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TensorFlow 1 version
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View source on GitHub
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Calculates how often predictions matches integer labels.
Inherits From: Mean, Metric, Layer, Module
tf.keras.metrics.SparseCategoricalAccuracy(
name='sparse_categorical_accuracy', dtype=None
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
acc = np.dot(sample_weight, np.equal(y_true, np.argmax(y_pred, axis=1))
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 sparse categorical 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.
Args | |
|---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
Standalone usage:
m = tf.keras.metrics.SparseCategoricalAccuracy()m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])m.result().numpy()0.5
m.reset_states()m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [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.SparseCategoricalAccuracy()])
Methods
reset_states
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
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
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].
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y_pred
|
The predicted values. shape = [batch_size, d0, .. dN].
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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)).
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| Returns | |
|---|---|
| Update op. |