tf.keras.layers.GlobalMaxPool1D

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Global max pooling operation for 1D temporal data.

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Main aliases

tf.keras.layers.GlobalMaxPooling1D

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.GlobalMaxPool1D, tf.compat.v1.keras.layers.GlobalMaxPooling1D

tf.keras.layers.GlobalMaxPool1D(
    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])
x
<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()
max_pool_1d(x)
<tf.Tensor: shape=(3, 1), dtype=float32, numpy=
array([[3.],
       [6.],
       [9.], dtype=float32)>

Arguments
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).