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Calculates how often predictions matches labels.

    name='binary_accuracy', dtype=None, threshold=0.5

Used in the notebooks

Used in the tutorials

For example, if y_true is [1, 1, 0, 0] and y_pred is [0.98, 1, 0, 0.6] then the binary accuracy is 3/4 or .75. If the weights were specified as [1, 0, 0, 1] then the binary 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.


m = tf.keras.metrics.BinaryAccuracy()
m.update_state([1, 1, 0, 0], [0.98, 1, 0, 0.6])
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.BinaryAccuracy()])


  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.
  • threshold: (Optional) Float representing the threshold for deciding whether prediction values are 1 or 0.



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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.


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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.


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    y_true, y_pred, sample_weight=None

Accumulates metric statistics.

y_true and y_pred should have the same shape.


  • 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.


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