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TensorFlow 1 version View source on GitHub

Global max pooling operation for 1D temporal data.

    data_format='channels_last', **kwargs

Downsamples the input representation by taking the maximum value over the time dimension.

For example:

x = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]) 
x = tf.reshape(x, [3, 3, 1]) 
<tf.Tensor: shape=(3, 3, 1), dtype=float32, numpy= 
array([[[1.], [2.], [3.]], 
       [[4.], [5.], [6.]], 
       [[7.], [8.], [9.]]], dtype=float32)> 
max_pool_1d = tf.keras.layers.GlobalMaxPooling1D() 
<tf.Tensor: shape=(3, 1), dtype=float32, numpy= 
       [9.], dtype=float32)> 


  • 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, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps).

Input shape:

  • If data_format='channels_last': 3D tensor with shape: (batch_size, steps, features)
  • If data_format='channels_first': 3D tensor with shape: (batch_size, features, steps)

Output shape:

2D tensor with shape (batch_size, features).