ignite.metrics

class ignite.metrics.BinaryAccuracy(output_transform=<function Metric.<lambda>>)

Calculates the binary accuracy.

• update must receive output of the form (y_pred, y).

• y_pred must be in the following shape (batch_size, …) and it’s elements must be between 0 and 1.

• y must be in the following shape (batch_size, …)

class ignite.metrics.CategoricalAccuracy(output_transform=<function Metric.<lambda>>)

Calculates the categorical accuracy.

• update must receive output of the form (y_pred, y).

• y_pred must be in the following shape (batch_size, num_categories, …)

• y must be in the following shape (batch_size, …)

class ignite.metrics.Loss(loss_fn, output_transform=<function Loss.<lambda>>)

Calculates the average loss according to the passed loss_fn.

• loss_fn must return the average loss over all observations in the batch.

• update must receive output of the form (y_pred, y).

class ignite.metrics.MeanAbsoluteError(output_transform=<function Metric.<lambda>>)

Calculates the mean absolute error.

• update must receive output of the form (y_pred, y).

class ignite.metrics.MeanPairwiseDistance(p=2, eps=1e-06, output_transform=<function MeanPairwiseDistance.<lambda>>)

Calculates the mean pairwise distance.

• update must receive output of the form (y_pred, y).

class ignite.metrics.MeanSquaredError(output_transform=<function Metric.<lambda>>)

Calculates the mean squared error.

• update must receive output of the form (y_pred, y).

class ignite.metrics.Metric(output_transform=<function Metric.<lambda>>)

abstract compute()

Computes the metric based on it’s accumulated state.

This is called at the end of each epoch.

Returns

the actual quantity of interest

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed

abstract reset()

Resets the metric to to it’s initial state.

This is called at the start of each epoch.

abstract update(output)

Updates the metric’s state using the passed batch output.

This is called once for each batch.

Parameters

output – the is the output from the engine’s process function

class ignite.metrics.Precision(average=False, output_transform=<function Precision.<lambda>>)

Calculates precision.

• update must receive output of the form (y_pred, y).

If average is True, returns the unweighted average across all classes. Otherwise, returns a tensor with the precision for each class.

class ignite.metrics.Recall(average=False, output_transform=<function Recall.<lambda>>)

Calculates recall.

• update must receive output of the form (y_pred, y).

If average is True, returns the unweighted average across all classes. Otherwise, returns a tensor with the recall for each class.

class ignite.metrics.RootMeanSquaredError(output_transform=<function Metric.<lambda>>)

Calculates the root mean squared error.

• update must receive output of the form (y_pred, y).

class ignite.metrics.TopKCategoricalAccuracy(k=5, output_transform=<function TopKCategoricalAccuracy.<lambda>>)

Calculates the top-k categorical accuracy.

• update must receive output of the form (y_pred, y).