# tf.keras.layers.GlobalMaxPooling3D

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/layers/pooling/global_max_pooling3d.py#L25-L69) |

Global Max pooling operation for 3D data.

Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)

#### View aliases


**Main aliases**

[`tf.keras.layers.GlobalMaxPool3D`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPooling3D)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.layers.GlobalMaxPool3D\`, \`tf.compat.v1.keras.layers.GlobalMaxPooling3D\`

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    tf.keras.layers.GlobalMaxPooling3D(
        data_format=None, keepdims=False, **kwargs
    )

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| ## Args ||
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| `data_format` | A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". |
| `keepdims` | A boolean, whether to keep the spatial dimensions or not. If `keepdims` is `False` (default), the rank of the tensor is reduced for spatial dimensions. If `keepdims` is `True`, the spatial dimensions are retained with length 1. The behavior is the same as for [`tf.reduce_max`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/reduce_max) or `np.max`. |

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| ## Input shape ||
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| <br /> - If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` - If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` ||

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| ## Output shape ||
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| <br /> - If `keepdims`=False: 2D tensor with shape `(batch_size, channels)`. - If `keepdims`=True: - If `data_format='channels_last'`: 5D tensor with shape `(batch_size, 1, 1, 1, channels)` - If `data_format='channels_first'`: 5D tensor with shape `(batch_size, channels, 1, 1, 1)` ||

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