Accuracy metrics

Accuracy class

tf.keras.metrics.Accuracy(name="accuracy", dtype=None)

Calculates how often predictions equals labels.

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.

Arguments

• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Standalone usage:

>>> m = tf.keras.metrics.Accuracy()
>>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
>>> m.result().numpy()
0.75

>>> m.reset_states()
>>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
...                sample_weight=[1, 1, 0, 0])
>>> m.result().numpy()
0.5

Usage with compile() API:

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

BinaryAccuracy class

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

Calculates how often predictions matches binary labels.

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.

Arguments

• 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.

Standalone 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 compile() API:

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

CategoricalAccuracy class

tf.keras.metrics.CategoricalAccuracy(name="categorical_accuracy", dtype=None)

Calculates how often predictions matches one-hot labels.

You can provide logits of classes as y_pred, since argmax of logits and probabilities are same.

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 categorical accuracy: an idempotent operation that simply divides total by count.

y_pred and y_true should be passed in as vectors of probabilities, rather than as labels. If necessary, use tf.one_hot to expand y_true as a vector.

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

Arguments

• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Standalone usage:

>>> m = tf.keras.metrics.CategoricalAccuracy()
>>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
...                 [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5

>>> m.reset_states()
>>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
...                 [0.05, 0.95, 0]],
...                sample_weight=[0.7, 0.3])
>>> m.result().numpy()
0.3

Usage with compile() API:

model.compile(
  optimizer='sgd',
  loss='mse',
  metrics=[tf.keras.metrics.CategoricalAccuracy()])

TopKCategoricalAccuracy class

tf.keras.metrics.TopKCategoricalAccuracy(
    k=5, name="top_k_categorical_accuracy", dtype=None
)

Computes how often targets are in the top K predictions.

Arguments

• k: (Optional) Number of top elements to look at for computing accuracy. Defaults to 5.
• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Standalone usage:

>>> m = tf.keras.metrics.TopKCategoricalAccuracy(k=1)
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
...                [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5

>>> m.reset_states()
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
...                [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
...                sample_weight=[0.7, 0.3])
>>> m.result().numpy()
0.3

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='mse',
              metrics=[tf.keras.metrics.TopKCategoricalAccuracy()])

SparseTopKCategoricalAccuracy class

tf.keras.metrics.SparseTopKCategoricalAccuracy(
    k=5, name="sparse_top_k_categorical_accuracy", dtype=None
)

Computes how often integer targets are in the top K predictions.

Arguments

• k: (Optional) Number of top elements to look at for computing accuracy. Defaults to 5.
• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Standalone usage:

>>> m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1)
>>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5

>>> m.reset_states()
>>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
...                sample_weight=[0.7, 0.3])
>>> m.result().numpy()
0.3

Usage with compile() API:

model.compile(
  optimizer='sgd',
  loss='mse',
  metrics=[tf.keras.metrics.SparseTopKCategoricalAccuracy()])