tf.keras.metrics.Poisson

Computes the Poisson metric between y_true and y_pred.

Main aliases

tf.metrics.Poisson

Compat aliases for migration

See Migration guide for more details.

tf.keras.metrics.Poisson(
    name='poisson', dtype=None
)

metric = y_pred - y_true * log(y_pred)

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 tf.keras API:

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

Args
`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()

Resets all of the metric state variables.

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

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(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
`y_true`	Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred`	The predicted values. shape = `[batch_size, d0, .. dN]`.
`sample_weight`	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)).

Returns
Update op.