| View source on GitHub |
Global average pooling operation for temporal data.
tf.keras.layers.GlobalAveragePooling1D(
data_format='channels_last', **kwargs
)
input_shape = (2, 3, 4) x = tf.random.normal(input_shape) y = tf.keras.layers.GlobalAveragePooling1D()(x) print(y.shape) (2, 4)
| 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). |
inputs: A 3D tensor.mask: Binary tensor of shape (batch_size, steps) indicating whether a given step should be masked (excluded from the average).data_format='channels_last': 3D tensor with shape: (batch_size, steps, features)
data_format='channels_first': 3D tensor with shape: (batch_size, features, steps)
2D tensor with shape (batch_size, features).
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/GlobalAveragePooling1D