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tf.keras.metrics.poisson

Computes the Poisson loss between y_true and y_pred.

The Poisson loss is the mean of the elements of the Tensor y_pred - y_true * log(y_pred).

Standalone usage:

y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.poisson(y_true, y_pred)
assert loss.shape == (2,)
y_pred = y_pred + 1e-7
assert np.allclose(
    loss.numpy(), np.mean(y_pred - y_true * np.log(y_pred), axis=-1),
    atol=1e-5)

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

Poisson loss value. shape = [batch_size, d0, .. dN-1].

InvalidArgumentError If y_true and y_pred have incompatible shapes.