TensorFlow 1 version | View source on GitHub |
Computes the crossentropy metric between the labels and predictions.
tf.keras.metrics.SparseCategoricalCrossentropy(
name='sparse_categorical_crossentropy', dtype=None, from_logits=False, axis=-1
)
Use this crossentropy metric when there are two or more label classes.
We expect labels to be provided as integers. If you want to provide labels
using one-hot
representation, please use CategoricalCrossentropy
metric.
There should be # classes
floating point values per feature for y_pred
and a single floating point value per feature for y_true
.
In the snippet below, there is a single floating point value per example for
y_true
and # classes
floating pointing values per example for y_pred
.
The shape of y_true
is [batch_size]
and the shape of y_pred
is
[batch_size, num_classes]
.
Usage:
m = tf.keras.metrics.SparseCategoricalCrossentropy()
m.update_state(
[1, 2],
[[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
# y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
# logits = log(y_pred)
# softmax = exp(logits) / sum(exp(logits), axis=-1)
# softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
# xent = -sum(y * log(softmax), 1)
# log(softmax) = [[-2.9957, -0.0513, -16.1181], [-2.3026, -0.2231, -2.3026]]
# y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
# xent = [0.0513, 2.3026]
# Reduced xent = (0.0513 + 2.3026) / 2
print('Final result: ', m.result().numpy()) # Final result: 1.176
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.SparseCategoricalCrossentropy()])
Args | |
---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
from_logits
|
(Optional ) Whether y_pred is expected to be a logits tensor.
By default, we assume that y_pred encodes a probability distribution.
|
axis
|
(Optional) Defaults to -1. The dimension along which the metric is computed. |
Args | |
---|---|
fn
|
The metric function to wrap, with signature
fn(y_true, y_pred, **kwargs) .
|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
**kwargs
|
The keyword arguments that are passed on to fn .
|
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
|
The ground truth values. |
y_pred
|
The predicted values. |
sample_weight
|
Optional weighting of each example. Defaults to 1. Can be
a Tensor whose rank is either 0, or the same rank as y_true ,
and must be broadcastable to y_true .
|
Returns | |
---|---|
Update op. |