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Average pooling for temporal data.
Inherits From: Layer
, Operation
tf.keras.layers.AveragePooling1D(
pool_size,
strides=None,
padding='valid',
data_format=None,
name=None,
**kwargs
)
Downsamples the input representation by taking the average value over the
window defined by pool_size
. The window is shifted by strides
. The
resulting output when using "valid" padding option has a shape of:
output_shape = (input_shape - pool_size + 1) / strides)
The resulting output shape when using the "same" padding option is:
output_shape = input_shape / strides
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:
- 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)
.
Examples:
strides=1
and padding="valid"
:
x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
avg_pool_1d = keras.layers.AveragePooling1D(pool_size=2,
strides=1, padding="valid")
avg_pool_1d(x)
strides=2
and padding="valid"
:
x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
avg_pool_1d = keras.layers.AveragePooling1D(pool_size=2,
strides=2, padding="valid")
avg_pool_1d(x)
strides=1
and padding="same"
:
x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
avg_pool_1d = keras.layers.AveragePooling1D(pool_size=2,
strides=1, padding="same")
avg_pool_1d(x)
Methods
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)