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## Usage of metrics
A metric is a function that is used to judge the performance of your model. Metric functions are to be supplied in the `metrics` parameter when a model is compiled.
```python
model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=['mae', 'acc'])
```
```python
from keras import metrics
model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=[metrics.mae, metrics.categorical_accuracy])
```
A metric function is similar to a [loss function](/losses), except that the results from evaluating a metric are not used when training the model. You may use any of the loss functions as a metric function.
You can either pass the name of an existing metric, or pass a Theano/TensorFlow symbolic function (see [Custom metrics](#custom-metrics)).
#### Arguments
- __y_true__: True labels. Theano/TensorFlow tensor.
- __y_pred__: Predictions. Theano/TensorFlow tensor of the same shape as y_true.
#### Returns
Single tensor value representing the mean of the output array across all
datapoints.
----
## Available metrics
{{autogenerated}}
In addition to the metrics above, you may use any of the loss functions described in the [loss function](/losses) page as metrics.
----
## Custom metrics
Custom metrics can be passed at the compilation step. The
function would need to take `(y_true, y_pred)` as arguments and return
a single tensor value.
```python
import keras.backend as K
def mean_pred(y_true, y_pred):
return K.mean(y_pred)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy', mean_pred])
```
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