View source on GitHub |
Max pooling operation for 1D temporal data.
Inherits From: Layer
, Operation
tf.keras.layers.MaxPool1D(
pool_size=2,
strides=None,
padding='valid',
data_format=None,
name=None,
**kwargs
)
Used in the notebooks
Used in the guide | Used in the tutorials |
---|---|
Downsamples the input representation by taking the maximum value over a
spatial window of size pool_size
. The window is shifted by strides
.
The resulting output when using the "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])
max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
strides=1, padding="valid")
max_pool_1d(x)
strides=2
and padding="valid"
:
x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
strides=2, padding="valid")
max_pool_1d(x)
strides=1
and padding="same"
:
x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
strides=1, padding="same")
max_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
)