tf.keras.metrics.BinaryAccuracy

TensorFlow 1 version

View source on GitHub

Calculates how often predictions matches binary labels.

View aliases

Main aliases

tf.metrics.binary_accuracy

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.binary_accuracy

tf.keras.metrics.binary_accuracy(
    y_true, y_pred, threshold=0.5
)

Standalone usage:

y_true = [[1], [1], [0], [0]]
y_pred = [[1], [1], [0], [0]]
m = tf.keras.metrics.binary_accuracy(y_true, y_pred)
assert m.shape == (4,)
m.numpy()
array([1., 1., 1., 1.], dtype=float32)

Args
`y_true`	Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred`	The predicted values. shape = `[batch_size, d0, .. dN]`.
`threshold`	(Optional) Float representing the threshold for deciding whether prediction values are 1 or 0.

Returns
Binary accuracy values. shape = `[batch_size, d0, .. dN-1]`

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.keras / metrics / metrics.BinaryAccuracy

View aliases

Standalone usage:

Args

Returns