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Global max pooling operation for 1D temporal data.
tf.keras.layers.GlobalMaxPool1D( data_format='channels_last', keepdims=False, **kwargs )
Downsamples the input representation by taking the maximum value over the time dimension.
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)>
Args | |
---|---|
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 . |
data_format='channels_last'
: 3D tensor with shape: (batch_size, steps, features)
data_format='channels_first'
: 3D tensor with shape: (batch_size, features, steps)
keepdims
=False: 2D tensor with shape (batch_size, features)
.keepdims
=True: data_format='channels_last'
: 3D tensor with shape (batch_size, 1, features)
data_format='channels_first'
: 3D tensor with shape (batch_size, features, 1)
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Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/GlobalMaxPool1D