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## Usage of loss functions
A loss function (or objective function, or optimization score function) is one of the two parameters required to compile a model:
```python
model.compile(loss='mean_squared_error', optimizer='sgd')
```
```python
from keras import losses
model.compile(loss=losses.mean_squared_error, optimizer='sgd')
```
You can either pass the name of an existing loss function, or pass a TensorFlow/Theano symbolic function that returns a scalar for each data-point and takes the following two arguments:
- __y_true__: True labels. TensorFlow/Theano tensor.
- __y_pred__: Predictions. TensorFlow/Theano tensor of the same shape as y_true.
The actual optimized objective is the mean of the output array across all datapoints.
For a few examples of such functions, check out the [losses source](https://github.com/keras-team/keras/blob/master/keras/losses.py).
## Available loss functions
### mean_squared_error
```python
keras.losses.mean_squared_error(y_true, y_pred)
```
----
### mean_absolute_error
```python
keras.losses.mean_absolute_error(y_true, y_pred)
```
----
### mean_absolute_percentage_error
```python
keras.losses.mean_absolute_percentage_error(y_true, y_pred)
```
----
### mean_squared_logarithmic_error
```python
keras.losses.mean_squared_logarithmic_error(y_true, y_pred)
```
----
### squared_hinge
```python
keras.losses.squared_hinge(y_true, y_pred)
```
----
### hinge
```python
keras.losses.hinge(y_true, y_pred)
```
----
### categorical_hinge
```python
keras.losses.categorical_hinge(y_true, y_pred)
```
----
### logcosh
```python
keras.losses.logcosh(y_true, y_pred)
```
Logarithm of the hyperbolic cosine of the prediction error.
`log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
like the mean squared error, but will not be so strongly affected by the
occasional wildly incorrect prediction.
__Arguments__
- __y_true__: tensor of true targets.
- __y_pred__: tensor of predicted targets.
__Returns__
Tensor with one scalar loss entry per sample.
----
### huber_loss
```python
keras.losses.huber_loss(y_true, y_pred, delta=1.0)
```
----
### categorical_crossentropy
```python
keras.losses.categorical_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0)
```
----
### sparse_categorical_crossentropy
```python
keras.losses.sparse_categorical_crossentropy(y_true, y_pred, from_logits=False, axis=-1)
```
----
### binary_crossentropy
```python
keras.losses.binary_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0)
```
----
### kullback_leibler_divergence
```python
keras.losses.kullback_leibler_divergence(y_true, y_pred)
```
----
### poisson
```python
keras.losses.poisson(y_true, y_pred)
```
----
### cosine_proximity
```python
keras.losses.cosine_proximity(y_true, y_pred, axis=-1)
```
----
### is_categorical_crossentropy
```python
keras.losses.is_categorical_crossentropy(loss)
```
----
**Note**: when using the `categorical_crossentropy` loss, your targets should be in categorical format (e.g. if you have 10 classes, the target for each sample should be a 10-dimensional vector that is all-zeros except for a 1 at the index corresponding to the class of the sample). In order to convert *integer targets* into *categorical targets*, you can use the Keras utility `to_categorical`:
```python
from keras.utils import to_categorical
categorical_labels = to_categorical(int_labels, num_classes=None)
```
When using the `sparse_categorical_crossentropy` loss, your targets should be *integer targets*.
If you have categorical targets, you should use `categorical_crossentropy`.
`categorical_crossentropy` is another term for [multi-class log loss](http://wiki.fast.ai/index.php/Log_Loss).
|
