---
title: Exact Match
description: Compute Exact match (also known as subset accuracy).
canonical: 'https://lightning.ai/docs/torchmetrics/stable/classification/exact_match'
---
# Exact Match {#exact-match}

## Module Interface {#module-interface}

### `class torchmetrics.ExactMatch(**kwargs)` {#torchmetrics.ExactMatch}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/classification/exact_match.py#L408-L462)

Compute Exact match (also known as subset accuracy).

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the `task` argument to either `'multiclass'` or `'multilabel'`. See the documentation of [`MulticlassExactMatch`](#torchmetrics.classification.MulticlassExactMatch "torchmetrics.classification.MulticlassExactMatch") and [`MultilabelExactMatch`](#torchmetrics.classification.MultilabelExactMatch "torchmetrics.classification.MultilabelExactMatch") for the specific details of each argument influence and examples.

Legacy Example:

```python
>>> from torch import tensor
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='global')
>>> metric(preds, target)
tensor(0.5000)
```

```python
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([1., 0.])
```

#### `static __new__(cls, task, threshold=0.5, num_classes=None, num_labels=None, multidim_average='global', ignore_index=None, validate_args=True, **kwargs)` {#torchmetrics.ExactMatch.__new__}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/classification/exact_match.py#L436-L462)

Initialize task metric.

Return type:

[`Metric`](/docs/torchmetrics/1.9.0/api-reference/metric.md#torchmetrics.Metric "torchmetrics.metric.Metric")

### MulticlassExactMatch {#multiclassexactmatch}

### `class torchmetrics.classification.MulticlassExactMatch(num_classes, multidim_average='global', ignore_index=None, validate_args=True, **kwargs)` {#torchmetrics.classification.MulticlassExactMatch}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/classification/exact_match.py#L45-L213)

Compute Exact match (also known as subset accuracy) for multiclass tasks.

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

As input to `forward` and `update` the metric accepts the following input:

*   `preds` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): An int tensor of shape `(N, ...)` or float tensor of shape `(N, C, ..)`. If preds is a floating point we apply `torch.argmax` along the `C` dimension to automatically convert probabilities/logits into an int tensor.
    
*   `target` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): An int tensor of shape `(N, ...)`.
    

As output to `forward` and `compute` the metric returns the following output:

*   `mcem` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): A tensor whose returned shape depends on the `multidim_average` argument:
    
    *   If `multidim_average` is set to `global` the output will be a scalar tensor
        
    *   If `multidim_average` is set to `samplewise` the output will be a tensor of shape `(N,)`
        

If `multidim_average` is set to `samplewise` we expect at least one additional dimension `...` to be present, which the reduction will then be applied over instead of the sample dimension `N`.

Parameters:

*   **num\_classes** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – Integer specifying the number of labels
    
*   **multidim\_average** ([`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")\[`'global'`, `'samplewise'`\]) –
    
    Defines how additionally dimensions `...` should be handled. Should be one of the following:
    
    *   `global`: Additional dimensions are flatted along the batch dimension
        
    *   `samplewise`: Statistic will be calculated independently for each sample on the `N` axis. The statistics in this case are calculated over the additional dimensions.
        
    
*   **ignore\_index** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")\]) – Specifies a target value that is ignored and does not contribute to the metric calculation
    
*   **validate\_args** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – bool indicating if input arguments and tensors should be validated for correctness. Set to `False` for faster computations.
    

Example (multidim tensors):

```python
>>> from torch import tensor
>>> from torchmetrics.classification import MulticlassExactMatch
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassExactMatch(num_classes=3, multidim_average='global')
>>> metric(preds, target)
tensor(0.5000)
```

Example (multidim tensors):

```python
>>> from torchmetrics.classification import MulticlassExactMatch
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassExactMatch(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([1., 0.])
```

#### `plot(val=None, ax=None)` {#torchmetrics.classification.MulticlassExactMatch.plot}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/classification/exact_match.py#L173-L213)

Plot a single or multiple values from the metric.

Parameters:

*   **val** (`Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
    
*   **ax** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)")\]) – An matplotlib axis object. If provided will add plot to that axis
    

Return type:

[`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Figure`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure "(in Matplotlib v3.11.2)"), `Union`\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)"), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.5)")\]\]

Returns:

Figure object and Axes object

Raises:

[**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If matplotlib is not installed

```python
>>> # Example plotting a single value per class
>>> from torch import randint
>>> from torchmetrics.classification import MulticlassExactMatch
>>> metric = MulticlassExactMatch(num_classes=3)
>>> metric.update(randint(3, (20,5)), randint(3, (20,5)))
>>> fig_, ax_ = metric.plot()
```

[![../../\_images/exact\_match-1.png](/docs/torchmetrics/1.9.0/static/_images/exact_match-1.png)](/docs/torchmetrics/1.9.0/static/_images/exact_match-1.png)

```python
>>> from torch import randint
>>> # Example plotting a multiple values per class
>>> from torchmetrics.classification import MulticlassExactMatch
>>> metric = MulticlassExactMatch(num_classes=3)
>>> values = []
>>> for _ in range(20):
...     values.append(metric(randint(3, (20,5)), randint(3, (20,5))))
>>> fig_, ax_ = metric.plot(values)
```

