TensorFlow 1 version | View source on GitHub |
Calculates how often predictions matches one-hot labels.
tf.keras.metrics.CategoricalAccuracy(
name='categorical_accuracy', dtype=None
)
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.
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 tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.CategoricalAccuracy()])
Args | |
---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
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] .
|
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. |