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tf.keras.metrics.BinaryAccuracy

TensorFlow 2 version View source on GitHub

Calculates how often predictions matches labels.

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.

Usage:

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.

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 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.