[![../../\_images/exact\_match-2.png](/docs/torchmetrics/1.9.0/static/_images/exact_match-2.png)](/docs/torchmetrics/1.9.0/static/_images/exact_match-2.png)

### MultilabelExactMatch {#multilabelexactmatch}

### `class torchmetrics.classification.MultilabelExactMatch(num_labels, threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True, **kwargs)` {#torchmetrics.classification.MultilabelExactMatch}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/classification/exact_match.py#L216-L405)

Compute Exact match (also known as subset accuracy) for multilabel tasks.

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

As input to `forward` and `update` the metric accepts the following input:

*   `preds` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): An int tensor or float tensor of shape `(N, C, ..)`. If preds is a floating point tensor with values outside \[0,1\] range we consider the input to be logits and will auto apply sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in `threshold`.
    
*   `target` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): An int tensor of shape `(N, C, ...)`.
    

As output to `forward` and `compute` the metric returns the following output:

*   `mlem` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): A tensor whose returned shape depends on the `multidim_average` argument:
    
    *   If `multidim_average` is set to `global` the output will be a scalar tensor
        
    *   If `multidim_average` is set to `samplewise` the output will be a tensor of shape `(N,)`
        

If `multidim_average` is set to `samplewise` we expect at least one additional dimension `...` to be present, which the reduction will then be applied over instead of the sample dimension `N`.

Parameters:

*   **num\_labels** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – Integer specifying the number of labels
    
*   **threshold** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Threshold for transforming probability to binary (0,1) predictions
    
*   **multidim\_average** ([`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")\[`'global'`, `'samplewise'`\]) –
    
    Defines how additionally dimensions `...` should be handled. Should be one of the following:
    
    *   `global`: Additional dimensions are flatted along the batch dimension
        
    *   `samplewise`: Statistic will be calculated independently for each sample on the `N` axis. The statistics in this case are calculated over the additional dimensions.
        
    
*   **ignore\_index** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")\]) – Specifies a target value that is ignored and does not contribute to the metric calculation
    
*   **validate\_args** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – bool indicating if input arguments and tensors should be validated for correctness. Set to `False` for faster computations.
    

Example (preds is int tensor):

```python
>>> from torch import tensor
>>> from torchmetrics.classification import MultilabelExactMatch
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelExactMatch(num_labels=3)
>>> metric(preds, target)
tensor(0.5000)
```

Example (preds is float tensor):

```python
>>> from torchmetrics.classification import MultilabelExactMatch
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> metric = MultilabelExactMatch(num_labels=3)
>>> metric(preds, target)
tensor(0.5000)
```

Example (multidim tensors):

```python
>>> from torchmetrics.classification import MultilabelExactMatch
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = MultilabelExactMatch(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0., 0.])
```

#### `plot(val=None, ax=None)` {#torchmetrics.classification.MultilabelExactMatch.plot}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/classification/exact_match.py#L365-L405)

Plot a single or multiple values from the metric.

Parameters:

*   **val** (`Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
    
*   **ax** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)")\]) – An matplotlib axis object. If provided will add plot to that axis
    

Return type:

[`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Figure`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure "(in Matplotlib v3.11.2)"), `Union`\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)"), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.5)")\]\]

Returns:

Figure and Axes object

Raises:

[**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If matplotlib is not installed

```python
>>> # Example plotting a single value
>>> from torch import rand, randint
>>> from torchmetrics.classification import MultilabelExactMatch
>>> metric = MultilabelExactMatch(num_labels=3)
>>> metric.update(randint(2, (20, 3, 5)), randint(2, (20, 3, 5)))
>>> fig_, ax_ = metric.plot()
```

[![../../\_images/exact\_match-3.png](/docs/torchmetrics/1.9.0/static/_images/exact_match-3.png)](/docs/torchmetrics/1.9.0/static/_images/exact_match-3.png)

```python
>>> # Example plotting multiple values
>>> from torch import rand, randint
>>> from torchmetrics.classification import MultilabelExactMatch
>>> metric = MultilabelExactMatch(num_labels=3)
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(randint(2, (20, 3, 5)), randint(2, (20, 3, 5))))
>>> fig_, ax_ = metric.plot(values)
```

[![../../\_images/exact\_match-4.png](/docs/torchmetrics/1.9.0/static/_images/exact_match-4.png)](/docs/torchmetrics/1.9.0/static/_images/exact_match-4.png)

## Functional Interface {#functional-interface}

### exact_match {#exact-match}

### `torchmetrics.functional.classification.exact_match(preds, target, task, num_classes=None, num_labels=None, threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True)` {#torchmetrics.functional.classification.exact_match}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/functional/classification/exact_match.py#L222-L266)

Compute Exact match (also known as subset accuracy).

