tf.keras.layers.AveragePooling1D
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Average pooling for temporal data.
tf.keras.layers.AveragePooling1D(
pool_size=2, strides=None, padding='valid', data_format='channels_last',
**kwargs
)
Arguments |
pool_size
|
Integer, size of the average pooling windows.
|
strides
|
Integer, or None. Factor by which to downscale.
E.g. 2 will halve the input.
If None, it will default to pool_size .
|
padding
|
One of "valid" or "same" (case-insensitive).
|
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) .
|
- 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
data_format='channels_last'
:
3D tensor with shape (batch_size, downsampled_steps, features)
.
- If
data_format='channels_first'
:
3D tensor with shape (batch_size, features, downsampled_steps)
.
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.AveragePooling1D\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/keras/layers/AveragePooling1D) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/pooling.py#L155-L193) |\n\nAverage pooling for temporal data.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.layers.AvgPool1D`](/api_docs/python/tf/keras/layers/AveragePooling1D)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.layers.AveragePooling1D`](/api_docs/python/tf/keras/layers/AveragePooling1D), [`tf.compat.v1.keras.layers.AvgPool1D`](/api_docs/python/tf/keras/layers/AveragePooling1D), \\`tf.compat.v2.keras.layers.AveragePooling1D\\`, \\`tf.compat.v2.keras.layers.AvgPool1D\\`\n\n\u003cbr /\u003e\n\n tf.keras.layers.AveragePooling1D(\n pool_size=2, strides=None, padding='valid', data_format='channels_last',\n **kwargs\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `pool_size` | Integer, size of the average pooling windows. |\n| `strides` | Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to `pool_size`. |\n| `padding` | One of `\"valid\"` or `\"same\"` (case-insensitive). |\n| `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)`. |\n\n\u003cbr /\u003e\n\n#### Input shape:\n\n- If `data_format='channels_last'`: 3D tensor with shape `(batch_size, steps, features)`.\n- If `data_format='channels_first'`: 3D tensor with shape `(batch_size, features, steps)`.\n\n#### Output shape:\n\n- If `data_format='channels_last'`: 3D tensor with shape `(batch_size, downsampled_steps, features)`.\n- If `data_format='channels_first'`: 3D tensor with shape `(batch_size, features, downsampled_steps)`."]]