tf.keras.metrics.CategoricalAccuracy

TensorFlow 1 version View source on GitHub

Calculates how often predictions matches one-hot labels.

Used in the notebooks

Used in the guide

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()])

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

Methods

reset_states

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Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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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

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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.