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tf.keras.metrics.Accuracy

TensorFlow 1 version View source on GitHub

Class Accuracy

Calculates how often predictions matches labels.

Aliases:

Used in the guide:

Used in the tutorials:

For example, if y_true is [1, 2, 3, 4] and y_pred is [0, 2, 3, 4] then the accuracy is 3/4 or .75. If the weights were specified as [1, 1, 0, 0] then the accuracy would be 1/2 or .5.

This metric creates two local variables, total and count that are used to compute the frequency with which y_pred matches y_true. This frequency is ultimately returned as binary accuracy: an idempotent operation that simply divides total by count.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Usage:

m = tf.keras.metrics.Accuracy()
m.update_state([1, 2, 3, 4], [0, 2, 3, 4])
print('Final result: ', m.result().numpy())  # Final result: 0.75

Usage with tf.keras API:

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

__init__

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__init__(
    name='accuracy',
    dtype=None
)

Creates a MeanMetricWrapper instance.

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.

__new__

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__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

Methods

reset_states

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reset_states()

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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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

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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: 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.