# W3cubDocs

/TensorFlow Python

# tf.contrib.gan.eval.mean_only_frechet_classifier_distance_from_activations

### Aliases:

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

Classifier distance for evaluating a generative model from activations.

Given two Gaussian distribution with means m and m_w and covariance matrices C and C_w, this function calcuates

```|m - m_w|^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.

In this variant, we only compute the difference between the means of the fitted Gaussians. The computation leads to O(n) vs. O(n^2) memory usage, yet still retains much of the same information as FID.

#### Args:

• `real_activations`: 2D array of activations of real images of size [num_images, num_dims] to use to compute Frechet Inception distance.
• `generated_activations`: 2D array of activations of generated images of size [num_images, num_dims] to use to compute Frechet Inception distance.

#### Returns:

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