tf.keras.metrics.BinaryAccuracy

TensorFlow 1 version View source on GitHub

Calculates how often predictions matches binary labels.

tf.keras.metrics.BinaryAccuracy(
    name='binary_accuracy', dtype=None, threshold=0.5
)

Used in the notebooks

Used in the tutorials

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]]) 
m.result().numpy() 
0.75 
m.reset_states() 
_ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]], 
                   sample_weight=[1, 0, 0, 1]) 
m.result().numpy() 
0.5 

Usage with tf.keras API:

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

Args:

  • 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

View source

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

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

View source

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: 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.