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tf.keras.layers.GlobalMaxPool2D

Global max pooling operation for spatial data.

Inherits From: Layer, Module

Used in the notebooks

Used in the guide

Examples:

input_shape = (2, 4, 5, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.GlobalMaxPool2D()(x)
print(y.shape)
(2, 3)

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, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). 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 or np.max.

Input shape:

  • If data_format='channels_last': 4D tensor with shape (batch_size, rows, cols, channels).
  • If data_format='channels_first': 4D tensor with shape (batch_size, channels, rows, cols).

Output shape:

  • If keepdims=False: 2D tensor with shape (batch_size, channels).
  • If keepdims=True:
    • If data_format='channels_last': 4D tensor with shape (batch_size, 1, 1, channels)
    • If data_format='channels_first': 4D tensor with shape (batch_size, channels, 1, 1)