► Keras 3 API documentation / Layers API / Pooling layers / GlobalMaxPooling3D layer

GlobalMaxPooling3D layer

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GlobalMaxPooling3D class

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

Global max pooling operation for 3D data.

Arguments

  • data_format: string, either "channels_last" 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 temporal dimension or not. If keepdims is False (default), the rank of the tensor is reduced for spatial dimensions. If keepdims is True, the spatial dimension are retained with length 1. The behavior is the same as for tf.reduce_mean or np.mean.

Input shape

  • 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)

Output shape

  • 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)

Example

>>> x = np.random.rand(2, 4, 5, 4, 3)
>>> y = keras.layers.GlobalMaxPooling3D()(x)
>>> y.shape
(2, 3)
GlobalMaxPooling3D layer
GlobalMaxPooling3D class