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# tf.contrib.gan.eval.frechet_classifier_distance_from_activations

### Aliases:

• `tf.contrib.gan.eval.classifier_metrics.frechet_classifier_distance_from_activations`
• `tf.contrib.gan.eval.frechet_classifier_distance_from_activations`
``````tf.contrib.gan.eval.frechet_classifier_distance_from_activations(
real_activations,
generated_activations
)
``````

Classifier distance for evaluating a generative model.

This methods computes the Frechet classifier distance from activations of real images and generated images. This can be used independently of the frechet_classifier_distance() method, especially in the case of using large batches during evaluation where we would like precompute all of the activations before computing the classifier distance.

This technique is described in detail in https://arxiv.org/abs/1706.08500. Given two Gaussian distribution with means m and m_w and covariance matrices C and C_w, this function calculates

``````          |m - m_w|^2 + Tr(C + C_w - 2(C * C_w)^(1/2))
``````

which captures how different the distributions of real images and generated images (or more accurately, their visual features) are. Note that unlike the Inception score, this is a true distance and utilizes information about real world images.

Note that when computed using sample means and sample covariance matrices, Frechet distance is biased. It is more biased for small sample sizes. (e.g. even if the two distributions are the same, for a small sample size, the expected Frechet distance is large). It is important to use the same sample size to compute frechet classifier distance when comparing two generative models.

#### Args:

• `real_activations`: 2D Tensor containing activations of real data. Shape is [batch_size, activation_size].
• `generated_activations`: 2D Tensor containing activations of generated data. Shape is [batch_size, activation_size].

#### Returns:

The Frechet Inception distance. A floating-point scalar of the same type as the output of the activations.