질문이있다? TensorFlow 포럼 에서 커뮤니티와 연결

# tf.keras.metrics.Poisson

Computes the Poisson metric between y_true and y_pred.

metric = y_pred - y_true * log(y_pred)

#### Usage:

m = tf.keras.metrics.Poisson()
m.update_state([1, 9, 2], [4, 8, 12])
print('Final result: ', m.result().numpy())  # Final result: -4.63

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.Poisson()])

fn The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
**kwargs The keyword arguments that are passed on to fn.

## Methods

### reset_states

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Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

### result

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Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

### update_state

View source

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
y_true The ground truth values.
y_pred The predicted values.
sample_weight Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns
Update op.

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[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"이해하기 쉬움" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"문제가 해결됨" },{ "type": "thumb-up", "id": "otherUp", "label":"기타" }]