ยป Keras API reference / Layers API / Pooling layers / GlobalMaxPooling1D layer

GlobalMaxPooling1D layer

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GlobalMaxPooling1D class

tf.keras.layers.GlobalMaxPooling1D(
    data_format="channels_last", keepdims=False, **kwargs
)

Global max pooling operation for 1D temporal data.

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

Output shape

  • If keepdims=False: 2D tensor with shape (batch_size, features).
  • If keepdims=True:
    • If data_format='channels_last': 3D tensor with shape (batch_size, 1, features)
    • If data_format='channels_first': 3D tensor with shape (batch_size, features, 1)