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TensorFlow 1 version
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View source on GitHub
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Computes the Poisson metric between y_true and y_pred.
Inherits From: Mean, Metric, Layer, Module
tf.keras.metrics.Poisson(
name='poisson', dtype=None
)
metric = y_pred - y_true * log(y_pred)
Args | |
|---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
Standalone usage:
m = tf.keras.metrics.Poisson()m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])m.result().numpy()0.49999997
m.reset_states()m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],sample_weight=[1, 0])m.result().numpy()0.99999994
Usage with compile() API:
model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.Poisson()])
Methods
reset_states
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
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
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates metric statistics.
y_true and y_pred should have the same shape.
| Args | |
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y_true
|
Ground truth values. shape = [batch_size, d0, .. dN].
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y_pred
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The predicted values. shape = [batch_size, d0, .. dN].
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sample_weight
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Optional sample_weight acts as a
coefficient for the metric. If a scalar is provided, then the metric is
simply scaled by the given value. If sample_weight is a tensor of size
[batch_size], then the metric for each sample of the batch is rescaled
by the corresponding element in the sample_weight vector. If the shape
of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted
to this shape), then each metric element of y_pred is scaled by the
corresponding value of sample_weight. (Note on dN-1: all metric
functions reduce by 1 dimension, usually the last axis (-1)).
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| Returns | |
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| Update op. |