tf.keras.layers.GlobalAveragePooling2D

TensorFlow 1 version

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Global max pooling operation for 1D temporal data.

Inherits From: Layer, Module

View aliases

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
)

Used in the notebooks

Used in the guide	Used in the tutorials
Training and evaluation with the built-in methods	Load text

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).

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tf.keras / layers / layers.GlobalAveragePooling2D

View aliases

Used in the notebooks

For example:

Arguments

Input shape:

Output shape: