Global max pooling operation for temporal data.
Inherits From: Layer
, Operation
View aliases
Main aliases
tf.keras.layers.GlobalMaxPool1D(
data_format=None, keepdims=False, **kwargs
)
Used in the notebooks
Used in the tutorials |
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Args | |
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data_format
|
string, either "channels_last" 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) . It defaults to the image_data_format
value found in your Keras config file at ~/.keras/keras.json .
If you never set it, then it will be "channels_last" .
|
keepdims
|
A boolean, whether to keep the temporal dimension or not.
If keepdims is False (default), the rank of the tensor is
reduced for spatial dimensions. If keepdims is True , the
temporal dimension are retained with length 1.
The behavior is the same as for tf.reduce_mean or np.mean .
|
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
keepdims=False
: 2D tensor with shape(batch_size, features)
. - If
keepdims=True
:- If
data_format="channels_last"
: 3D tensor with shape(batch_size, 1, features)
- If
data_format="channels_first"
: 3D tensor with shape(batch_size, features, 1)
- If
Example:
x = np.random.rand(2, 3, 4)
y = keras.layers.GlobalMaxPooling1D()(x)
y.shape
(2, 4)
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 | |
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config
|
A Python dictionary, typically the output of get_config. |
Returns | |
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A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)