tf.keras.layers.GaussianDropout

TensorFlow 1 version

View source on GitHub

Global average pooling operation for temporal data.

Inherits From: Layer, Module

View aliases

Main aliases

tf.keras.layers.GlobalAvgPool1D

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.GlobalAveragePooling1D, tf.compat.v1.keras.layers.GlobalAvgPool1D

tf.keras.layers.GlobalAveragePooling1D(
    data_format='channels_last', **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
The Functional API	Basic text classification Word embeddings Graph regularization for sentiment classification using synthesized graphs Taking advantage of context features Building deep retrieval models

Examples:

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)`.

Call arguments:

inputs: A 3D tensor.
mask: Binary tensor of shape (batch_size, steps) indicating whether a given step should be masked (excluded from the average).

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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