y_pred: https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/model/TensorLike,
        sample_weight: Optional[utils.TensorLike] = None
    ) -> tf.Tensor

See _RankingLoss.