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# tf.keras.metrics.Hinge

Computes the hinge metric between y_true and y_pred.

Inherits From: Mean, Metric, Layer, Module

y_true values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1.

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

#### Standalone usage:

m = tf.keras.metrics.Hinge()
m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
m.result().numpy()
1.3
m.reset_states()
m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
sample_weight=[1, 0])
m.result().numpy()
1.1

Usage with compile() API:

model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Hinge()])

## 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

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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.