add_metric
- FullyConnected.add_metric(value, name=None, **kwargs)
Adds metric tensor to the layer.
This method can be used inside the call() method of a subclassed layer or model.
```python class MyMetricLayer(tf.keras.layers.Layer):
- def __init__(self):
super(MyMetricLayer, self).__init__(name=’my_metric_layer’) self.mean = tf.keras.metrics.Mean(name=’metric_1’)
- def call(self, inputs):
self.add_metric(self.mean(inputs)) self.add_metric(tf.reduce_sum(inputs), name=’metric_2’) return inputs
This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These metrics become part of the model’s topology and are tracked when you save the model via `save().
`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(math_ops.reduce_sum(x), name='metric_1') `Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.
`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1') `- Parameters:
value – Metric tensor.
name – String metric name.
**kwargs – Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.