Exact Match is a stricter version of accuracy where all classes/labels have to match exactly for the sample to be correctly classified.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the `task` argument to either `'multiclass'` or `'multilabel'`. See the documentation of [`multiclass_exact_match()`](#torchmetrics.functional.classification.multiclass_exact_match "torchmetrics.functional.classification.multiclass_exact_match") and [`multilabel_exact_match()`](#torchmetrics.functional.classification.multilabel_exact_match "torchmetrics.functional.classification.multilabel_exact_match") for the specific details of each argument influence and examples.

Return type:

[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")

Legacy Example:

```python
>>> from torch import tensor
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='global')
tensor(0.5000)
```

```python
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise')
tensor([1., 0.])
```

### multiclass_exact_match {#multiclass-exact-match}

### `torchmetrics.functional.classification.multiclass_exact_match(preds, target, num_classes, multidim_average='global', ignore_index=None, validate_args=True)` {#torchmetrics.functional.classification.multiclass_exact_match}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/functional/classification/exact_match.py#L57-L121)

Compute Exact match (also known as subset accuracy) for multiclass tasks.

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

Accepts the following input tensors:

*   `preds`: `(N, ...)` (int tensor) or `(N, C, ..)` (float tensor). If preds is a floating point we apply `torch.argmax` along the `C` dimension to automatically convert probabilities/logits into an int tensor.
    
*   `target` (int tensor): `(N, ...)`
    

Parameters:

*   **preds** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – Tensor with predictions
    
*   **target** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – Tensor with true labels
    
*   **num\_classes** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – Integer specifying the number of labels
    
*   **multidim\_average** ([`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")\[`'global'`, `'samplewise'`\]) –
    
    Defines how additionally dimensions `...` should be handled. Should be one of the following:
    
    *   `global`: Additional dimensions are flatted along the batch dimension
        
    *   `samplewise`: Statistic will be calculated independently for each sample on the `N` axis. The statistics in this case are calculated over the additional dimensions.
        
    
*   **ignore\_index** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")\]) – Specifies a target value that is ignored and does not contribute to the metric calculation
    
*   **validate\_args** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – bool indicating if input arguments and tensors should be validated for correctness. Set to `False` for faster computations.
    

Returns:

*   If `multidim_average` is set to `global` the output will be a scalar tensor
    
*   If `multidim_average` is set to `samplewise` the output will be a tensor of shape `(N,)`
    

Return type:

The returned shape depends on the `multidim_average` argument

Example (multidim tensors):

```python
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multiclass_exact_match
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='global')
tensor(0.5000)
```

Example (multidim tensors):

```python
>>> from torchmetrics.functional.classification import multiclass_exact_match
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
>>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='samplewise')
tensor([1., 0.])
```

### multilabel_exact_match {#multilabel-exact-match}

### `torchmetrics.functional.classification.multilabel_exact_match(preds, target, num_labels, threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True)` {#torchmetrics.functional.classification.multilabel_exact_match}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/functional/classification/exact_match.py#L145-L219)

Compute Exact match (also known as subset accuracy) for multilabel tasks.

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified.

Accepts the following input tensors:

*   `preds` (int or float tensor): `(N, C, ...)`. If preds is a floating point tensor with values outside \[0,1\] range we consider the input to be logits and will auto apply sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in `threshold`.
    
*   `target` (int tensor): `(N, C, ...)`
    

Parameters:

*   **preds** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – Tensor with predictions
    
*   **target** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – Tensor with true labels
    
*   **num\_labels** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – Integer specifying the number of labels
    
*   **threshold** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Threshold for transforming probability to binary (0,1) predictions
    
*   **multidim\_average** ([`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")\[`'global'`, `'samplewise'`\]) –
    
    Defines how additionally dimensions `...` should be handled. Should be one of the following:
    
    *   `global`: Additional dimensions are flatted along the batch dimension
        
    *   `samplewise`: Statistic will be calculated independently for each sample on the `N` axis. The statistics in this case are calculated over the additional dimensions.
        
    
*   **ignore\_index** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")\]) – Specifies a target value that is ignored and does not contribute to the metric calculation
    
*   **validate\_args** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – bool indicating if input arguments and tensors should be validated for correctness. Set to `False` for faster computations.
    

Returns:

*   If `multidim_average` is set to `global` the output will be a scalar tensor
    
*   If `multidim_average` is set to `samplewise` the output will be a tensor of shape `(N,)`
    

Return type:

The returned shape depends on the `multidim_average` argument

Example (preds is int tensor):

```python
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_exact_match(preds, target, num_labels=3)
tensor(0.5000)
```

Example (preds is float tensor):

```python
>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> multilabel_exact_match(preds, target, num_labels=3)
tensor(0.5000)
```

Example (multidim tensors):

```python
>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> multilabel_exact_match(preds, target, num_labels=3, multidim_average='samplewise')
tensor([0., 0.])
```

---

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- Parent: [Classification](/docs/torchmetrics/1.9.0/classification.md)